{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,8,29]],"date-time":"2026-08-29T11:17:23Z","timestamp":1788002243799,"version":"build-2784847793"},"reference-count":66,"publisher":"Springer Science and Business Media LLC","issue":"1","license":[{"start":{"date-parts":[[2020,1,8]],"date-time":"2020-01-08T00:00:00Z","timestamp":1578441600000},"content-version":"tdm","delay-in-days":0,"URL":"http:\/\/creativecommons.org\/licenses\/by\/4.0\/"},{"start":{"date-parts":[[2020,1,8]],"date-time":"2020-01-08T00:00:00Z","timestamp":1578441600000},"content-version":"vor","delay-in-days":0,"URL":"http:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"DOI":"10.13039\/100010661","name":"Horizon 2020 Framework Programme","doi-asserted-by":"publisher","award":["676434"],"award-info":[{"award-number":["676434"]}],"id":[{"id":"10.13039\/100010661","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":["J Cheminform"],"published-print":{"date-parts":[[2020,12]]},"abstract":"<jats:title>Abstract<\/jats:title>\n                  <jats:p>Neural Message Passing for graphs is a promising and relatively recent approach for applying Machine Learning to networked data. As molecules can be described intrinsically as a molecular graph, it makes sense to apply these techniques to improve molecular property prediction in the field of cheminformatics. We introduce Attention and Edge Memory schemes to the existing message passing neural network framework, and benchmark our approaches against eight different physical\u2013chemical and bioactivity datasets from the literature. We remove the need to introduce a priori knowledge of the task and chemical descriptor calculation by using only fundamental graph-derived properties. Our results consistently perform on-par with other state-of-the-art machine learning approaches, and set a new standard on sparse multi-task virtual screening targets. We also investigate model performance as a function of dataset preprocessing, and make some suggestions regarding hyperparameter selection.<\/jats:p>","DOI":"10.1186\/s13321-019-0407-y","type":"journal-article","created":{"date-parts":[[2020,1,8]],"date-time":"2020-01-08T12:02:33Z","timestamp":1578484953000},"update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":184,"title":["Building attention and edge message passing neural networks for bioactivity and physical\u2013chemical property prediction"],"prefix":"10.1186","volume":"12","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-9706-8698","authenticated-orcid":false,"given":"M.","family":"Withnall","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"E.","family":"Lindel\u00f6f","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"O.","family":"Engkvist","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"H.","family":"Chen","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"297","published-online":{"date-parts":[[2020,1,8]]},"reference":[{"key":"407_CR1","doi-asserted-by":"publisher","DOI":"10.1002\/jps.2600690938","author":"GL Flynn","year":"1980","unstructured":"Flynn GL (1980) Substituent constants for correlation analysis in chemistry and biology. J Pharm Sci. https:\/\/doi.org\/10.1002\/jps.2600690938","journal-title":"J Pharm Sci"},{"issue":"5","key":"407_CR2","doi-asserted-by":"publisher","first-page":"683","DOI":"10.1021\/ci00015a005","volume":"33","author":"G Ruecker","year":"1993","unstructured":"Ruecker G, Ruecker C (1993) Counts of all walks as atomic and molecular descriptors. J Chem Inf Comput Sci 33(5):683\u2013695","journal-title":"J Chem Inf Comput Sci"},{"issue":"2","key":"407_CR3","first-page":"237","volume":"56","author":"A Mauri","year":"2006","unstructured":"Mauri A, Consonni V, Pavan M, Todeschini R (2006) Dragon software: an easy approach to molecular descriptor calculations. Match 56(2):237\u2013248","journal-title":"Match"},{"issue":"3","key":"407_CR4","doi-asserted-by":"publisher","first-page":"622","DOI":"10.1016\/j.jmgm.2007.02.005","volume":"26","author":"JH Nettles","year":"2007","unstructured":"Nettles JH et al (2007) Flexible 3D pharmacophores as descriptors of dynamic biological space. J Mol Graph Model 26(3):622\u2013633. https:\/\/doi.org\/10.1016\/j.jmgm.2007.02.005","journal-title":"J Mol Graph Model"},{"key":"407_CR5","doi-asserted-by":"publisher","DOI":"10.1002\/9783527613106","volume-title":"Handbook of molecular descriptors","author":"R Todeschini","year":"2000","unstructured":"Todeschini R, Consonni V (2000) Handbook of molecular descriptors. Wiley VCH, Weinheim"},{"issue":"4","key":"407_CR6","doi-asserted-by":"publisher","first-page":"263","DOI":"10.1002\/cem.1180080405","volume":"8","author":"R Todeschini","year":"1994","unstructured":"Todeschini R, Lasagni M, Marengo E (1994) New molecular descriptors for 2D and 3D structures. Theory. J Chemom 8(4):263\u2013272","journal-title":"J Chemom"},{"key":"407_CR7","doi-asserted-by":"crossref","unstructured":"Kriege NM, Johansson FD, Morris C (2019) A Survey on Graph Kernels. ArXiv190311835 Cs Stat","DOI":"10.1007\/s41109-019-0195-3"},{"issue":"5","key":"407_CR8","doi-asserted-by":"publisher","first-page":"742","DOI":"10.1021\/ci100050t","volume":"50","author":"D Rogers","year":"2010","unstructured":"Rogers D, Hahn M (2010) Extended-connectivity fingerprints. J Chem Inf Model 50(5):742\u2013754. https:\/\/doi.org\/10.1021\/ci100050t","journal-title":"J Chem Inf Model"},{"issue":"6","key":"407_CR9","doi-asserted-by":"publisher","first-page":"1241","DOI":"10.1016\/j.drudis.2018.01.039","volume":"23","author":"H Chen","year":"2018","unstructured":"Chen H, Engkvist O, Wang Y, Olivecrona M, Blaschke T (2018) The rise of deep learning in drug discovery. Drug Discov Today 23(6):1241\u20131250. https:\/\/doi.org\/10.1016\/j.drudis.2018.01.039","journal-title":"Drug Discov Today"},{"issue":"8","key":"407_CR10","doi-asserted-by":"publisher","first-page":"785","DOI":"10.1080\/17460441.2016.1201262","volume":"11","author":"II Baskin","year":"2016","unstructured":"Baskin II, Winkler D, Tetko IV (2016) A renaissance of neural networks in drug discovery. Expert Opin Drug Discov 11(8):785\u2013795. https:\/\/doi.org\/10.1080\/17460441.2016.1201262","journal-title":"Expert Opin Drug Discov"},{"key":"407_CR11","doi-asserted-by":"publisher","unstructured":"Gori M, Monfardini G, Scarselli F (2005) A new model for learning in graph domains. In: Proceedings. 2005 IEEE international joint conference on neural networks, vol. 2, pp 729\u2013734 https:\/\/doi.org\/10.1109\/ijcnn.2005.1555942","DOI":"10.1109\/ijcnn.2005.1555942"},{"issue":"3","key":"407_CR12","doi-asserted-by":"publisher","first-page":"498","DOI":"10.1109\/TNN.2008.2010350","volume":"20","author":"A Micheli","year":"2009","unstructured":"Micheli A (2009) Neural network for graphs: a contextual constructive approach. IEEE Trans Neural Netw 20(3):498\u2013511. https:\/\/doi.org\/10.1109\/TNN.2008.2010350","journal-title":"IEEE Trans Neural Netw"},{"issue":"1","key":"407_CR13","doi-asserted-by":"publisher","first-page":"61","DOI":"10.1109\/TNN.2008.2005605","volume":"20","author":"F Scarselli","year":"2009","unstructured":"Scarselli F, Gori M, Tsoi AC, Hagenbuchner M, Monfardini G (2009) The graph neural network model. IEEE Trans Neural Netw 20(1):61\u201380. https:\/\/doi.org\/10.1109\/TNN.2008.2005605","journal-title":"IEEE Trans Neural Netw"},{"key":"407_CR14","unstructured":"Bruna J, Zaremba W, Szlam A, LeCun Y (2013) Spectral networks and locally connected networks on graphs. ArXiv13126203 Cs"},{"key":"407_CR15","unstructured":"Lee JB, Rossi RA, Kim S, Ahmed NK, Koh E (2018) Attention models in graphs: a survey. ArXiv180707984 Cs"},{"key":"407_CR16","doi-asserted-by":"crossref","unstructured":"Cao S, Lu W, Xu Q (2016) Deep neural networks for learning graph representations. In: Thirtieth AAAI conference on artificial intelligence","DOI":"10.1609\/aaai.v30i1.10179"},{"key":"407_CR17","doi-asserted-by":"publisher","unstructured":"Wang D, Cui P, Zhu W (2016) Structural deep network embedding. In: Proceedings of the 22Nd ACM SIGKDD international conference on knowledge discovery and data mining, New York, pp 1225\u20131234. https:\/\/doi.org\/10.1145\/2939672.2939753","DOI":"10.1145\/2939672.2939753"},{"key":"407_CR18","doi-asserted-by":"crossref","unstructured":"Pan S, Hu R, Long G, Jiang J, Yao L, Zhang C (2018) Adversarially regularized graph Autoencoder for graph embedding. ArXiv180204407 Cs Stat","DOI":"10.24963\/ijcai.2018\/362"},{"key":"407_CR19","doi-asserted-by":"publisher","unstructured":"Yu W, et al. (2018) Learning Deep Network Representations with Adversarially Regularized Autoencoders. In: Proceedings of the 24th ACM SIGKDD international conference on knowledge discovery & data mining, New York, pp 2663\u20132671. https:\/\/doi.org\/10.1145\/3219819.3220000","DOI":"10.1145\/3219819.3220000"},{"key":"407_CR20","unstructured":"Yan S, Xiong Y, Lin D. Spatial temporal graph convolutional networks for skeleton-based action recognition. In: Thirty-second AAAI conference on artificial intelligence"},{"key":"407_CR21","unstructured":"Li Y, Yu R, Shahabi C, Liu Y (2017) Diffusion convolutional recurrent neural network: data-driven traffic forecasting. ArXiv170701926 Cs Stat"},{"key":"407_CR22","unstructured":"Jain A, Zamir AR, Savarese S, Saxena A (2016) Structural-RNN: deep learning on spatio-temporal graphs presented at the Proceedings of the IEEE conference on computer vision and pattern recognition. pp 5308\u20135317"},{"key":"407_CR23","doi-asserted-by":"publisher","unstructured":"Yu B, Yin H, Zhu Z (2018) Spatio-temporal graph convolutional networks: a deep learning framework for traffic forecasting. In: Proc. Twenty-Seventh Int Jt Conf Artif Intell. pp 3634\u20133640. https:\/\/doi.org\/10.24963\/ijcai.2018\/505","DOI":"10.24963\/ijcai.2018\/505"},{"key":"407_CR24","first-page":"2224","volume-title":"Advances in neural information processing systems","author":"DK Duvenaud","year":"2015","unstructured":"Duvenaud DK et al (2015) Convolutional networks on graphs for learning molecular fingerprints. In: Cortes C, Lawrence ND, Lee DD, Sugiyama M, Garnett R (eds) Advances in neural information processing systems. Curran Associates Inc, New York, pp 2224\u20132232"},{"issue":"8","key":"407_CR25","doi-asserted-by":"publisher","first-page":"595","DOI":"10.1007\/s10822-016-9938-8","volume":"30","author":"S Kearnes","year":"2016","unstructured":"Kearnes S, McCloskey K, Berndl M, Pande V, Riley P (2016) Molecular graph convolutions: moving beyond fingerprints. J Comput Aided Mol Des 30(8):595\u2013608. https:\/\/doi.org\/10.1007\/s10822-016-9938-8","journal-title":"J Comput Aided Mol Des"},{"issue":"8","key":"407_CR26","doi-asserted-by":"publisher","first-page":"1757","DOI":"10.1021\/acs.jcim.6b00601","volume":"57","author":"CW Coley","year":"2017","unstructured":"Coley CW, Barzilay R, Green WH, Jaakkola TS, Jensen KF (2017) Convolutional embedding of attributed molecular graphs for physical property prediction. Journal of chemical information and modeling. 57(8):1757\u20131772. https:\/\/doi.org\/10.1021\/acs.jcim.6b00601","journal-title":"Journal of chemical information and modeling."},{"key":"407_CR27","unstructured":"Gilmer J, Schoenholz SS, Riley PF, Vinyals O, Dahl GE (2017) Neural Message Passing for Quantum Chemistry. ArXiv170401212 Cs"},{"key":"407_CR28","unstructured":"Li Y, Tarlow D, Brockschmidt M, Zemel R (2015) Gated Graph Sequence Neural Networks. ArXiv151105493 Cs Stat"},{"key":"407_CR29","unstructured":"Kipf TN, Welling M (2016) Semi-supervised classification with graph convolutional networks. ArXiv160902907 Cs Stat"},{"issue":"2","key":"407_CR30","doi-asserted-by":"publisher","first-page":"513","DOI":"10.1039\/C7SC02664A","volume":"9","author":"Z Wu","year":"2018","unstructured":"Wu Z et al (2018) MoleculeNet: a benchmark for molecular machine learning. Chem Sci 9(2):513\u2013530. https:\/\/doi.org\/10.1039\/C7SC02664A","journal-title":"Chem Sci"},{"issue":"9\u201310","key":"407_CR31","doi-asserted-by":"publisher","first-page":"625","DOI":"10.1007\/s10822-005-9020-4","volume":"19","author":"C Bologa","year":"2005","unstructured":"Bologa C, Allu TK, Olah M, Kappler MA, Oprea TI (2005) Descriptor collision and confusion: toward the design of descriptors to mask chemical structures. J Comput Aided Mol Des 19(9\u201310):625\u2013635. https:\/\/doi.org\/10.1007\/s10822-005-9020-4","journal-title":"J Comput Aided Mol Des"},{"issue":"9\u201310","key":"407_CR32","doi-asserted-by":"publisher","first-page":"705","DOI":"10.1007\/s10822-005-9014-2","volume":"19","author":"D Filimonov","year":"2005","unstructured":"Filimonov D, Poroikov V (2005) Why relevant chemical information cannot be exchanged without disclosing structures. J Comput Aided Mol Des 19(9\u201310):705\u2013713. https:\/\/doi.org\/10.1007\/s10822-005-9014-2","journal-title":"J Comput Aided Mol Des"},{"issue":"9\u201310","key":"407_CR33","doi-asserted-by":"publisher","first-page":"749","DOI":"10.1007\/s10822-005-9013-3","volume":"19","author":"IV Tetko","year":"2005","unstructured":"Tetko IV, Abagyan R, Oprea TI (2005) Surrogate data\u2014a secure way to share corporate data. J Comput Aided Mol Des 19(9\u201310):749\u2013764. https:\/\/doi.org\/10.1007\/s10822-005-9013-3","journal-title":"J Comput Aided Mol Des"},{"key":"407_CR34","doi-asserted-by":"publisher","first-page":"752","DOI":"10.1007\/978-3-030-30493-5_69","volume-title":"Artificial Neural Networks and Machine Learning \u2013 ICANN 2019: Workshop and Special Sessions","author":"Michael Withnall","year":"2019","unstructured":"Withnall M, Lindel\u00f6f E, Engkvist O, Chen H (2019) Attention and edge memory convolution for bioactivity prediction. In: Artificial neural networks and machine learning\u2014ICANN 2019: Workshop and Special Sessions. Springer, Cham. pp 752\u2013757. https:\/\/doi.org\/10.1007\/978-3-030-30493-5_69"},{"key":"407_CR35","doi-asserted-by":"crossref","unstructured":"Yang K, et al (2019) Are learned molecular representations ready for prime time?,\u201d ArXiv190401561 Cs Stat","DOI":"10.26434\/chemrxiv.7940594.v2"},{"key":"407_CR36","unstructured":"Lindel\u00f6f (2019) Deep Learning for Drug Discovery, Property Prediction with Neural Networks on Raw Molecular Graphs,\u201d Masters Thesis, Chalmers"},{"key":"407_CR37","first-page":"3844","volume-title":"Advances in neural information processing systems","author":"M Defferrard","year":"2016","unstructured":"Defferrard M, Bresson X, Vandergheynst P (2016) Convolutional neural networks on graphs with fast localized spectral filtering. In: Lee DD, Sugiyama M, Luxburg UV, Guyon I, Garnett R (eds) Advances in neural information processing systems. Curran Associates Inc, New York, pp 3844\u20133852"},{"key":"407_CR38","doi-asserted-by":"publisher","first-page":"13890","DOI":"10.1038\/ncomms13890","volume":"8","author":"KT Sch\u00fctt","year":"2017","unstructured":"Sch\u00fctt KT, Arbabzadah F, Chmiela S, M\u00fcller KR, Tkatchenko A (2017) Quantum-chemical insights from deep tensor neural networks. Nat Commun 8:13890. https:\/\/doi.org\/10.1038\/ncomms13890","journal-title":"Nat Commun"},{"key":"407_CR39","first-page":"971","volume-title":"Advances in neural information processing systems","author":"G Klambauer","year":"2017","unstructured":"Klambauer G, Unterthiner T, Mayr A, Hochreiter S (2017) Self-normalizing neural networks. In: Guyon I, Luxburg UV, Bengio S, Wallach H, Fergus R, Vishwanathan S, Garnett R (eds) Advances in neural information processing systems. Curran Associates Inc, New York, pp 971\u2013980"},{"key":"407_CR40","unstructured":"\u201cDeepchem\/contrib\/mpnn at master deepchem\/deepchem GitHub.\u201d https:\/\/github.com\/deepchem\/deepchem\/tree\/master\/contrib\/mpnn. Accessed 12 Aug 2019"},{"key":"407_CR41","unstructured":"Veli\u010dkovi\u0107 P, Cucurull G, Casanova A, Romero A, Lio P, Bengio Y (2017) Graph attention networks. ArXiv171010903 Cs Stat"},{"key":"407_CR42","unstructured":"Rezatofighi SH, et al.(2018) Deep Perm-Set Net: Learn to predict sets with unknown permutation and cardinality using deep neural networks. ArXiv180500613 Cs"},{"key":"407_CR43","unstructured":"Zaheer M, Kottur S, Ravanbakhsh S, Poczos B, Salakhutdinov R, Smola A (2017) Deep Sets. ArXiv170306114 Cs Stat"},{"key":"407_CR44","unstructured":"Ilse M, Tomczak JM, Welling M (2018) Attention-based deep multiple instance learning. ArXiv180204712 Cs Stat"},{"key":"407_CR45","unstructured":"Liu Y, Sun C, Lin L, Wang X (2016) Learning natural language inference using bidirectional LSTM model and inner-attention. ArXiv160509090 Cs"},{"key":"407_CR46","doi-asserted-by":"crossref","unstructured":"Fu J, Zheng H, Mei T (2017) Look closer to see better: recurrent attention convolutional neural network for fine-grained image recognition. In: Proceedings of the IEEE conference on computer vision and pattern recognition. p. 4438\u20134446","DOI":"10.1109\/CVPR.2017.476"},{"key":"407_CR47","unstructured":"Kimber TB, Engelke S, Tetko IV, Bruno E, Godin G (2018) Synergy effect between convolutional neural networks and the multiplicity of SMILES for improvement of molecular prediction. ArXiv181204439 Cs Stat"},{"key":"407_CR48","unstructured":"Paszke A, et al. (2017) Automatic differentiation in PyTorch"},{"key":"407_CR49","unstructured":"Gonz\u00e1lez J, Dai Z, Hennig P, Lawrence N (2015) Batch Bayesian optimization via local penalization. ArXiv150508052 Stat"},{"key":"407_CR50","unstructured":"Gonz\u00e1lez J (2016) Gpyopt: A bayesian optimization framework in python"},{"issue":"15","key":"407_CR51","doi-asserted-by":"publisher","first-page":"2887","DOI":"10.1021\/jm9602928","volume":"39","author":"GW Bemis","year":"1996","unstructured":"Bemis GW, Murcko MA (1996) The properties of known drugs. 1. Molecular frameworks. J Med Chem 39(15):2887\u20132893. https:\/\/doi.org\/10.1021\/jm9602928","journal-title":"J Med Chem"},{"issue":"8","key":"407_CR52","doi-asserted-by":"publisher","first-page":"084111","DOI":"10.1063\/1.4928757","volume":"143","author":"R Ramakrishnan","year":"2015","unstructured":"Ramakrishnan R, Hartmann M, Tapavicza E, von Lilienfeld OA (2015) Electronic spectra from TDDFT and machine learning in chemical space. J Chem Phys 143(8):084111. https:\/\/doi.org\/10.1063\/1.4928757","journal-title":"J Chem Phys"},{"issue":"3","key":"407_CR53","doi-asserted-by":"publisher","first-page":"1000","DOI":"10.1021\/ci034243x","volume":"44","author":"JS Delaney","year":"2004","unstructured":"Delaney JS (2004) ESOL: estimating aqueous solubility directly from molecular structure. J Chem Inf Comput Sci 44(3):1000\u20131005. https:\/\/doi.org\/10.1021\/ci034243x","journal-title":"J Chem Inf Comput Sci"},{"issue":"2","key":"407_CR54","doi-asserted-by":"publisher","first-page":"169","DOI":"10.1021\/ci8002649","volume":"49","author":"SG Rohrer","year":"2009","unstructured":"Rohrer SG, Baumann K (2009) Maximum unbiased validation (MUV) data sets for virtual screening based on PubChem bioactivity data. J Chem Inf Model 49(2):169\u2013184. https:\/\/doi.org\/10.1021\/ci8002649","journal-title":"J Chem Inf Model"},{"key":"407_CR55","unstructured":"AIDS Antiviral Screen Data - NCI DTP Data - National Cancer Institute - Confluence Wiki. https:\/\/wiki.nci.nih.gov\/display\/NCIDTPdata\/AIDS+Antiviral+Screen+Data. Accessed 10 July 2019"},{"issue":"6","key":"407_CR56","doi-asserted-by":"publisher","first-page":"1686","DOI":"10.1021\/ci300124c","volume":"52","author":"IF Martins","year":"2012","unstructured":"Martins IF, Teixeira AL, Pinheiro L, Falcao AO (2012) A Bayesian approach to in silico blood-brain barrier penetration modeling. J Chem Inf Model 52(6):1686\u20131697. https:\/\/doi.org\/10.1021\/ci300124c","journal-title":"J Chem Inf Model"},{"key":"407_CR57","unstructured":"\u201cTox21.\u201d https:\/\/tripod.nih.gov\/tox21\/challenge\/index.jsp. Accessed 10 July 2019"},{"issue":"D1","key":"407_CR58","doi-asserted-by":"publisher","first-page":"D1075","DOI":"10.1093\/nar\/gkv1075","volume":"44","author":"M Kuhn","year":"2016","unstructured":"Kuhn M, Letunic I, Jensen LJ, Bork P (2016) The SIDER database of drugs and side effects. Nucleic Acids Res 44(D1):D1075\u2013D1079. https:\/\/doi.org\/10.1093\/nar\/gkv1075","journal-title":"Nucleic Acids Res"},{"key":"407_CR59","unstructured":"\u201cMedDRA |.\u201d https:\/\/www.meddra.org\/. Accessed 10 July 2019"},{"issue":"4","key":"407_CR60","doi-asserted-by":"publisher","first-page":"283","DOI":"10.1021\/acscentsci.6b00367","volume":"3","author":"H Altae-Tran","year":"2017","unstructured":"Altae-Tran H, Ramsundar B, Pappu AS, Pande V (2017) Low data drug discovery with one-shot learning. ACS Cent Sci 3(4):283\u2013293. https:\/\/doi.org\/10.1021\/acscentsci.6b00367","journal-title":"ACS Cent Sci"},{"key":"407_CR61","unstructured":"Swain M (2018) MolVS: molecule validation and standardization"},{"issue":"7","key":"407_CR62","doi-asserted-by":"publisher","first-page":"1722","DOI":"10.1182\/blood.V78.7.1722.1722","volume":"78","author":"F Ishida","year":"1991","unstructured":"Ishida F, Saji H, Maruya E, Furihata K (1991) Human platelet-specific antigen, Siba, is associated with the molecular weight polymorphism of glycoprotein Ib alpha. Blood 78(7):1722\u20131729","journal-title":"Blood"},{"issue":"4","key":"407_CR63","doi-asserted-by":"publisher","first-page":"e1800108","DOI":"10.1002\/minf.201800108","volume":"38","author":"S Sosnin","year":"2019","unstructured":"Sosnin S, Vashurina M, Withnall M, Karpov P, Fedorov M, Tetko IV (2019) A survey of multi-task learning methods in chemoinformatics. Mol Inform 38(4):e1800108. https:\/\/doi.org\/10.1002\/minf.201800108","journal-title":"Mol Inform"},{"issue":"3","key":"407_CR64","doi-asserted-by":"publisher","first-page":"355","DOI":"10.1016\/S0169-409X(02)00008-X","volume":"54","author":"WL Jorgensen","year":"2002","unstructured":"Jorgensen WL, Duffy EM (2002) Prediction of drug solubility from structure. Adv Drug Deliv Rev 54(3):355\u2013366. https:\/\/doi.org\/10.1016\/S0169-409X(02)00008-X","journal-title":"Adv Drug Deliv Rev"},{"issue":"8","key":"407_CR65","doi-asserted-by":"publisher","first-page":"2962","DOI":"10.1021\/mp500103r","volume":"11","author":"DS Palmer","year":"2014","unstructured":"Palmer DS, Mitchell JBO (2014) Is experimental data quality the limiting factor in predicting the aqueous solubility of druglike molecules? Mol Pharm 11(8):2962\u20132972. https:\/\/doi.org\/10.1021\/mp500103r","journal-title":"Mol Pharm"},{"key":"407_CR66","doi-asserted-by":"publisher","unstructured":"Chen C, Hou J, Shi X, Yang H, Birchler JA, Cheng J (2019) Interpretable attention model in transcription factor binding site prediction with deep neural networks. bioRxiv, p 648691. https:\/\/doi.org\/10.1101\/648691","DOI":"10.1101\/648691"}],"container-title":["Journal of Cheminformatics"],"original-title":[],"language":"en","link":[{"URL":"http:\/\/link.springer.com\/content\/pdf\/10.1186\/s13321-019-0407-y.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"http:\/\/link.springer.com\/article\/10.1186\/s13321-019-0407-y\/fulltext.html","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"http:\/\/link.springer.com\/content\/pdf\/10.1186\/s13321-019-0407-y.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2022,10,10]],"date-time":"2022-10-10T07:18:42Z","timestamp":1665386322000},"score":1,"resource":{"primary":{"URL":"https:\/\/jcheminf.biomedcentral.com\/articles\/10.1186\/s13321-019-0407-y"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2020,1,8]]},"references-count":66,"journal-issue":{"issue":"1","published-print":{"date-parts":[[2020,12]]}},"alternative-id":["407"],"URL":"https:\/\/doi.org\/10.1186\/s13321-019-0407-y","relation":{"has-preprint":[{"id-type":"doi","id":"10.26434\/chemrxiv.9873599.v2","asserted-by":"object"}]},"ISSN":["1758-2946"],"issn-type":[{"value":"1758-2946","type":"electronic"}],"subject":[],"published":{"date-parts":[[2020,1,8]]},"assertion":[{"value":"17 September 2019","order":1,"name":"received","label":"Received","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"25 December 2019","order":2,"name":"accepted","label":"Accepted","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"8 January 2020","order":3,"name":"first_online","label":"First Online","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"The authors declare that they have no competing interests.","order":1,"name":"Ethics","group":{"name":"EthicsHeading","label":"Competing interests"}}],"article-number":"1"}}