{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,10,26]],"date-time":"2026-10-26T18:38:31Z","timestamp":1793039911778,"version":"4.5.0"},"reference-count":233,"publisher":"American Association for the Advancement of Science (AAAS)","license":[{"start":{"date-parts":[[2022,1,1]],"date-time":"2022-01-01T00:00:00Z","timestamp":1640995200000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.elsevier.com\/tdm\/userlicense\/1.0\/"},{"start":{"date-parts":[[2022,1,1]],"date-time":"2022-01-01T00:00:00Z","timestamp":1640995200000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.elsevier.com\/legal\/tdmrep-license"},{"start":{"date-parts":[[2022,7,29]],"date-time":"2022-07-29T00:00:00Z","timestamp":1659052800000},"content-version":"vor","delay-in-days":209,"URL":"http:\/\/creativecommons.org\/licenses\/by-nc-nd\/4.0\/"}],"funder":[{"DOI":"10.13039\/501100002551","name":"Seoul National University","doi-asserted-by":"publisher","award":["DY0002259501"],"award-info":[{"award-number":["DY0002259501"]}],"id":[{"id":"10.13039\/501100002551","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100003569","name":"Ministry of Food and Drug Safety","doi-asserted-by":"publisher","id":[{"id":"10.13039\/501100003569","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100003621","name":"Ministry of Science, ICT and Future Planning","doi-asserted-by":"publisher","award":["2022M3E5F3085677"],"award-info":[{"award-number":["2022M3E5F3085677"]}],"id":[{"id":"10.13039\/501100003621","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100003621","name":"Ministry of Science, ICT and Future Planning","doi-asserted-by":"publisher","award":["NRF-2014M3C9A3063541"],"award-info":[{"award-number":["NRF-2014M3C9A3063541"]}],"id":[{"id":"10.13039\/501100003621","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100003725","name":"National Research Foundation of Korea","doi-asserted-by":"publisher","id":[{"id":"10.13039\/501100003725","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100010418","name":"Institute for Information and Communications Technology Promotion","doi-asserted-by":"publisher","award":["2021\u20130-01343"],"award-info":[{"award-number":["2021\u20130-01343"]}],"id":[{"id":"10.13039\/501100010418","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":["csbj.org","elsevier.com","sciencedirect.com"],"crossmark-restriction":true},"short-container-title":["Comput Struct Biotechnol J"],"published-print":{"date-parts":[[2022,1]]},"DOI":"10.1016\/j.csbj.2022.07.049","type":"journal-article","created":{"date-parts":[[2022,8,4]],"date-time":"2022-08-04T23:41:38Z","timestamp":1659656498000},"page":"4288-4304","update-policy":"https:\/\/doi.org\/10.1016\/elsevier_cm_policy","source":"Crossref","is-referenced-by-count":19,"title":["On modeling and utilizing chemical compound information with deep learning technologies: A task-oriented approach"],"prefix":"10.34133","volume":"20","author":[{"given":"Sangsoo","family":"Lim","sequence":"first","affiliation":[{"name":"Bioinformatics Institute, Seoul National University, Gwanak-ro 1, Gwanak-gu, Seoul 08826, South Korea"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Sangseon","family":"Lee","sequence":"additional","affiliation":[{"name":"Institute of Computer Technology, Seoul National University, Gwanak-ro 1, Gwanak-gu, Seoul 08826, South Korea"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Yinhua","family":"Piao","sequence":"additional","affiliation":[{"name":"Department of Computer Science and Engineering, Seoul National University, Gwanak-ro 1, Gwanak-gu, Seoul 08826, South Korea"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"MinGyu","family":"Choi","sequence":"additional","affiliation":[{"name":"Department of Chemistry, Seoul National University, Gwanak-ro 1, Gwanak-gu, Seoul 08826, South Korea"},{"name":"AIGENDRUG Co., Ltd., Gwanak-ro 1, Gwanak-gu, Seoul 08826, South Korea"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Dongmin","family":"Bang","sequence":"additional","affiliation":[{"name":"Interdisciplinary Program in Bioinformatics, Seoul National University, Gwanak-ro 1, Gwanak-gu, Seoul 08826, South Korea"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Jeonghyeon","family":"Gu","sequence":"additional","affiliation":[{"name":"Interdisciplinary Program in Artificial Intelligence, Seoul National University, Gwanak-ro 1, Gwanak-gu, Seoul 08826, South Korea"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Sun","family":"Kim","sequence":"additional","affiliation":[{"name":"Department of Computer Science and Engineering, Seoul National University, Gwanak-ro 1, Gwanak-gu, Seoul 08826, South Korea"},{"name":"Interdisciplinary Program in Artificial Intelligence, Seoul National University, Gwanak-ro 1, Gwanak-gu, Seoul 08826, South Korea"},{"name":"AIGENDRUG Co., Ltd., Gwanak-ro 1, Gwanak-gu, Seoul 08826, South Korea"},{"name":"MOGAM Institute for Biomedical Research, Yong-in 16924, South Korea"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"221","published-online":{"date-parts":[[2022,8,13]]},"reference":[{"key":"e_1_3_5_2_2","doi-asserted-by":"publisher","DOI":"10.1038\/nature03193"},{"key":"e_1_3_5_3_2","doi-asserted-by":"crossref","first-page":"322","DOI":"10.2174\/157340908786786010","article-title":"Visualization of the chemical space in drug discovery","volume":"4","author":"Medina-Franco J.L.","year":"2008","unstructured":"Medina-Franco J.L., Mart\u00ednez-Mayorga K., Giulianotti M.A., Houghten R.A. and Pinilla C.: Visualization of the chemical space in drug discovery. Curr Comput Aided Drug Des 2008; 4: 322.","journal-title":"Curr Comput Aided Drug Des"},{"key":"e_1_3_5_4_2","doi-asserted-by":"crossref","first-page":"718","DOI":"10.1016\/j.drudis.2012.04.001","article-title":"Expanding the medicinally relevant chemical space with compound libraries","volume":"17","author":"L\u00f3pez-Vallejo F.","year":"2012","unstructured":"L\u00f3pez-Vallejo F., Giulianotti M.A., Houghten R.A. and Medina-Franco J.L.: Expanding the medicinally relevant chemical space with compound libraries. Drug Discovery Today 2012; 17: 718.","journal-title":"Drug Discovery Today"},{"key":"e_1_3_5_5_2","doi-asserted-by":"publisher","DOI":"10.1039\/c0md00020e"},{"key":"e_1_3_5_6_2","doi-asserted-by":"crossref","first-page":"675","DOI":"10.1007\/s10822-013-9672-4","article-title":"Estimation of the size of drug-like chemical space based on gdb-17 data","volume":"27","author":"Polishchuk P.G.","year":"2013","unstructured":"Polishchuk P.G., Madzhidov T.I. and Varnek A.: Estimation of the size of drug-like chemical space based on gdb-17 data. J Computer-Aided Mol Des 2013; 27: 675.","journal-title":"J Computer-Aided Mol Des"},{"key":"e_1_3_5_7_2","doi-asserted-by":"publisher","DOI":"10.1021\/ar500432k"},{"key":"e_1_3_5_8_2","doi-asserted-by":"publisher","DOI":"10.1021\/cn3000422"},{"key":"e_1_3_5_9_2","doi-asserted-by":"crossref","first-page":"3","DOI":"10.1016\/S0169-409X(96)00423-1","article-title":"Experimental and computational approaches to estimate solubility and permeability in drug discovery and development settings","volume":"23","author":"Lipinski C.A.","year":"1997","unstructured":"Lipinski C.A., Lombardo F., Dominy B.W. and Feeney P.J.: Experimental and computational approaches to estimate solubility and permeability in drug discovery and development settings. Adv Drug Delivery Rev 1997; 23: 3.","journal-title":"Adv Drug Delivery Rev"},{"key":"e_1_3_5_10_2","doi-asserted-by":"publisher","DOI":"10.1016\/j.chembiol.2014.08.013"},{"key":"e_1_3_5_11_2","doi-asserted-by":"crossref","unstructured":"B.C. Doak J. Kihlberg Drug discovery beyond the rule of 5-opportunities and challenges 2017.","DOI":"10.1080\/17460441.2017.1264385"},{"key":"e_1_3_5_12_2","doi-asserted-by":"publisher","DOI":"10.1039\/C7SC02664A"},{"key":"e_1_3_5_13_2","unstructured":"K. Huang T. Fu W. Gao Y. Zhao Y. Roohani J. Leskovec C.W. Coley C. Xiao J. Sun M. Zitnik Therapeutics data commons: machine learning datasets and tasks for therapeutics arXiv e-prints (2021) arXiv\u20132102."},{"key":"e_1_3_5_14_2","doi-asserted-by":"publisher","DOI":"10.1016\/j.drudis.2019.03.015"},{"key":"e_1_3_5_15_2","doi-asserted-by":"publisher","DOI":"10.1371\/journal.pcbi.1003741"},{"key":"e_1_3_5_16_2","doi-asserted-by":"publisher","DOI":"10.1021\/jm100301x"},{"key":"e_1_3_5_17_2","doi-asserted-by":"crossref","first-page":"192","DOI":"10.1038\/nrd1032","article-title":"Admet in silico modelling: towards prediction paradise?","volume":"2","author":"Van De Waterbeemd H.","year":"2003","unstructured":"Van De Waterbeemd H. and Gifford E.: Admet in silico modelling: towards prediction paradise?. Nature Rev Drug Discovery 2003; 2: 192.","journal-title":"Nature Rev Drug Discovery"},{"key":"e_1_3_5_18_2","doi-asserted-by":"crossref","first-page":"763","DOI":"10.1021\/acs.jcim.5b00642","article-title":"Adme properties evaluation in drug discovery: prediction of caco-2 cell permeability using a combination of nsga-ii and boosting","volume":"56","author":"Wang N.-N.","year":"2016","unstructured":"Wang N.-N., Dong J., Deng Y.-H., Zhu M.-F., Wen M., Yao Z.-J., Lu A.-P., Wang J.-B. and Cao D.-S.: Adme properties evaluation in drug discovery: prediction of caco-2 cell permeability using a combination of nsga-ii and boosting. J Chem Inf Model 2016; 56: 763.","journal-title":"J Chem Inf Model"},{"key":"e_1_3_5_19_2","doi-asserted-by":"crossref","first-page":"208","DOI":"10.1021\/ci600343x","article-title":"Adme evaluation in drug discovery. 7. prediction of oral absorption by correlation and classification","volume":"47","author":"Hou T.","year":"2007","unstructured":"Hou T., Wang J., Zhang W. and Xu X.: Adme evaluation in drug discovery. 7. prediction of oral absorption by correlation and classification. J Chem Inform Modeling 2007; 47: 208.","journal-title":"J Chem Inform Modeling"},{"key":"e_1_3_5_20_2","doi-asserted-by":"crossref","first-page":"1740","DOI":"10.1021\/jm101421d","article-title":"A novel approach for predicting p-glycoprotein (abcb1) inhibition using molecular interaction fields","volume":"54","author":"Broccatelli F.","year":"2011","unstructured":"Broccatelli F., Carosati E., Neri A., Frosini M., Goracci L., Oprea T.I. and Cruciani G.: A novel approach for predicting p-glycoprotein (abcb1) inhibition using molecular interaction fields. J Med Chem 2011; 54: 1740.","journal-title":"J Med Chem"},{"key":"e_1_3_5_21_2","doi-asserted-by":"crossref","first-page":"677","DOI":"10.1016\/j.jpba.2008.03.023","article-title":"Prediction models of human plasma protein binding rate and oral bioavailability derived by using ga\u2013cg\u2013svm method","volume":"47","author":"Ma C.-Y.","year":"2008","unstructured":"Ma C.-Y., Yang S.-Y., Zhang H., Xiang M.-L., Huang Q. and Wei Y.-Q.: Prediction models of human plasma protein binding rate and oral bioavailability derived by using ga\u2013cg\u2013svm method. J Pharmaceutical Biomed Anal 2008; 47: 677.","journal-title":"J Pharmaceutical Biomed Anal"},{"key":"e_1_3_5_22_2","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1038\/s41597-019-0151-1","article-title":"Aqsoldb, a curated reference set of aqueous solubility and 2d descriptors for a diverse set of compounds","volume":"6","author":"Sorkun M.C.","year":"2019","unstructured":"Sorkun M.C., Khetan A. and Er S.: Aqsoldb, a curated reference set of aqueous solubility and 2d descriptors for a diverse set of compounds. Sci Data 2019; 6: 1.","journal-title":"Sci Data"},{"key":"e_1_3_5_23_2","doi-asserted-by":"crossref","first-page":"711","DOI":"10.1007\/s10822-014-9747-x","article-title":"Freesolv: a database of experimental and calculated hydration free energies, with input files","volume":"28","author":"Mobley D.L.","year":"2014","unstructured":"Mobley D.L. and Guthrie J.P.: Freesolv: a database of experimental and calculated hydration free energies, with input files. J Computer-Aided Mol Des 2014; 28: 711.","journal-title":"J Computer-Aided Mol Des"},{"key":"e_1_3_5_24_2","doi-asserted-by":"crossref","first-page":"1686","DOI":"10.1021\/ci300124c","article-title":"A bayesian approach to in silico blood-brain barrier penetration modeling","volume":"52","author":"Martins I.F.","year":"2012","unstructured":"Martins I.F., Teixeira A.L., Pinheiro L. and Falcao A.O.: A bayesian approach to in silico blood-brain barrier penetration modeling. J Chem Inform Modeling 2012; 52: 1686.","journal-title":"J Chem Inform Modeling"},{"key":"e_1_3_5_25_2","doi-asserted-by":"crossref","unstructured":"A. Hersey ChEMBL Deposited Data Set-AZ_dataset Technical Report Technical report EMBL-EBI 2015. https:\/\/www. ebi. ac. uk\/chembl\/doc... 2015.","DOI":"10.6019\/CHEMBL3301361"},{"key":"e_1_3_5_26_2","doi-asserted-by":"crossref","first-page":"2042","DOI":"10.1021\/acs.jcim.6b00044","article-title":"In silico prediction of volume of distribution in humans. extensive data set and the exploration of linear and nonlinear methods coupled with molecular interaction fields descriptors","volume":"56","author":"Lombardo F.","year":"2016","unstructured":"Lombardo F. and Jing Y.: In silico prediction of volume of distribution in humans. extensive data set and the exploration of linear and nonlinear methods coupled with molecular interaction fields descriptors. J Chem Inf Model 2016; 56: 2042.","journal-title":"J Chem Inf Model"},{"key":"e_1_3_5_27_2","doi-asserted-by":"publisher","DOI":"10.1038\/nbt.1581"},{"key":"e_1_3_5_28_2","doi-asserted-by":"crossref","first-page":"885","DOI":"10.1002\/minf.201100069","article-title":"Selecting relevant descriptors for classification by bayesian estimates: a comparison with decision trees and support vector machines approaches for disparate data sets","volume":"30","author":"Carbon-Mangels M.","year":"2011","unstructured":"Carbon-Mangels M. and Hutter M.C.: Selecting relevant descriptors for classification by bayesian estimates: a comparison with decision trees and support vector machines approaches for disparate data sets. Mol Informatics 2011; 30: 885.","journal-title":"Mol Informatics"},{"key":"e_1_3_5_29_2","doi-asserted-by":"crossref","unstructured":"F. Cheng W. Li Y. Zhou J. Shen Z. Wu G. Liu P.W. Lee Y. Tang admetsar: a comprehensive source and free tool for assessment of chemical admet properties 2012.","DOI":"10.1021\/ci300367a"},{"key":"e_1_3_5_30_2","doi-asserted-by":"crossref","first-page":"1385","DOI":"10.1124\/dmd.108.020479","article-title":"Trend analysis of a database of intravenous pharmacokinetic parameters in humans for 670 drug compounds","volume":"36","author":"Obach R.S.","year":"2008","unstructured":"Obach R.S., Lombardo F. and Waters N.J.: Trend analysis of a database of intravenous pharmacokinetic parameters in humans for 670 drug compounds. Drug Metab Dispos 2008; 36: 1385.","journal-title":"Drug Metab Dispos"},{"key":"e_1_3_5_31_2","doi-asserted-by":"crossref","first-page":"441","DOI":"10.1016\/j.ejmech.2012.06.043","article-title":"Mechanistic insights from comparing intrinsic clearance values between human liver microsomes and hepatocytes to guide drug design","volume":"57","author":"Di L.","year":"2012","unstructured":"Di L., Keefer C., Scott D.O., Strelevitz T.J., Chang G., Bi Y.-A., Lai Y., Duckworth J., Fenner K., Troutman M.D. et al: Mechanistic insights from comparing intrinsic clearance values between human liver microsomes and hepatocytes to guide drug design. Eur J Med Chem 2012; 57: 441.","journal-title":"Eur J Med Chem"},{"key":"e_1_3_5_32_2","doi-asserted-by":"crossref","first-page":"1913","DOI":"10.1021\/tx900189p","article-title":"Quantitative structure- activity relationship modeling of rat acute toxicity by oral exposure","volume":"22","author":"Zhu H.","year":"2009","unstructured":"Zhu H., Martin T.M., Ye L., Sedykh A., Young D.M. and Tropsha A.: Quantitative structure- activity relationship modeling of rat acute toxicity by oral exposure. Chem Res Toxicol 2009; 22: 1913.","journal-title":"Chem Res Toxicol"},{"key":"e_1_3_5_33_2","doi-asserted-by":"crossref","first-page":"2855","DOI":"10.1021\/acs.molpharmaceut.6b00471","article-title":"Admet evaluation in drug discovery. 16. predicting herg blockers by combining multiple pharmacophores and machine learning approaches","volume":"13","author":"Wang S.","year":"2016","unstructured":"Wang S., Sun H., Liu H., Li D., Li Y. and Hou T.: Admet evaluation in drug discovery. 16. predicting herg blockers by combining multiple pharmacophores and machine learning approaches. Mol Pharmaceutics 2016; 13: 2855.","journal-title":"Mol Pharmaceutics"},{"key":"e_1_3_5_34_2","doi-asserted-by":"crossref","first-page":"580","DOI":"10.1089\/adt.2011.0425","article-title":"hergcentral: a large database to store, retrieve, and analyze compound-human ether-a-go-go related gene channel interactions to facilitate cardiotoxicity assessment in drug development","volume":"9","author":"Du F.","year":"2011","unstructured":"Du F., Yu H., Zou B., Babcock J., Long S. and Li M.: hergcentral: a large database to store, retrieve, and analyze compound-human ether-a-go-go related gene channel interactions to facilitate cardiotoxicity assessment in drug development. Assay Drug Dev Technol 2011; 9: 580.","journal-title":"Assay Drug Dev Technol"},{"key":"e_1_3_5_35_2","doi-asserted-by":"crossref","first-page":"2840","DOI":"10.1021\/ci300400a","article-title":"In silico prediction of chemical ames mutagenicity","volume":"52","author":"Xu C.","year":"2012","unstructured":"Xu C., Cheng F., Chen L., Du Z., Li W., Liu G., Lee P.W. and Tang Y.: In silico prediction of chemical ames mutagenicity. J Chem Inform Modeling 2012; 52: 2840.","journal-title":"J Chem Inform Modeling"},{"key":"e_1_3_5_36_2","doi-asserted-by":"crossref","first-page":"2085","DOI":"10.1021\/acs.jcim.5b00238","article-title":"Deep learning for drug-induced liver injury","volume":"55","author":"Xu Y.","year":"2015","unstructured":"Xu Y., Dai Z., Chen F., Gao S., Pei J. and Lai L.: Deep learning for drug-induced liver injury. J Chem Inform Modeling 2015; 55: 2085.","journal-title":"J Chem Inform Modeling"},{"key":"e_1_3_5_37_2","doi-asserted-by":"crossref","first-page":"262","DOI":"10.1016\/j.taap.2014.12.014","article-title":"Predicting chemically-induced skin reactions. part i: Qsar models of skin sensitization and their application to identify potentially hazardous compounds","volume":"284","author":"Alves V.M.","year":"2015","unstructured":"Alves V.M., Muratov E., Fourches D., Strickland J., Kleinstreuer N., Andrade C.H. and Tropsha A.: Predicting chemically-induced skin reactions. part i: Qsar models of skin sensitization and their application to identify potentially hazardous compounds. Toxicol Appl Pharmacology 2015; 284: 262.","journal-title":"Toxicol Appl Pharmacology"},{"key":"e_1_3_5_38_2","doi-asserted-by":"crossref","first-page":"806","DOI":"10.1002\/qsar.200860192","article-title":"Computer-aided prediction of rodent carcinogenicity by pass and cisoc-psct","volume":"28","author":"Lagunin A.","year":"2009","unstructured":"Lagunin A., Filimonov D., Zakharov A., Xie W., Huang Y., Zhu F., Shen T., Yao J. and Poroikov V.: Computer-aided prediction of rodent carcinogenicity by pass and cisoc-psct. QSAR Combinatorial Sci 2009; 28: 806.","journal-title":"QSAR Combinatorial Sci"},{"key":"e_1_3_5_39_2","doi-asserted-by":"crossref","first-page":"85","DOI":"10.3389\/fenvs.2015.00085","article-title":"Tox21challenge to build predictive models of nuclear receptor and stress response pathways as mediated by exposure to environmental chemicals and drugs","volume":"3","author":"Huang R.","year":"2016","unstructured":"Huang R., Xia M., Nguyen D.-T., Zhao T., Sakamuru S., Zhao J., Shahane S.A., Rossoshek A. and Simeonov A.: Tox21challenge to build predictive models of nuclear receptor and stress response pathways as mediated by exposure to environmental chemicals and drugs. Front Environ Sci 2016; 3: 85.","journal-title":"Front Environ Sci"},{"key":"e_1_3_5_40_2","doi-asserted-by":"publisher","DOI":"10.1021\/acs.chemrestox.6b00135"},{"key":"e_1_3_5_41_2","doi-asserted-by":"publisher","DOI":"10.1016\/j.chembiol.2016.07.023"},{"key":"e_1_3_5_42_2","doi-asserted-by":"crossref","first-page":"235","DOI":"10.1517\/17460441003605098","article-title":"Lipophilicity in drug discovery","volume":"5","author":"Waring M.J.","year":"2010","unstructured":"Waring M.J.: Lipophilicity in drug discovery. Expert Opin Drug Discov 2010; 5: 235.","journal-title":"Expert Opin Drug Discov"},{"key":"e_1_3_5_43_2","article-title":"Drug solubility: importance and enhancement techniques","volume":"2012","author":"Savjani K.T.","year":"2012","unstructured":"Savjani K.T., Gajjar A.K. and Savjani J.K.: Drug solubility: importance and enhancement techniques. Int Scholarly Res Notices 2012; 2012.","journal-title":"Int Scholarly Res Notices"},{"key":"e_1_3_5_44_2","doi-asserted-by":"crossref","first-page":"1162","DOI":"10.1021\/mp3005319","article-title":"Evolution of adme science: where else can modeling and simulation contribute?","volume":"10","author":"Smith D.A.","year":"2013","unstructured":"Smith D.A.: Evolution of adme science: where else can modeling and simulation contribute?. Mol Pharmaceutics 2013; 10: 1162.","journal-title":"Mol Pharmaceutics"},{"key":"e_1_3_5_45_2","doi-asserted-by":"crossref","unstructured":"M.L. Amin P-glycoprotein inhibition for optimal drug delivery Drug target insights 7 (2013) DTI\u2013S12519.","DOI":"10.4137\/DTI.S12519"},{"key":"e_1_3_5_46_2","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1007\/s10565-005-0085-6","article-title":"The caco-2 cell line as a model of the intestinal barrier: influence of cell and culture-related factors on caco-2 cell functional characteristics","volume":"21","author":"Sambuy Y.","year":"2005","unstructured":"Sambuy Y., De Angelis I., Ranaldi G., Scarino M., Stammati A. and Zucco F.: The caco-2 cell line as a model of the intestinal barrier: influence of cell and culture-related factors on caco-2 cell functional characteristics. Cell Biol Toxicol 2005; 21: 1.","journal-title":"Cell Biol Toxicol"},{"key":"e_1_3_5_47_2","doi-asserted-by":"publisher","DOI":"10.1016\/j.nbd.2009.07.030"},{"key":"e_1_3_5_48_2","unstructured":"J. Aslam Utilization of big data analysis in biomedicinal chemistry chemistry 4 (2019)."},{"key":"e_1_3_5_49_2","doi-asserted-by":"crossref","unstructured":"W. Lindup M. Orme Clinical pharmacology: plasma protein binding of drugs. British medical journal (Clinical research ed.) 282 (1981) 212.","DOI":"10.1136\/bmj.282.6259.212"},{"key":"e_1_3_5_50_2","first-page":"263","article-title":"Basic review of the cytochrome p450 system","volume":"4","author":"McDonnell A.M.","year":"2013","unstructured":"McDonnell A.M. and Dang C.H.: Basic review of the cytochrome p450 system. J Adv Practitioner Oncol 2013; 4: 263.","journal-title":"J Adv Practitioner Oncol"},{"key":"e_1_3_5_51_2","article-title":"Pharmacogenomics of cyp2d6: molecular genetics, interethnic differences and clinical importance","author":"Teh L.K.","year":"2011","unstructured":"Teh L.K. and Bertilsson L.: Pharmacogenomics of cyp2d6: molecular genetics, interethnic differences and clinical importance. Drug metabolism and pharmacokinetics 2011; . 1112190300\u20131112190300.","journal-title":"Drug metabolism and pharmacokinetics"},{"key":"e_1_3_5_52_2","doi-asserted-by":"publisher","DOI":"10.1016\/j.pharmthera.2012.12.007"},{"key":"e_1_3_5_53_2","doi-asserted-by":"publisher","DOI":"10.1038\/nrd4581"},{"key":"e_1_3_5_54_2","doi-asserted-by":"crossref","first-page":"263","DOI":"10.1021\/ci500747n","article-title":"Deep neural nets as a method for quantitative structure\u2013activity relationships","volume":"55","author":"Ma J.","year":"2015","unstructured":"Ma J., Sheridan R.P., Liaw A., Dahl G.E. and Svetnik V.: Deep neural nets as a method for quantitative structure\u2013activity relationships. J Chem Inform Modeling 2015; 55: 263.","journal-title":"J Chem Inform Modeling"},{"key":"e_1_3_5_55_2","doi-asserted-by":"crossref","first-page":"8835","DOI":"10.1021\/acs.jmedchem.9b02187","article-title":"Improvement in admet prediction with multitask deep featurization","volume":"63","author":"Feinberg E.N.","year":"2020","unstructured":"Feinberg E.N., Joshi E., Pande V.S. and Cheng A.C.: Improvement in admet prediction with multitask deep featurization. J Med Chem 2020; 63: 8835.","journal-title":"J Med Chem"},{"key":"e_1_3_5_56_2","doi-asserted-by":"crossref","first-page":"115","DOI":"10.1177\/019262339502300203","article-title":"Basic principles of pharmacokinetics","volume":"23","author":"Benet L.Z.","year":"1995","unstructured":"Benet L.Z. and Zia-Amirhosseini P.: Basic principles of pharmacokinetics. Toxicologic Pathol 1995; 23: 115.","journal-title":"Toxicologic Pathol"},{"key":"e_1_3_5_57_2","doi-asserted-by":"crossref","first-page":"415","DOI":"10.1111\/j.1365-2885.2004.00605.x","article-title":"Plasma clearance","volume":"27","author":"Toutain P.-L.","year":"2004","unstructured":"Toutain P.-L. and Bousquet-m\u00e9lou A.: Plasma clearance. J Veterinary Pharmacology Therapeutics 2004; 27: 415.","journal-title":"J Veterinary Pharmacology Therapeutics"},{"key":"e_1_3_5_58_2","doi-asserted-by":"crossref","first-page":"1236","DOI":"10.1007\/s11095-008-9728-7","article-title":"Prediction of vitreal half-life based on drug physicochemical properties: quantitative structure\u2013pharmacokinetic relationships (qspkr)","volume":"26","author":"Durairaj C.","year":"2009","unstructured":"Durairaj C., Shah J.C., Senapati S. and Kompella U.B.: Prediction of vitreal half-life based on drug physicochemical properties: quantitative structure\u2013pharmacokinetic relationships (qspkr). Pharmaceutical Res 2009; 26: 1236.","journal-title":"Pharmaceutical Res"},{"key":"e_1_3_5_59_2","doi-asserted-by":"publisher","DOI":"10.1038\/nrd2378"},{"key":"e_1_3_5_60_2","doi-asserted-by":"crossref","first-page":"143","DOI":"10.1038\/s41587-020-0418-2","article-title":"Assessing the impact of generative ai on medicinal chemistry","volume":"38","author":"Walters W.P.","year":"2020","unstructured":"Walters W.P. and Murcko M.: Assessing the impact of generative ai on medicinal chemistry. Nature Biotechnol 2020; 38: 143.","journal-title":"Nature Biotechnol"},{"key":"e_1_3_5_61_2","doi-asserted-by":"crossref","DOI":"10.3389\/fphar.2020.565644","article-title":"Molecular sets (moses): a benchmarking platform for molecular generation models","volume":"11","author":"Polykovskiy D.","year":"2020","unstructured":"Polykovskiy D., Zhebrak A., Sanchez-Lengeling B., Golovanov S., Tatanov O., Belyaev S., Kurbanov R., Artamonov A., Aladinskiy V., Veselov M. et al: Molecular sets (moses): a benchmarking platform for molecular generation models. Front Pharmacol 2020; 11: 565644.","journal-title":"Front Pharmacol"},{"key":"e_1_3_5_62_2","doi-asserted-by":"publisher","DOI":"10.1021\/acs.jcim.8b00839"},{"key":"e_1_3_5_63_2","doi-asserted-by":"crossref","first-page":"267","DOI":"10.1016\/S1359-6446(05)03730-X","article-title":"Defining and maintaining a high quality screening collection: the gsk experience","volume":"11","author":"Lane S.J.","year":"2006","unstructured":"Lane S.J., Eggleston D.S., Brinded K.A., Hollerton J.C., Taylor N.L. and Readshaw S.A.: Defining and maintaining a high quality screening collection: the gsk experience. Drug Discovery Today 2006; 11: 267.","journal-title":"Drug Discovery Today"},{"key":"e_1_3_5_64_2","doi-asserted-by":"crossref","first-page":"D1220","DOI":"10.1093\/nar\/gkv1253","article-title":"Surechembl: a large-scale, chemically annotated patent document database","volume":"44","author":"Papadatos G.","year":"2016","unstructured":"Papadatos G., Davies M., Dedman N., Chambers J., Gaulton A., Siddle J., Koks R., Irvine S.A., Pettersson J., Goncharoff N. et al: Surechembl: a large-scale, chemically annotated patent document database. Nucl Acids Res 2016; 44: D1220.","journal-title":"Nucl Acids Res"},{"key":"e_1_3_5_65_2","doi-asserted-by":"publisher","DOI":"10.1021\/jm901137j"},{"key":"e_1_3_5_66_2","doi-asserted-by":"crossref","first-page":"2864","DOI":"10.1021\/ci300415d","article-title":"Enumeration of 166 billion organic small molecules in the chemical universe database gdb-17","volume":"52","author":"Ruddigkeit L.","year":"2012","unstructured":"Ruddigkeit L., Van Deursen R., Blum L.C. and Reymond J.-L.: Enumeration of 166 billion organic small molecules in the chemical universe database gdb-17. J Chem Inform Modeling 2012; 52: 2864.","journal-title":"J Chem Inform Modeling"},{"key":"e_1_3_5_67_2","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1038\/sdata.2014.22","article-title":"Quantum chemistry structures and properties of 134 kilo molecules","volume":"1","author":"Ramakrishnan R.","year":"2014","unstructured":"Ramakrishnan R., Dral P.O., Rupp M. and Von Lilienfeld O.A.: Quantum chemistry structures and properties of 134 kilo molecules. Sci Data 2014; 1: 1.","journal-title":"Sci Data"},{"key":"e_1_3_5_68_2","doi-asserted-by":"publisher","DOI":"10.1038\/s41587-019-0224-x"},{"key":"e_1_3_5_69_2","doi-asserted-by":"publisher","DOI":"10.1021\/ci3001277"},{"key":"e_1_3_5_70_2","doi-asserted-by":"publisher","DOI":"10.1002\/minf.201700153"},{"key":"e_1_3_5_71_2","doi-asserted-by":"crossref","first-page":"D945","DOI":"10.1093\/nar\/gkw1074","article-title":"The chembl database in 2017","volume":"45","author":"Gaulton A.","year":"2017","unstructured":"Gaulton A., Hersey A., Nowotka M., Bento A.P., Chambers J., Mendez D., Mutowo P., Atkinson F., Bellis L.J., Cibri\u00e1n-Uhalte E. et al: The chembl database in 2017. Nucl Acids Res 2017; 45: D945.","journal-title":"Nucl Acids Res"},{"key":"e_1_3_5_72_2","doi-asserted-by":"publisher","DOI":"10.1124\/pr.58.4.7"},{"key":"e_1_3_5_73_2","doi-asserted-by":"crossref","first-page":"726","DOI":"10.1124\/pr.58.4.5","article-title":"International union of pharmacology. lxi. peroxisome proliferator-activated receptors","volume":"58","author":"Michalik L.","year":"2006","unstructured":"Michalik L., Auwerx J., Berger J.P., Chatterjee V.K., Glass C.K., Gonzalez F.J., Grimaldi P.A., Kadowaki T., Lazar M.A., O\u2019Rahilly S. et al: International union of pharmacology. lxi. peroxisome proliferator-activated receptors. Pharmacol Rev 2006; 58: 726.","journal-title":"Pharmacol Rev"},{"key":"e_1_3_5_74_2","doi-asserted-by":"publisher","DOI":"10.1021\/ci8002649"},{"key":"e_1_3_5_75_2","doi-asserted-by":"publisher","DOI":"10.1093\/nar\/gkw1118"},{"key":"e_1_3_5_76_2","doi-asserted-by":"crossref","first-page":"785","DOI":"10.1016\/j.ejca.2003.11.022","article-title":"Update on nci in vitro drug screen utilities","volume":"40","author":"Holbeck S.","year":"2004","unstructured":"Holbeck S.: Update on nci in vitro drug screen utilities. Eur J Cancer 2004; 40: 785.","journal-title":"Eur J Cancer"},{"key":"e_1_3_5_77_2","doi-asserted-by":"publisher","DOI":"10.1002\/jcc.20290"},{"key":"e_1_3_5_78_2","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1186\/1758-2946-4-17","article-title":"Avogadro: an advanced semantic chemical editor, visualization, and analysis platform","volume":"4","author":"Hanwell M.D.","year":"2012","unstructured":"Hanwell M.D., Curtis D.E., Lonie D.C., Vandermeersch T., Zurek E. and Hutchison G.R.: Avogadro: an advanced semantic chemical editor, visualization, and analysis platform. J Cheminformatics 2012; 4: 1.","journal-title":"J Cheminformatics"},{"key":"e_1_3_5_79_2","doi-asserted-by":"publisher","DOI":"10.1002\/jcc.21287"},{"key":"e_1_3_5_80_2","doi-asserted-by":"crossref","unstructured":"E.E. Bolton Y. Wang P.A. Thiessen S.H. Bryant Pubchem: integrated platform of small molecules and biological activities in: Annual reports in computational chemistry volume 4 Elsevier 2008 pp. 217\u2013241.","DOI":"10.1016\/S1574-1400(08)00012-1"},{"key":"e_1_3_5_81_2","doi-asserted-by":"publisher","DOI":"10.1039\/C6SC05720A"},{"key":"e_1_3_5_82_2","doi-asserted-by":"publisher","DOI":"10.1126\/sciadv.1603015"},{"key":"e_1_3_5_83_2","doi-asserted-by":"publisher","DOI":"10.1016\/j.cpc.2019.02.007"},{"key":"e_1_3_5_84_2","doi-asserted-by":"crossref","DOI":"10.1007\/978-1-0716-0282-9","volume-title":"Quantum mechanics in drug discovery","author":"Heifetz A.","year":"2020","unstructured":"Heifetz A.: Quantum mechanics in drug discovery. : Springer 2020."},{"key":"e_1_3_5_85_2","article-title":"Machine-learning scoring functions for structure-based drug lead optimization","volume":"10","author":"Li H.","year":"2020","unstructured":"Li H., Sze K.-H., Lu G. and Ballester P.J.: Machine-learning scoring functions for structure-based drug lead optimization. Wiley Interdisciplinary Rev: Comput Mol Sci 2020; 10: e1465.","journal-title":"Wiley Interdisciplinary Rev: Comput Mol Sci"},{"key":"e_1_3_5_86_2","first-page":"1431","article-title":"Structure-based molecular modeling in sar analysis and lead optimization, Computational and Structural","volume":"19","author":"Temml V.","year":"2021","unstructured":"Temml V. and Kutil Z.: Structure-based molecular modeling in sar analysis and lead optimization, Computational and Structural. Biotechnol J 2021; 19: 1431.","journal-title":"Biotechnol J"},{"key":"e_1_3_5_87_2","doi-asserted-by":"crossref","first-page":"93","DOI":"10.3389\/fchem.2020.00093","article-title":"In silico strategies to support fragment-to-lead optimization in drug discovery","volume":"8","author":"de Souza Neto L.R.","year":"2020","unstructured":"de Souza Neto L.R., Moreira-Filho J.T., Neves B.J., Maidana R.L.B.R., Guimar\u00e3es A.C.R., Furnham N., Andrade C.H. and Silva F.P.: In silico strategies to support fragment-to-lead optimization in drug discovery. Front Chem 2020; 8: 93.","journal-title":"Front Chem"},{"key":"e_1_3_5_88_2","doi-asserted-by":"crossref","first-page":"31","DOI":"10.1021\/ci00057a005","article-title":"Smiles, a chemical language and information system. 1. introduction to methodology and encoding rules","volume":"28","author":"Weininger D.","year":"1988","unstructured":"Weininger D.: Smiles, a chemical language and information system. 1. introduction to methodology and encoding rules. J Chem Inform Computer Sci 1988; 28: 31.","journal-title":"J Chem Inform Computer Sci"},{"key":"e_1_3_5_89_2","doi-asserted-by":"crossref","first-page":"1030","DOI":"10.1021\/acs.jcim.8b00677","article-title":"Hit dexter 2.0: machine-learning models for the prediction of frequent hitters","volume":"59","author":"Stork C.","year":"2019","unstructured":"Stork C., Chen Y., Sicho M. and Kirchmair J.: Hit dexter 2.0: machine-learning models for the prediction of frequent hitters. J Chem Inform Modeling 2019; 59: 1030.","journal-title":"J Chem Inform Modeling"},{"key":"e_1_3_5_90_2","doi-asserted-by":"publisher","DOI":"10.1088\/2632-2153\/aba947"},{"key":"e_1_3_5_91_2","doi-asserted-by":"publisher","DOI":"10.1016\/j.ymeth.2014.08.005"},{"key":"e_1_3_5_92_2","doi-asserted-by":"publisher","DOI":"10.1021\/c160017a018"},{"key":"e_1_3_5_93_2","doi-asserted-by":"publisher","DOI":"10.1021\/ci010132r"},{"key":"e_1_3_5_94_2","doi-asserted-by":"crossref","first-page":"966","DOI":"10.1016\/j.jmgm.2007.08.002","article-title":"Exploring qstr and toxicophore of herg k+ channel blockers using gfa and hypogen techniques","volume":"26","author":"Garg D.","year":"2008","unstructured":"Garg D., Gandhi T. and Mohan C.G.: Exploring qstr and toxicophore of herg k+ channel blockers using gfa and hypogen techniques. J Mol Graph Model 2008; 26: 966.","journal-title":"J Mol Graph Model"},{"key":"e_1_3_5_95_2","doi-asserted-by":"crossref","first-page":"1785","DOI":"10.1007\/s00204-015-1587-5","article-title":"Toxicophore exploration as a screening technology for drug design and discovery: techniques, scope and limitations","volume":"90","author":"Singh P.K.","year":"2016","unstructured":"Singh P.K., Negi A., Gupta P.K., Chauhan M. and Kumar R.: Toxicophore exploration as a screening technology for drug design and discovery: techniques, scope and limitations. Arch Toxicol 2016; 90: 1785.","journal-title":"Arch Toxicol"},{"key":"e_1_3_5_96_2","unstructured":"S. Chithrananda G. Grand B. Ramsundar Chemberta: Large-scale self-supervised pretraining for molecular property prediction arXiv preprint arXiv:2010.09885 (2020)."},{"key":"e_1_3_5_97_2","unstructured":"S. Zhang Z. Hu A. Subramonian Y. Sun Motif-driven contrastive learning of graph representations arXiv preprint arXiv:2012.12533 (2020)."},{"key":"e_1_3_5_98_2","unstructured":"M. Sun J. Xing H. Wang B. Chen J. Zhou Mocl: Contrastive learning on molecular graphs with multi-level domain knowledge arXiv preprint arXiv:2106.04509 (2021)."},{"key":"e_1_3_5_99_2","doi-asserted-by":"crossref","unstructured":"Y. Wang J. Wang Z. Cao A.B. Farimani Molclr: molecular contrastive learning of representations via graph neural networks arXiv preprint arXiv:2102.10056 (2021).","DOI":"10.1038\/s42256-022-00447-x"},{"key":"e_1_3_5_100_2","unstructured":"G.B. Goh N.O. Hodas C. Siegel A. Vishnu Smiles2vec: An interpretable general-purpose deep neural network for predicting chemical properties arXiv preprint arXiv:1712.02034 (2017)."},{"key":"e_1_3_5_101_2","doi-asserted-by":"crossref","unstructured":"Y. Fang Q. Zhang H. Yang X. Zhuang S. Deng W. Zhang M. Qin Z. Chen X. Fan H. Chen Molecular contrastive learning with chemical element knowledge graph arXiv preprint arXiv:2112.00544 (2021).","DOI":"10.1609\/aaai.v36i4.20313"},{"key":"e_1_3_5_102_2","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1186\/s13321-020-00423-w","article-title":"Transformer-cnn: Swiss knife for qsar modeling and interpretation","volume":"12","author":"Karpov P.","year":"2020","unstructured":"Karpov P., Godin G. and Tetko I.V.: Transformer-cnn: Swiss knife for qsar modeling and interpretation. J Cheminformatics 2020; 12: 1.","journal-title":"J Cheminformatics"},{"key":"e_1_3_5_103_2","doi-asserted-by":"crossref","first-page":"3370","DOI":"10.1021\/acs.jcim.9b00237","article-title":"Analyzing learned molecular representations for property prediction","volume":"59","author":"Yang K.","year":"2019","unstructured":"Yang K., Swanson K., Jin W., Coley C., Eiden P., Gao H., Guzman-Perez A., Hopper T., Kelley B., Mathea M. et al: Analyzing learned molecular representations for property prediction. J Chem Inform Modeling 2019; 59: 3370.","journal-title":"J Chem Inform Modeling"},{"key":"e_1_3_5_104_2","doi-asserted-by":"crossref","first-page":"1520","DOI":"10.1021\/acscentsci.8b00507","article-title":"Potentialnet for molecular property prediction","volume":"4","author":"Feinberg E.N.","year":"2018","unstructured":"Feinberg E.N., Sur D., Wu Z., Husic B.E., Mai H., Li Y., Sun S., Yang J., Ramsundar B. and Pande V.S.: Potentialnet for molecular property prediction. ACS Central Sci 2018; 4: 1520.","journal-title":"ACS Central Sci"},{"key":"e_1_3_5_105_2","doi-asserted-by":"crossref","first-page":"2981","DOI":"10.1093\/bioinformatics\/btab195","article-title":"Fragat: a fragment-oriented multi-scale graph attention model for molecular property prediction","volume":"37","author":"Zhang Z.","year":"2021","unstructured":"Zhang Z., Guan J. and Zhou S.: Fragat: a fragment-oriented multi-scale graph attention model for molecular property prediction. Bioinformatics 2021; 37: 2981.","journal-title":"Bioinformatics"},{"key":"e_1_3_5_106_2","doi-asserted-by":"crossref","first-page":"bbaa266","DOI":"10.1093\/bib\/bbaa266","article-title":"Trimnet: learning molecular representation from triplet messages for biomedicine","volume":"22","author":"Li P.","year":"2021","unstructured":"Li P., Li Y., Hsieh C.-Y., Zhang S., Liu X., Liu H., Song S. and Yao X.: Trimnet: learning molecular representation from triplet messages for biomedicine. Briefings Bioinform 2021; 22: bbaa266.","journal-title":"Briefings Bioinform"},{"key":"e_1_3_5_107_2","doi-asserted-by":"crossref","first-page":"bbab317","DOI":"10.1093\/bib\/bbab317","article-title":"Mol2context-vec: learning molecular representation from context awareness for drug discovery","volume":"22","author":"Lv Q.","year":"2021","unstructured":"Lv Q., Chen G., Zhao L., Zhong W. and Yu-Chian Chen C.: Mol2context-vec: learning molecular representation from context awareness for drug discovery. Briefings Bioinform 2021; 22: bbab317.","journal-title":"Briefings Bioinform"},{"key":"e_1_3_5_108_2","doi-asserted-by":"crossref","unstructured":"Y. Song S. Zheng Z. Niu Z.-H. Fu Y. Lu Y. Yang Communicative representation learning on attributed molecular graphs. in: IJCAI volume 2020 2020 pp. 2831\u20132838.","DOI":"10.24963\/ijcai.2020\/392"},{"key":"e_1_3_5_109_2","doi-asserted-by":"crossref","first-page":"65","DOI":"10.1016\/j.ymeth.2020.05.009","article-title":"The message passing neural networks for chemical property prediction on smiles","volume":"179","author":"Jo J.","year":"2020","unstructured":"Jo J., Kwak B., Choi H.-S. and Yoon S.: The message passing neural networks for chemical property prediction on smiles. Methods 2020; 179: 65.","journal-title":"Methods"},{"key":"e_1_3_5_110_2","doi-asserted-by":"crossref","first-page":"10764","DOI":"10.1109\/CVPR.2019.01103","volume-title":"2019 IEEE\/CVF Conference on Computer Vision and Pattern Recognition (CVPR)","author":"Pope P.E.","year":"2019","unstructured":"Pope P.E., Kolouri S., Rostami M., Martin C.E. and Hoffmann H.: Explainability methods for graph convolutional neural networks. In: 2019 IEEE\/CVF Conference on Computer Vision and Pattern Recognition (CVPR). : IEEE Computer Society 2019: 10764."},{"key":"e_1_3_5_111_2","doi-asserted-by":"publisher","DOI":"10.1093\/bioinformatics\/btz307"},{"key":"e_1_3_5_112_2","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1007\/BF02289565","article-title":"Multidimensional scaling by optimizing goodness of fit to a nonmetric hypothesis","volume":"29","author":"Kruskal J.B.","year":"1964","unstructured":"Kruskal J.B.: Multidimensional scaling by optimizing goodness of fit to a nonmetric hypothesis. Psychometrika 1964; 29: 1.","journal-title":"Psychometrika"},{"key":"e_1_3_5_113_2","doi-asserted-by":"crossref","first-page":"55","DOI":"10.1016\/j.ymeth.2020.05.013","article-title":"Top: a deep mixture representation learning method for boosting molecular toxicity prediction","volume":"179","author":"Peng Y.","year":"2020","unstructured":"Peng Y., Zhang Z., Jiang Q., Guan J. and Zhou S.: Top: a deep mixture representation learning method for boosting molecular toxicity prediction. Methods 2020; 179: 55.","journal-title":"Methods"},{"key":"e_1_3_5_114_2","article-title":"Imagenet classification with deep convolutional neural networks","volume":"25","author":"Krizhevsky A.","year":"2012","unstructured":"Krizhevsky A., Sutskever I. and Hinton G.E.: Imagenet classification with deep convolutional neural networks. Adv Neural Inform Process Syst 2012; 25.","journal-title":"Adv Neural Inform Process Syst"},{"key":"e_1_3_5_115_2","first-page":"1","volume-title":"Proceedings of the IEEE conference on computer vision and pattern recognition","author":"Szegedy C.","year":"2015","unstructured":"Szegedy C., Liu W., Jia Y., Sermanet P., Reed S., Anguelov D., Erhan D., Vanhoucke V. and Rabinovich A.: Going deeper with convolutions. In: Proceedings of the IEEE conference on computer vision and pattern recognition. : 2015: 1."},{"key":"e_1_3_5_116_2","first-page":"7132","volume-title":"Proceedings of the IEEE conference on computer vision and pattern recognition","author":"Hu J.","year":"2018","unstructured":"Hu J., Shen L. and Sun G.: Squeeze-and-excitation networks. In: Proceedings of the IEEE conference on computer vision and pattern recognition. : 2018: 7132."},{"key":"e_1_3_5_117_2","first-page":"1492","volume-title":"Proceedings of the IEEE conference on computer vision and pattern recognition","author":"Xie S.","year":"2017","unstructured":"Xie S., Girshick R., Doll\u00e1r P., Tu Z. and He K.: Aggregated residual transformations for deep neural networks. In: Proceedings of the IEEE conference on computer vision and pattern recognition. : 2017: 1492."},{"key":"e_1_3_5_118_2","volume-title":"Thirty-first AAAI conference on artificial intelligence","author":"Szegedy C.","year":"2017","unstructured":"Szegedy C., Ioffe S., Vanhoucke V. and Alemi A.A.: Inception-v4, inception-resnet and the impact of residual connections on learning. In: Thirty-first AAAI conference on artificial intelligence. : 2017."},{"key":"e_1_3_5_119_2","doi-asserted-by":"crossref","first-page":"83","DOI":"10.1186\/s12859-018-2523-5","article-title":"Convolutional neural network based on smiles representation of compounds for detecting chemical motif","volume":"19","author":"Hirohara M.","year":"2018","unstructured":"Hirohara M., Saito Y., Koda Y., Sato K. and Sakakibara Y.: Convolutional neural network based on smiles representation of compounds for detecting chemical motif. BMC Bioinformatics 2018; 19: 83.","journal-title":"BMC Bioinformatics"},{"key":"e_1_3_5_120_2","unstructured":"I. Wallach M. Dzamba A. Heifets Atomnet: a deep convolutional neural network for bioactivity prediction in structure-based drug discovery arXiv preprint arXiv:1510.02855 (2015)."},{"key":"e_1_3_5_121_2","unstructured":"G.B. Goh C. Siegel A. Vishnu N.O. Hodas N. Baker Chemception: a deep neural network with minimal chemistry knowledge matches the performance of expert-developed qsar\/qspr models arXiv preprint arXiv:1706.06689 (2017)."},{"key":"e_1_3_5_122_2","doi-asserted-by":"crossref","first-page":"1340","DOI":"10.1109\/WACV.2018.00151","volume-title":"2018 IEEE Winter Conference on Applications of Computer Vision (WACV)","author":"Goh G.B.","year":"2018","unstructured":"Goh G.B., Siegel C., Vishnu A., Hodas N. and Baker N.: How much chemistry does a deep neural network need to know to make accurate predictions?. In: 2018 IEEE Winter Conference on Applications of Computer Vision (WACV). : IEEE 2018: 1340."},{"key":"e_1_3_5_123_2","doi-asserted-by":"publisher","DOI":"10.1021\/acs.jcim.6b00740"},{"key":"e_1_3_5_124_2","doi-asserted-by":"crossref","first-page":"3146","DOI":"10.1109\/ICPR48806.2021.9412555","volume-title":"2020 25th International Conference on Pattern Recognition (ICPR)","author":"Lim S.","year":"2021","unstructured":"Lim S. and Lee Y.O.: Predicting chemical properties using self-attention multi-task learning based on smiles representation. In: 2020 25th International Conference on Pattern Recognition (ICPR). : IEEE 2021: 3146."},{"key":"e_1_3_5_125_2","unstructured":"S. Honda S. Shi H.R. Ueda Smiles transformer: Pre-trained molecular fingerprint for low data drug discovery arXiv preprint arXiv:1911.04738 (2019)."},{"key":"e_1_3_5_126_2","doi-asserted-by":"crossref","first-page":"914","DOI":"10.1021\/acs.jcim.8b00803","article-title":"Identifying structure\u2013property relationships through smiles syntax analysis with self-attention mechanism","volume":"59","author":"Zheng S.","year":"2019","unstructured":"Zheng S., Yan X., Yang Y. and Xu J.: Identifying structure\u2013property relationships through smiles syntax analysis with self-attention mechanism. J Chem Inform Modeling 2019; 59: 914.","journal-title":"J Chem Inform Modeling"},{"key":"e_1_3_5_127_2","doi-asserted-by":"crossref","first-page":"bbab367","DOI":"10.1093\/bib\/bbab367","article-title":"A general optimization protocol for molecular property prediction using a deep learning network","volume":"23","author":"Chen J.-H.","year":"2022","unstructured":"Chen J.-H. and Tseng Y.J.: A general optimization protocol for molecular property prediction using a deep learning network. Briefings in Bioinformatics 2022; 23: bbab367.","journal-title":"Briefings in Bioinformatics"},{"key":"e_1_3_5_128_2","unstructured":"P. Ertl R. Lewis E. Martin V. Polyakov In silico generation of novel drug-like chemical matter using the lstm neural network arXiv preprint arXiv:1712.07449 (2017)."},{"key":"e_1_3_5_129_2","article-title":"A novel molecular representation learning for molecular property prediction with a multiple smiles-based augmentation","volume":"2022","author":"Li C.","year":"2022","unstructured":"Li C., Feng J., Liu S. and Yao J.: A novel molecular representation learning for molecular property prediction with a multiple smiles-based augmentation. Comput Intell Neurosci 2022; 2022.","journal-title":"Comput Intell Neurosci"},{"key":"e_1_3_5_130_2","article-title":"Maxsmi: maximizing molecular property prediction performance with confidence estimation using smiles augmentation and deep learning","volume":"1","author":"Kimber T.B.","year":"2021","unstructured":"Kimber T.B., Gagnebin M. and Volkamer A.: Maxsmi: maximizing molecular property prediction performance with confidence estimation using smiles augmentation and deep learning. Artif Intell Life Sci 2021; 1: 100014.","journal-title":"Artif Intell Life Sci"},{"key":"e_1_3_5_131_2","doi-asserted-by":"publisher","DOI":"10.1021\/ci100050t"},{"key":"e_1_3_5_132_2","first-page":"1503","article-title":"On the art of compiling and using\u2019drug-like\u2019chemical fragment spaces","volume":"3","author":"Degen J.","year":"2008","unstructured":"Degen J., Wegscheid-Gerlach C., Zaliani A. and Rarey M.: On the art of compiling and using\u2019drug-like\u2019chemical fragment spaces. , : Chemistry Enabling 2008; 3: 1503.","journal-title":", : Chemistry Enabling"},{"key":"e_1_3_5_133_2","doi-asserted-by":"crossref","unstructured":"D. Sumner J. He A. Thakkar O. Engkvist E.J. Bjerrum Levenshtein augmentation improves performance of smiles based deep-learning synthesis prediction (2020).","DOI":"10.26434\/chemrxiv.12562121.v2"},{"key":"e_1_3_5_134_2","article-title":"Attention is all you need","volume":"30","author":"Vaswani A.","year":"2017","unstructured":"Vaswani A., Shazeer N., Parmar N., Uszkoreit J., Jones L., Gomez A.N., Kaiser \u0141. and Polosukhin I.: Attention is all you need. Adv Neural Inform Processing Syst 2017; 30.","journal-title":"Adv Neural Inform Processing Syst"},{"key":"e_1_3_5_135_2","unstructured":"J.D.M.-W.C. Kenton L.K. Toutanova Bert: Pre-training of deep bidirectional transformers for language understanding in: Proceedings of NAACL-HLT 2019 pp. 4171\u20134186."},{"key":"e_1_3_5_136_2","doi-asserted-by":"publisher","DOI":"10.1093\/bioinformatics\/btz682"},{"key":"e_1_3_5_137_2","unstructured":"A. Dosovitskiy L. Beyer A. Kolesnikov D. Weissenborn X. Zhai T. Unterthiner M. Dehghani M. Minderer G. Heigold S. Gelly et al. An image is worth 16x16 words: Transformers for image recognition at scale in: International Conference on Learning Representations 2020."},{"key":"e_1_3_5_138_2","first-page":"10012","volume-title":"Proceedings of the IEEE\/CVF International Conference on Computer Vision","author":"Liu Z.","year":"2021","unstructured":"Liu Z., Lin Y., Cao Y., Hu H., Wei Y., Zhang Z., Lin S. and Guo B.: Swin transformer: Hierarchical vision transformer using shifted windows. In: Proceedings of the IEEE\/CVF International Conference on Computer Vision. : 2021: 10012."},{"key":"e_1_3_5_139_2","unstructured":"\u0141. Maziarka T. Danel S. Mucha K. Rataj J. Tabor S. Jastrzebski Molecule attention transformer arXiv preprint arXiv:2002.08264 (2020)."},{"key":"e_1_3_5_140_2","doi-asserted-by":"crossref","first-page":"429","DOI":"10.1145\/3307339.3342186","volume-title":"Proceedings of the 10th ACM international conference on bioinformatics, computational biology and health informatics","author":"Wang S.","year":"2019","unstructured":"Wang S., Guo Y., Wang Y., Sun H. and Huang J.: Smiles-bert: large scale unsupervised pre-training for molecular property prediction. In: Proceedings of the 10th ACM international conference on bioinformatics, computational biology and health informatics. : 2019: 429."},{"key":"e_1_3_5_141_2","doi-asserted-by":"crossref","unstructured":"D. Xue H. Zhang D. Xiao Y. Gong G. Chuai Y. Sun H. Tian H. Wu Y. Li Q. Liu X-mol: large-scale pre-training for molecular understanding and diverse molecular analysis bioRxiv (2021) 2020\u201312.","DOI":"10.1101\/2020.12.23.424259"},{"key":"e_1_3_5_142_2","unstructured":"J. Devlin M.-W. Chang K. Lee K. Toutanova Bert: Pre-training of deep bidirectional transformers for language understanding arXiv preprint arXiv:1810.04805 (2018)."},{"key":"e_1_3_5_143_2","unstructured":"W. Zhu Z. Li L. Cai G. Song Stepping back to smiles transformers for fast molecular representation inference arXiv preprint arXiv:2112.13305 (2021)."},{"key":"e_1_3_5_144_2","unstructured":"R. v. d. Berg T.N. Kipf M. Welling Graph convolutional matrix completion arXiv preprint arXiv:1706.02263 (2017)."},{"key":"e_1_3_5_145_2","article-title":"Protein interface prediction using graph convolutional networks","volume":"30","author":"Fout A.","year":"2017","unstructured":"Fout A., Byrd J., Shariat B. and Ben-Hur A.: Protein interface prediction using graph convolutional networks. Adv Neural Inform Processing Systems 2017; 30.","journal-title":"Adv Neural Inform Processing Systems"},{"key":"e_1_3_5_146_2","doi-asserted-by":"crossref","first-page":"919","DOI":"10.1093\/bib\/bbz042","article-title":"Graph convolutional networks for computational drug development and discovery","volume":"21","author":"Sun M.","year":"2020","unstructured":"Sun M., Zhao S., Gilvary C., Elemento O., Zhou J. and Wang F.: Graph convolutional networks for computational drug development and discovery. Briefings Bioinform 2020; 21: 919.","journal-title":"Briefings Bioinform"},{"key":"e_1_3_5_147_2","doi-asserted-by":"crossref","first-page":"595","DOI":"10.1007\/s10822-016-9938-8","article-title":"Molecular graph convolutions: moving beyond fingerprints","volume":"30","author":"Kearnes S.","year":"2016","unstructured":"Kearnes S., McCloskey K., Berndl M., Pande V. and Riley P.: Molecular graph convolutions: moving beyond fingerprints. J Computer-Aided Mol Des 2016; 30: 595.","journal-title":"J Computer-Aided Mol Des"},{"key":"e_1_3_5_148_2","first-page":"1263","volume-title":"International conference on machine learning","author":"Gilmer J.","year":"2017","unstructured":"Gilmer J., Schoenholz S.S., Riley P.F., Vinyals O. and Dahl G.E.: Neural message passing for quantum chemistry. In: International conference on machine learning. : PMLR 2017: 1263."},{"key":"e_1_3_5_149_2","doi-asserted-by":"crossref","first-page":"309","DOI":"10.1093\/bioinformatics\/bty535","article-title":"Compound\u2013protein interaction prediction with end-to-end learning of neural networks for graphs and sequences","volume":"35","author":"Tsubaki M.","year":"2019","unstructured":"Tsubaki M., Tomii K. and Sese J.: Compound\u2013protein interaction prediction with end-to-end learning of neural networks for graphs and sequences. Bioinformatics 2019; 35: 309.","journal-title":"Bioinformatics"},{"key":"e_1_3_5_150_2","doi-asserted-by":"crossref","first-page":"2200","DOI":"10.1021\/acs.analchem.0c04071","article-title":"Prediction of liquid chromatographic retention time with graph neural networks to assist in small molecule identification","volume":"93","author":"Yang Q.","year":"2021","unstructured":"Yang Q., Ji H., Lu H. and Zhang Z.: Prediction of liquid chromatographic retention time with graph neural networks to assist in small molecule identification. Anal Chem 2021; 93: 2200.","journal-title":"Anal Chem"},{"key":"e_1_3_5_151_2","doi-asserted-by":"crossref","first-page":"263","DOI":"10.1109\/ICSESS47205.2019.9040723","volume-title":"2019 IEEE 10th International Conference on Software Engineering and Service Science (ICSESS)","author":"Meng M.","year":"2019","unstructured":"Meng M., Wei Z., Li Z., Jiang M. and Bian Y.: Property prediction of molecules in graph convolutional neural network expansion. In: 2019 IEEE 10th International Conference on Software Engineering and Service Science (ICSESS). : IEEE 2019: 263."},{"key":"e_1_3_5_152_2","unstructured":"Y. Li D. Tarlow M. Brockschmidt R. Zemel Gated graph sequence neural networks arXiv preprint arXiv:1511.05493 (2015)."},{"key":"e_1_3_5_153_2","unstructured":"J. Klicpera J. Gro\u00df S. G\u00fcnnemann Directional message passing for molecular graphs in: International Conference on Learning Representations 2019."},{"key":"e_1_3_5_154_2","article-title":"Gemnet: Universal directional graph neural networks for molecules","volume":"34","author":"Klicpera J.","year":"2021","unstructured":"Klicpera J., Becker F. and G\u00fcnnemann S.: Gemnet: Universal directional graph neural networks for molecules. Adv Neural Inform Process Syst 2021; 34.","journal-title":"Adv Neural Inform Process Syst"},{"key":"e_1_3_5_155_2","unstructured":"S. Ryu J. Lim S.H. Hong W.Y. Kim Deeply learning molecular structure-property relationships using attention-and gate-augmented graph convolutional network arXiv preprint arXiv:1805.10988 (2018)."},{"key":"e_1_3_5_156_2","doi-asserted-by":"crossref","first-page":"8749","DOI":"10.1021\/acs.jmedchem.9b00959","article-title":"Pushing the boundaries of molecular representation for drug discovery with the graph attention mechanism","volume":"63","author":"Xiong Z.","year":"2019","unstructured":"Xiong Z., Wang D., Liu X., Zhong F., Wan X., Li X., Li Z., Luo X., Chen K., Jiang H. et al: Pushing the boundaries of molecular representation for drug discovery with the graph attention mechanism. J Med Chem 2019; 63: 8749.","journal-title":"J Med Chem"},{"key":"e_1_3_5_157_2","doi-asserted-by":"crossref","unstructured":"C. Lu Q. Liu C. Wang Z. Huang P. Lin L. He Molecular property prediction: A multilevel quantum interactions modeling perspective in: Proceedings of the AAAI Conference on Artificial Intelligence volume 33 2019 pp. 1052\u20131060.","DOI":"10.1609\/aaai.v33i01.33011052"},{"key":"e_1_3_5_158_2","doi-asserted-by":"publisher","DOI":"10.1038\/nature24270"},{"key":"e_1_3_5_159_2","doi-asserted-by":"crossref","first-page":"70","DOI":"10.2352\/ISSN.2470-1173.2017.19.AVM-023","article-title":"Deep reinforcement learning framework for autonomous driving","volume":"2017","author":"Sallab A.E.","year":"2017","unstructured":"Sallab A.E., Abdou M., Perot E. and Yogamani S.: Deep reinforcement learning framework for autonomous driving. Electronic Imaging 2017; 2017: 70.","journal-title":"Electronic Imaging"},{"key":"e_1_3_5_160_2","doi-asserted-by":"publisher","DOI":"10.1038\/s41586-021-03819-2"},{"key":"e_1_3_5_161_2","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1186\/s13321-017-0235-x","article-title":"Molecular de-novo design through deep reinforcement learning","volume":"9","author":"Olivecrona M.","year":"2017","unstructured":"Olivecrona M., Blaschke T., Engkvist O. and Chen H.: Molecular de-novo design through deep reinforcement learning. J Cheminformatics 2017; 9: 1.","journal-title":"J Cheminformatics"},{"key":"e_1_3_5_162_2","doi-asserted-by":"publisher","DOI":"10.1021\/acs.jcim.9b00325"},{"key":"e_1_3_5_163_2","doi-asserted-by":"crossref","first-page":"6716","DOI":"10.1021\/jm060167o","article-title":"Do structurally similar ligands bind in a similar fashion?","volume":"49","author":"Bostr\u00f6m J.","year":"2006","unstructured":"Bostr\u00f6m J., Hogner A. and Schmitt S.: Do structurally similar ligands bind in a similar fashion?. J Med Chem 2006; 49: 6716.","journal-title":"J Med Chem"},{"key":"e_1_3_5_164_2","unstructured":"Levenshtein V.I. et al: Binary codes capable of correcting deletions insertions and reversals. In: . : Soviet Union 1966: 707."},{"key":"e_1_3_5_165_2","article-title":"Graph convolutional policy network for goal-directed molecular graph generation","volume":"31","author":"You J.","year":"2018","unstructured":"You J., Liu B., Ying Z., Pande V. and Leskovec J.: Graph convolutional policy network for goal-directed molecular graph generation. Adv Neural Inform Process Syst 2018; 31.","journal-title":"Adv Neural Inform Process Syst"},{"key":"e_1_3_5_166_2","first-page":"1","article-title":"Optimization of molecules via deep reinforcement learning","volume":"9","author":"Zhou Z.","year":"2019","unstructured":"Zhou Z., Kearnes S., Li L., Zare R.N. and Riley P.: Optimization of molecules via deep reinforcement learning. Sci Rep 2019; 9: 1.","journal-title":"Sci Rep"},{"key":"e_1_3_5_167_2","doi-asserted-by":"publisher","DOI":"10.1021\/acs.jcim.1c01341"},{"key":"e_1_3_5_168_2","doi-asserted-by":"crossref","first-page":"32984","DOI":"10.1021\/acsomega.0c04153","article-title":"Molecular design in synthetically accessible chemical space via deep reinforcement learning","volume":"5","author":"Horwood J.","year":"2020","unstructured":"Horwood J. and Noutahi E.: Molecular design in synthetically accessible chemical space via deep reinforcement learning. ACS Omega 2020; 5: 32984.","journal-title":"ACS Omega"},{"key":"e_1_3_5_169_2","first-page":"2323","volume-title":"International conference on machine learning","author":"Jin W.","year":"2018","unstructured":"Jin W., Barzilay R. and Jaakkola T.: Junction tree variational autoencoder for molecular graph generation. In: International conference on machine learning. : PMLR 2018: 2323."},{"key":"e_1_3_5_170_2","doi-asserted-by":"crossref","first-page":"6065","DOI":"10.1021\/acs.jcim.0c00675","article-title":"Zinc20\u2014a free ultralarge-scale chemical database for ligand discovery","volume":"60","author":"Irwin J.J.","year":"2020","unstructured":"Irwin J.J., Tang K.G., Young J., Dandarchuluun C., Wong B.R., Khurelbaatar M., Moroz Y.S., Mayfield J. and Sayle R.A.: Zinc20\u2014a free ultralarge-scale chemical database for ligand discovery. J Chem Inform Modeling 2020; 60: 6065.","journal-title":"J Chem Inform Modeling"},{"key":"e_1_3_5_171_2","doi-asserted-by":"publisher","DOI":"10.1016\/j.trechm.2020.11.004"},{"key":"e_1_3_5_172_2","doi-asserted-by":"publisher","DOI":"10.1016\/j.drudis.2020.01.020"},{"key":"e_1_3_5_173_2","doi-asserted-by":"publisher","DOI":"10.1021\/acs.jcim.0c00321"},{"key":"e_1_3_5_174_2","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1186\/s13321-019-0391-2","article-title":"Dataset\u2019s chemical diversity limits the generalizability of machine learning predictions","volume":"11","author":"Glavatskikh M.","year":"2019","unstructured":"Glavatskikh M., Leguy J., Hunault G., Cauchy T. and Da Mota B.: Dataset\u2019s chemical diversity limits the generalizability of machine learning predictions. J Cheminformatics 2019; 11: 1.","journal-title":"J Cheminformatics"},{"key":"e_1_3_5_175_2","doi-asserted-by":"crossref","unstructured":"F. Broccatelli R. Trager M. Reutlinger G. Karypis M. Li Benchmarking accuracy and generalizability of four graph neural networks using large in vitro adme datasets from different chemical spaces arXiv preprint arXiv:2111.13964 (2021).","DOI":"10.1002\/minf.202100321"},{"key":"e_1_3_5_176_2","doi-asserted-by":"crossref","first-page":"D1102","DOI":"10.1093\/nar\/gky1033","article-title":"Pubchem 2019 update: improved access to chemical data","volume":"47","author":"Kim S.","year":"2019","unstructured":"Kim S., Chen J., Cheng T., Gindulyte A., He J., He S., Li Q., Shoemaker B.A., Thiessen P.A., Yu B. et al: Pubchem 2019 update: improved access to chemical data. Nucl Acids Res 2019; 47: D1102.","journal-title":"Nucl Acids Res"},{"key":"e_1_3_5_177_2","unstructured":"W. Hu B. Liu J. Gomes M. Zitnik P. Liang V. Pande J. Leskovec Strategies for pre-training graph neural networks arXiv preprint arXiv:1905.12265 (2019)."},{"key":"e_1_3_5_178_2","article-title":"N-gram graph: Simple unsupervised representation for graphs, with applications to molecules","volume":"32","author":"Liu S.","year":"2019","unstructured":"Liu S., Demirel M.F. and Liang Y.: N-gram graph: Simple unsupervised representation for graphs, with applications to molecules. Adv Neural Inform Process Syst 2019; 32.","journal-title":"Adv Neural Inform Process Syst"},{"key":"e_1_3_5_179_2","doi-asserted-by":"crossref","first-page":"1857","DOI":"10.1145\/3394486.3403237","volume-title":"Proceedings of the 26th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining","author":"Hu Z.","year":"2020","unstructured":"Hu Z., Dong Y., Wang K., Chang K.-W. and Sun Y.: Gpt-gnn: Generative pre-training of graph neural networks. In: Proceedings of the 26th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining. : 2020: 1857."},{"key":"e_1_3_5_180_2","first-page":"12559","article-title":"Self-supervised graph transformer on large-scale molecular data","volume":"33","author":"Rong Y.","year":"2020","unstructured":"Rong Y., Bian Y., Xu T., Xie W., Wei Y., Huang W. and Huang J.: Self-supervised graph transformer on large-scale molecular data. Adv Neural Inform Process Syst 2020; 33: 12559.","journal-title":"Adv Neural Inform Process Syst"},{"key":"e_1_3_5_181_2","article-title":"Motif-based graph self-supervised learning for molecular property prediction","volume":"34","author":"Zhang Z.","year":"2021","unstructured":"Zhang Z., Liu Q., Wang H., Lu C. and Lee C.-K.: Motif-based graph self-supervised learning for molecular property prediction. Adv Neural Inform Process Syst 2021; 34.","journal-title":"Adv Neural Inform Process Syst"},{"key":"e_1_3_5_182_2","doi-asserted-by":"crossref","first-page":"bbab109","DOI":"10.1093\/bib\/bbab109","article-title":"An effective self-supervised framework for learning expressive molecular global representations to drug discovery","volume":"22","author":"Li P.","year":"2021","unstructured":"Li P., Wang J., Qiao Y., Chen H., Yu Y., Yao X., Gao P., Xie G. and Song S.: An effective self-supervised framework for learning expressive molecular global representations to drug discovery. Briefings Bioinformatics 2021; 22: bbab109.","journal-title":"Briefings Bioinformatics"},{"key":"e_1_3_5_183_2","unstructured":"D. Kim J. Baek S.J. Hwang Graph self-supervised learning with accurate discrepancy learning arXiv preprint arXiv:2202.02989 (2022)."},{"key":"e_1_3_5_184_2","doi-asserted-by":"crossref","first-page":"1150","DOI":"10.1145\/3394486.3403168","volume-title":"Proceedings of the 26th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining","author":"Qiu J.","year":"2020","unstructured":"Qiu J., Chen Q., Dong Y., Zhang J., Yang H., Ding M., Wang K. and Tang J.: Gcc: Graph contrastive coding for graph neural network pre-training. In: Proceedings of the 26th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining. : 2020: 1150."},{"key":"e_1_3_5_185_2","doi-asserted-by":"crossref","first-page":"2081","DOI":"10.1145\/3442381.3449822","volume-title":"Proceedings of the Web Conference 2021","author":"Sun Q.","year":"2021","unstructured":"Sun Q., Li J., Peng H., Wu J., Ning Y., Yu P.S. and He L.: Sugar: Subgraph neural network with reinforcement pooling and self-supervised mutual information mechanism. In: Proceedings of the Web Conference 2021. : 2021: 2081."},{"key":"e_1_3_5_186_2","unstructured":"H. Hafidi M. Ghogho P. Ciblat A. Swami Graphcl: Contrastive self-supervised learning of graph representations arXiv preprint arXiv:2007.08025 (2020)."},{"key":"e_1_3_5_187_2","unstructured":"J. Zeng P. Xie Contrastive self-supervised learning for graph classification arXiv preprint arXiv:2009.05923 (2020)."},{"key":"e_1_3_5_188_2","doi-asserted-by":"crossref","first-page":"123","DOI":"10.1007\/978-3-030-73194-6_10","volume-title":"International Conference on Database Systems for Advanced Applications","author":"Ren Y.","year":"2021","unstructured":"Ren Y., Bai J. and Zhang J.: Label contrastive coding based graph neural network for graph classification. In: International Conference on Database Systems for Advanced Applications. : Springer 2021: 123."},{"key":"e_1_3_5_189_2","first-page":"4401","volume-title":"Proceedings of the IEEE\/CVF conference on computer vision and pattern recognition","author":"Karras T.","year":"2019","unstructured":"Karras T., Laine S. and Aila T.: A style-based generator architecture for generative adversarial networks. In: Proceedings of the IEEE\/CVF conference on computer vision and pattern recognition. : 2019: 4401."},{"key":"e_1_3_5_190_2","article-title":"Generating diverse high-fidelity images with vq-vae-2","volume":"32","author":"Razavi A.","year":"2019","unstructured":"Razavi A., Van den Oord A. and Vinyals O.: Generating diverse high-fidelity images with vq-vae-2. Adv Neural Inform Process Syst 2019; 32.","journal-title":"Adv Neural Inform Process Syst"},{"key":"e_1_3_5_191_2","first-page":"11157","volume-title":"Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition","author":"Rao K.","year":"2020","unstructured":"Rao K., Harris C., Irpan A., Levine S., Ibarz J. and Khansari M.: Rl-cyclegan: Reinforcement learning aware simulation-to-real. In: Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition. : 2020: 11157."},{"key":"e_1_3_5_192_2","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1186\/s13321-019-0404-1","article-title":"Mol-cyclegan: a generative model for molecular optimization","volume":"12","author":"Maziarka \u0141.","year":"2020","unstructured":"Maziarka \u0141., Pocha A., Kaczmarczyk J., Rataj K., Danel T. and Warcho\u0142 M.: Mol-cyclegan: a generative model for molecular optimization. J Cheminformatics 2020; 12: 1.","journal-title":"J Cheminformatics"},{"key":"e_1_3_5_193_2","first-page":"2223","volume-title":"Proceedings of the IEEE international conference on computer vision","author":"Zhu J.-Y.","year":"2017","unstructured":"Zhu J.-Y., Park T., Isola P. and Efros A.A.: Unpaired image-to-image translation using cycle-consistent adversarial networks. In: Proceedings of the IEEE international conference on computer vision. : 2017: 2223."},{"key":"e_1_3_5_194_2","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1038\/s41467-022-28526-y","article-title":"Inverse design of 3d molecular structures with conditional generative neural networks","volume":"13","author":"Gebauer N.W.","year":"2022","unstructured":"Gebauer N.W., Gastegger M., Hessmann S.S., M\u00fcller K.-R. and Sch\u00fctt K.T.: Inverse design of 3d molecular structures with conditional generative neural networks. Nature communications 2022; 13: 1.","journal-title":"Nature communications"},{"key":"e_1_3_5_195_2","first-page":"8959","volume-title":"International Conference on Machine Learning","author":"Simm G.","year":"2020","unstructured":"Simm G., Pinsler R. and Hern\u00e1ndez-Lobato J.M.: Reinforcement learning for molecular design guided by quantum mechanics. In: International Conference on Machine Learning. : PMLR 2020: 8959."},{"key":"e_1_3_5_196_2","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1038\/s41598-020-78537-2","article-title":"Autonomous molecule generation using reinforcement learning and docking to develop potential novel inhibitors","volume":"10","author":"Jeon W.","year":"2020","unstructured":"Jeon W. and Kim D.: Autonomous molecule generation using reinforcement learning and docking to develop potential novel inhibitors. Scientific reports 2020; 10: 1.","journal-title":"Scientific reports"},{"key":"e_1_3_5_197_2","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1038\/s41467-019-13807-w","article-title":"De novo generation of hit-like molecules from gene expression signatures using artificial intelligence","volume":"11","author":"M\u00e9ndez-Lucio O.","year":"2020","unstructured":"M\u00e9ndez-Lucio O., Baillif B., Clevert D.-A., Rouqui\u00e9 D. and Wichard J.: De novo generation of hit-like molecules from gene expression signatures using artificial intelligence. Nature Commun 2020; 11: 1.","journal-title":"Nature Commun"},{"key":"e_1_3_5_198_2","doi-asserted-by":"crossref","first-page":"3663","DOI":"10.1145\/3442381.3449796","volume-title":"Proceedings of the Web Conference 2021","author":"Wang Y.","year":"2021","unstructured":"Wang Y., Wang W., Liang Y., Cai Y. and Hooi B.: Mixup for node and graph classification. In: Proceedings of the Web Conference 2021. : 2021: 3663."},{"key":"e_1_3_5_199_2","doi-asserted-by":"crossref","unstructured":"J. Park H. Shim E. Yang Graph transplant: Node saliency-guided graph mixup with local structure preservation arXiv preprint arXiv:2111.05639 (2021).","DOI":"10.1609\/aaai.v36i7.20767"},{"key":"e_1_3_5_200_2","unstructured":"H. Zhang M. Cisse Y.N. Dauphin D. Lopez-Paz mixup: Beyond empirical risk minimization in: International Conference on Learning Representations 2018."},{"key":"e_1_3_5_201_2","first-page":"6438","volume-title":"International Conference on Machine Learning","author":"Verma V.","year":"2019","unstructured":"Verma V., Lamb A., Beckham C., Najafi A., Mitliagkas I., Lopez-Paz D. and Bengio Y.: Manifold mixup: Better representations by interpolating hidden states. In: International Conference on Machine Learning. : PMLR 2019: 6438."},{"key":"e_1_3_5_202_2","doi-asserted-by":"publisher","DOI":"10.1021\/ci400187y"},{"key":"e_1_3_5_203_2","unstructured":"K. Swanson Message passing neural networks for molecular property prediction Ph.D. thesis Massachusetts Institute of Technology 2019."},{"key":"e_1_3_5_204_2","doi-asserted-by":"crossref","unstructured":"J. Chen S. Zheng Y. Song J. Rao Y. Yang Learning attributed graph representations with communicative message passing transformer arXiv preprint arXiv:2107.08773 (2021).","DOI":"10.24963\/ijcai.2021\/309"},{"key":"e_1_3_5_205_2","doi-asserted-by":"publisher","DOI":"10.1038\/nrd823"},{"key":"e_1_3_5_206_2","doi-asserted-by":"crossref","first-page":"182","DOI":"10.1016\/S2221-1691(15)30003-4","article-title":"Exposure to polycyclic aromatic hydrocarbons with special focus on cancer","volume":"5","author":"Rengarajan T.","year":"2015","unstructured":"Rengarajan T., Rajendran P., Nandakumar N., Lokeshkumar B., Rajendran P. and Nishigaki I.: Exposure to polycyclic aromatic hydrocarbons with special focus on cancer. Asian Pacific J Tropical Biomed 2015; 5: 182.","journal-title":"Asian Pacific J Tropical Biomed"},{"key":"e_1_3_5_207_2","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1038\/srep38660","article-title":"A predictive model for toxicity effects assessment of biotransformed hepatic drugs using iterative sampling method","volume":"6","author":"Tharwat A.","year":"2016","unstructured":"Tharwat A., Moemen Y.S. and Hassanien A.E.: A predictive model for toxicity effects assessment of biotransformed hepatic drugs using iterative sampling method. Sci Rep 2016; 6: 1.","journal-title":"Sci Rep"},{"key":"e_1_3_5_208_2","doi-asserted-by":"crossref","first-page":"226","DOI":"10.1016\/j.patrec.2009.10.013","article-title":"A rough set approach to feature selection based on ant colony optimization","volume":"31","author":"Chen Y.","year":"2010","unstructured":"Chen Y., Miao D. and Wang R.: A rough set approach to feature selection based on ant colony optimization. Pattern Recogn Lett 2010; 31: 226.","journal-title":"Pattern Recogn Lett"},{"key":"e_1_3_5_209_2","doi-asserted-by":"publisher","DOI":"10.1007\/BF02977789"},{"key":"e_1_3_5_210_2","doi-asserted-by":"publisher","DOI":"10.1016\/S0006-2952(02)01055-9"},{"key":"e_1_3_5_211_2","doi-asserted-by":"crossref","first-page":"675","DOI":"10.1080\/01926230500321213","article-title":"A gene expression signature that predicts the future onset of drug-induced renal tubular toxicity","volume":"33","author":"Fielden M.R.","year":"2005","unstructured":"Fielden M.R., Eynon B.P., Natsoulis G., Jarnagin K., Banas D. and Kolaja K.L.: A gene expression signature that predicts the future onset of drug-induced renal tubular toxicity. Toxicol Pathol 2005; 33: 675.","journal-title":"Toxicol Pathol"},{"key":"e_1_3_5_212_2","doi-asserted-by":"publisher","DOI":"10.1039\/C8MO00042E"},{"key":"e_1_3_5_213_2","doi-asserted-by":"publisher","DOI":"10.3390\/ijms19082358"},{"key":"e_1_3_5_214_2","doi-asserted-by":"crossref","first-page":"20","DOI":"10.1021\/acs.chemrestox.9b00227","article-title":"An overview of machine learning and big data for drug toxicity evaluation","volume":"33","author":"Vo A.H.","year":"2019","unstructured":"Vo A.H., Van Vleet T.R., Gupta R.R., Liguori M.J. and Rao M.S.: An overview of machine learning and big data for drug toxicity evaluation. Chem Res Toxicol 2019; 33: 20.","journal-title":"Chem Res Toxicol"},{"key":"e_1_3_5_215_2","doi-asserted-by":"publisher","DOI":"10.1021\/acs.jcim.1c00967"},{"key":"e_1_3_5_216_2","first-page":"1","article-title":"A novel heterogeneous network-based method for drug response prediction in cancer cell lines","volume":"8","author":"Zhang F.","year":"2018","unstructured":"Zhang F., Wang M., Xi J., Yang J. and Li A.: A novel heterogeneous network-based method for drug response prediction in cancer cell lines. Sci Rep 2018; 8: 1.","journal-title":"Sci Rep"},{"key":"e_1_3_5_217_2","doi-asserted-by":"crossref","first-page":"346","DOI":"10.1093\/bib\/bbz153","article-title":"Improving drug response prediction by integrating multiple data sources: matrix factorization, kernel and network-based approaches","volume":"22","author":"G\u00fcven\u00e7 Paltun B.","year":"2021","unstructured":"G\u00fcven\u00e7 Paltun B., Mamitsuka H. and Kaski S.: Improving drug response prediction by integrating multiple data sources: matrix factorization, kernel and network-based approaches. Briefings Bioinform 2021; 22: 346.","journal-title":"Briefings Bioinform"},{"key":"e_1_3_5_218_2","first-page":"1541","article-title":"A review on compound-protein interaction prediction methods: data, format, representation and model, Computational and Structural","volume":"19","author":"Lim S.","year":"2021","unstructured":"Lim S., Lu Y., Cho C.Y., Sung I., Kim J., Kim Y., Park S. and Kim S.: A review on compound-protein interaction prediction methods: data, format, representation and model, Computational and Structural. Biotechnol J 2021; 19: 1541.","journal-title":"Biotechnol J"},{"key":"e_1_3_5_219_2","doi-asserted-by":"publisher","DOI":"10.1016\/j.coche.2019.08.004"},{"key":"e_1_3_5_220_2","doi-asserted-by":"crossref","first-page":"969","DOI":"10.2174\/092986609788923374","article-title":"Prediction of small molecules\u2019 metabolic pathways based on functional group composition","volume":"16","author":"Lu J.","year":"2009","unstructured":"Lu J., Niu B., Liu L., Lu W.-C. and Cai Y.-D.: Prediction of small molecules\u2019 metabolic pathways based on functional group composition. Protein Pept Lett 2009; 16: 969.","journal-title":"Protein Pept Lett"},{"key":"e_1_3_5_221_2","doi-asserted-by":"crossref","first-page":"1771","DOI":"10.1021\/jm00168a036","article-title":"Evolution of a series of peptidoleukotriene antagonists: Synthesis and structure-activity relationships of 1, 6-disubstituted indoles and indazoles","volume":"33","author":"Brown F.J.","year":"1990","unstructured":"Brown F.J., Yee Y.K., Cronk L.A., Hebbel K.C., Krell R.D. and Snyder D.W.: Evolution of a series of peptidoleukotriene antagonists: Synthesis and structure-activity relationships of 1, 6-disubstituted indoles and indazoles. J Med Chem 1990; 33: 1771.","journal-title":"J Med Chem"},{"key":"e_1_3_5_222_2","doi-asserted-by":"publisher","DOI":"10.1021\/acs.jmedchem.6b01287"},{"key":"e_1_3_5_223_2","article-title":"Property-aware relation networks for few-shot molecular property prediction","volume":"34","author":"Wang Y.","year":"2021","unstructured":"Wang Y., Abuduweili A., Yao Q. and Dou D.: Property-aware relation networks for few-shot molecular property prediction. Adv Neural Inform Process Syst 2021; 34.","journal-title":"Adv Neural Inform Process Syst"},{"key":"e_1_3_5_224_2","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1186\/s13321-015-0098-y","article-title":"Target prediction utilising negative bioactivity data covering large chemical space","volume":"7","author":"Mervin L.H.","year":"2015","unstructured":"Mervin L.H., Afzal A.M., Drakakis G., Lewis R., Engkvist O. and Bender A.: Target prediction utilising negative bioactivity data covering large chemical space. J Cheminformatics 2015; 7: 1.","journal-title":"J Cheminformatics"},{"key":"e_1_3_5_225_2","doi-asserted-by":"publisher","DOI":"10.1093\/bioinformatics\/btv256"},{"key":"e_1_3_5_226_2","first-page":"1929","article-title":"Dropout: a simple way to prevent neural networks from overfitting","volume":"15","author":"Srivastava N.","year":"2014","unstructured":"Srivastava N., Hinton G., Krizhevsky A., Sutskever I. and Salakhutdinov R.: Dropout: a simple way to prevent neural networks from overfitting. J Mach Learn Res 2014; 15: 1929.","journal-title":"J Mach Learn Res"},{"key":"e_1_3_5_227_2","unstructured":"C. Zhang O. Vinyals R. Munos S. Bengio A study on overfitting in deep reinforcement learning arXiv preprint arXiv:1804.06893 (2018)."},{"key":"e_1_3_5_228_2","unstructured":"M. Hardt B. Recht Y. Singer Train faster generalize better: Stability of stochastic gradient descent in: International conference on machine learning PMLR 2016 pp. 1225\u20131234."},{"key":"e_1_3_5_229_2","doi-asserted-by":"publisher","DOI":"10.1093\/bioinformatics\/btr444"},{"key":"e_1_3_5_230_2","doi-asserted-by":"crossref","first-page":"880","DOI":"10.1145\/1390156.1390267","volume-title":"Proceedings of the 25th international conference on Machine learning","author":"Salakhutdinov R.","year":"2008","unstructured":"Salakhutdinov R. and Mnih A.: Bayesian probabilistic matrix factorization using markov chain monte carlo. In: Proceedings of the 25th international conference on Machine learning. : 2008: 880."},{"key":"e_1_3_5_231_2","doi-asserted-by":"crossref","first-page":"783","DOI":"10.1021\/ci400084k","article-title":"Time-split cross-validation as a method for estimating the goodness of prospective prediction","volume":"53","author":"Sheridan R.P.","year":"2013","unstructured":"Sheridan R.P.: Time-split cross-validation as a method for estimating the goodness of prospective prediction. J Chem Inform Modeling 2013; 53: 783.","journal-title":"J Chem Inform Modeling"},{"key":"e_1_3_5_232_2","doi-asserted-by":"crossref","first-page":"4263","DOI":"10.1021\/acs.jcim.0c00155","article-title":"Lit-pcba: An unbiased data set for machine learning and virtual screening","volume":"60","author":"Tran-Nguyen V.-K.","year":"2020","unstructured":"Tran-Nguyen V.-K., Jacquemard C. and Rognan D.: Lit-pcba: An unbiased data set for machine learning and virtual screening. J Chem Inform Modeling 2020; 60: 4263.","journal-title":"J Chem Inform Modeling"},{"key":"e_1_3_5_233_2","doi-asserted-by":"crossref","unstructured":"H. Cai H. Zhang D. Zhao J. Wu L. Wang Fp-gnn: a versatile deep learning architecture for enhanced molecular property prediction arXiv preprint arXiv:2205.03834 (2022).","DOI":"10.1093\/bib\/bbac408"},{"key":"e_1_3_5_234_2","doi-asserted-by":"crossref","first-page":"131","DOI":"10.3390\/biom8040131","article-title":"Improving chemical autoencoder latent space and molecular de novo generation diversity with heteroencoders","volume":"8","author":"Bjerrum E.J.","year":"2018","unstructured":"Bjerrum E.J. and Sattarov B.: Improving chemical autoencoder latent space and molecular de novo generation diversity with heteroencoders. Biomolecules 2018; 8: 131.","journal-title":"Biomolecules"}],"container-title":["Computational and Structural Biotechnology Journal"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/api.elsevier.com\/content\/article\/PII:S2001037022003300?httpAccept=text\/xml","content-type":"text\/xml","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/api.elsevier.com\/content\/article\/PII:S2001037022003300?httpAccept=text\/plain","content-type":"text\/plain","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/spj.science.org\/doi\/pdf\/10.1016\/j.csbj.2022.07.049","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2026,5,17]],"date-time":"2026-05-17T12:55:24Z","timestamp":1779022524000},"score":1,"resource":{"primary":{"URL":"https:\/\/spj.science.org\/doi\/10.1016\/j.csbj.2022.07.049"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2022,1]]},"references-count":233,"alternative-id":["10.1016\/j.csbj.2022.07.049"],"URL":"https:\/\/doi.org\/10.1016\/j.csbj.2022.07.049","relation":{},"ISSN":["2001-0370"],"issn-type":[{"value":"2001-0370","type":"electronic"}],"subject":[],"published":{"date-parts":[[2022,1]]},"assertion":[{"value":"Elsevier","name":"publisher","label":"This article is maintained by"},{"value":"On modeling and utilizing chemical compound information with deep learning technologies: A task-oriented approach","name":"articletitle","label":"Article Title"},{"value":"Computational and Structural Biotechnology Journal","name":"journaltitle","label":"Journal Title"},{"value":"https:\/\/doi.org\/10.1016\/j.csbj.2022.07.049","name":"articlelink","label":"CrossRef DOI link to publisher maintained version"},{"value":"article","name":"content_type","label":"Content Type"},{"value":"\u00a9 2022 The Author(s). Published by Elsevier B.V. on behalf of Research Network of Computational and Structural Biotechnology.","name":"copyright","label":"Copyright"}]}}