{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,4,14]],"date-time":"2026-04-14T05:56:42Z","timestamp":1776146202284,"version":"3.50.1"},"reference-count":69,"publisher":"Springer Science and Business Media LLC","issue":"1","license":[{"start":{"date-parts":[[2021,11,13]],"date-time":"2021-11-13T00:00:00Z","timestamp":1636761600000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0"},{"start":{"date-parts":[[2021,11,13]],"date-time":"2021-11-13T00:00:00Z","timestamp":1636761600000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0"}],"funder":[{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"publisher","award":["61772566"],"award-info":[{"award-number":["61772566"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]},{"name":"Guangzhou S&T Research Plan","award":["202007030010"],"award-info":[{"award-number":["202007030010"]}]}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":["J Cheminform"],"abstract":"<jats:title>Abstract<\/jats:title>\n                  <jats:p>\n                    Scaffold hopping is a central task of modern medicinal chemistry for rational drug design, which aims to design molecules of novel scaffolds sharing similar target biological activities toward known hit molecules. Traditionally, scaffolding hopping depends on searching databases of available compounds that can't exploit vast chemical space. In this study, we have re-formulated this task as a\n                    <jats:italic>supervised molecule-to-molecule translation<\/jats:italic>\n                    to generate hopped molecules novel in 2D structure but similar in 3D structure, as inspired by the fact that candidate compounds bind with their targets through 3D conformations. To efficiently train the model, we curated over 50 thousand pairs of molecules with increased bioactivity, similar 3D structure, but different 2D structure from public bioactivity database, which spanned 40 kinases commonly investigated by medicinal chemists. Moreover, we have designed a multimodal molecular transformer architecture by integrating molecular 3D conformer through a spatial graph neural network and protein sequence information through Transformer. The trained DeepHop model was shown able to generate around 70% molecules having improved bioactivity together with high 3D similarity but low 2D scaffold similarity to the template molecules. This ratio was 1.9 times higher than other state-of-the-art deep learning methods and rule- and virtual screening-based methods. Furthermore, we demonstrated that the model could generalize to new target proteins through fine-tuning with a small set of active compounds. Case studies have also shown the advantages and usefulness of DeepHop in practical scaffold hopping scenarios.\n                  <\/jats:p>","DOI":"10.1186\/s13321-021-00565-5","type":"journal-article","created":{"date-parts":[[2021,11,13]],"date-time":"2021-11-13T07:02:59Z","timestamp":1636786979000},"update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":60,"title":["Deep scaffold hopping with multimodal transformer neural networks"],"prefix":"10.1186","volume":"13","author":[{"given":"Shuangjia","family":"Zheng","sequence":"first","affiliation":[]},{"given":"Zengrong","family":"Lei","sequence":"additional","affiliation":[]},{"given":"Haitao","family":"Ai","sequence":"additional","affiliation":[]},{"given":"Hongming","family":"Chen","sequence":"additional","affiliation":[]},{"given":"Daiguo","family":"Deng","sequence":"additional","affiliation":[]},{"ORCID":"https:\/\/orcid.org\/0000-0002-6782-2813","authenticated-orcid":false,"given":"Yuedong","family":"Yang","sequence":"additional","affiliation":[]}],"member":"297","published-online":{"date-parts":[[2021,11,13]]},"reference":[{"issue":"4","key":"565_CR1","first-page":"351","volume":"13","author":"DJ Ecker","year":"1995","unstructured":"Ecker DJ, Crooke ST (1995) Combinatorial drug discovery: which methods will produce the greatest value? Biotechnology (N Y) 13(4):351\u2013360","journal-title":"Biotechnology (N Y)"},{"issue":"4","key":"565_CR2","doi-asserted-by":"publisher","first-page":"217","DOI":"10.2165\/00126839-200809040-00002","volume":"9","author":"D Fattori","year":"2008","unstructured":"Fattori D, Squarcia A, Bartoli S (2008) Fragment-based approach to drug lead discovery: overview and advances in various techniques. Drugs R D 9(4):217\u2013227","journal-title":"Drugs R D"},{"issue":"19","key":"565_CR3","doi-asserted-by":"publisher","first-page":"2894","DOI":"10.1002\/(SICI)1521-3773(19991004)38:19<2894::AID-ANIE2894>3.0.CO;2-F","volume":"38","author":"G Schneider","year":"1999","unstructured":"Schneider G, Neidhart W, Giller T, Schmid G (1999) \u201cScaffold-Hopping\u201d by topological pharmacophore search: a contribution to virtual screening. Angew Chem Int Ed Engl 38(19):2894\u20132896","journal-title":"Angew Chem Int Ed Engl"},{"issue":"4","key":"565_CR4","doi-asserted-by":"publisher","first-page":"1238","DOI":"10.1021\/acs.jmedchem.6b01437","volume":"60","author":"Y Hu","year":"2017","unstructured":"Hu Y, Stumpfe D, Bajorath J (2017) Recent Advances in Scaffold Hopping. J Med Chem 60(4):1238\u20131246","journal-title":"J Med Chem"},{"issue":"2","key":"565_CR5","doi-asserted-by":"publisher","first-page":"134","DOI":"10.1038\/s42256-020-0152-y","volume":"2","author":"S Zheng","year":"2020","unstructured":"Zheng S, Li Y, Chen S, Xu J, Yang Y (2020) Predicting drug\u2013protein interaction using quasi-visual question answering system. Na Mach Intell 2(2):134\u2013140","journal-title":"Na Mach Intell"},{"issue":"5","key":"565_CR6","doi-asserted-by":"publisher","first-page":"1489","DOI":"10.1021\/jm040163o","volume":"48","author":"TS Rush","year":"2005","unstructured":"Rush TS, Grant JA, Mosyak L, Nicholls A (2005) A shape-based 3-D scaffold hopping method and its application to a bacterial protein-protein interaction. J Med Chem 48(5):1489\u20131495","journal-title":"J Med Chem"},{"issue":"20","key":"565_CR7","doi-asserted-by":"publisher","first-page":"7011","DOI":"10.1016\/j.bmc.2006.06.024","volume":"14","author":"KD Stewart","year":"2006","unstructured":"Stewart KD, Shiroda M, James CA (2006) Drug Guru: a computer software program for drug design using medicinal chemistry rules. Bioorg Med Chem 14(20):7011\u20137022","journal-title":"Bioorg Med Chem"},{"issue":"9","key":"565_CR8","doi-asserted-by":"publisher","first-page":"4062","DOI":"10.1021\/acs.jmedchem.5b01746","volume":"59","author":"Y Hu","year":"2016","unstructured":"Hu Y, Stumpfe D, Bajorath J (2016) Computational exploration of molecular scaffolds in medicinal chemistry. J Med Chem 59(9):4062\u20134076","journal-title":"J Med Chem"},{"issue":"7\u20138","key":"565_CR9","doi-asserted-by":"publisher","first-page":"310","DOI":"10.1016\/j.drudis.2011.10.024","volume":"17","author":"H Sun","year":"2012","unstructured":"Sun H, Tawa G, Wallqvist A (2012) Classification of scaffold-hopping approaches. Drug Discov Today 17(7\u20138):310\u2013324","journal-title":"Drug Discov Today"},{"issue":"4","key":"565_CR10","doi-asserted-by":"publisher","first-page":"2073","DOI":"10.1021\/acs.jcim.0c00098","volume":"60","author":"H Nakano","year":"2020","unstructured":"Nakano H, Miyao T, Funatsu K (2020) Exploring topological pharmacophore graphs for scaffold hopping. J Chem Inf Model 60(4):2073\u20132081","journal-title":"J Chem Inf Model"},{"issue":"1","key":"565_CR11","doi-asserted-by":"publisher","first-page":"54","DOI":"10.1186\/s13321-019-0376-1","volume":"11","author":"O Laufkotter","year":"2019","unstructured":"Laufkotter O, Sturm N, Bajorath J, Chen H, Engkvist O (2019) Combining structural and bioactivity-based fingerprints improves prediction performance and scaffold hopping capability. J Cheminform 11(1):54","journal-title":"J Cheminform"},{"issue":"2","key":"565_CR12","doi-asserted-by":"publisher","first-page":"181","DOI":"10.1002\/cmdc.200500005","volume":"1","author":"S Renner","year":"2006","unstructured":"Renner S, Schneider G (2006) Scaffold-hopping potential of ligand-based similarity concepts. ChemMedChem 1(2):181\u2013185","journal-title":"ChemMedChem"},{"issue":"1","key":"565_CR13","doi-asserted-by":"publisher","first-page":"16469","DOI":"10.1038\/s41598-018-34677-0","volume":"8","author":"F Grisoni","year":"2018","unstructured":"Grisoni F, Merk D, Byrne R, Schneider G (2018) Scaffold-hopping from synthetic drugs by holistic molecular representation. Sci Rep 8(1):16469","journal-title":"Sci Rep"},{"issue":"2","key":"565_CR14","doi-asserted-by":"publisher","first-page":"133","DOI":"10.1002\/minf.201200141","volume":"32","author":"M Reutlinger","year":"2013","unstructured":"Reutlinger M, Koch CP, Reker D, Todoroff N, Schneider P, Rodrigues T, Schneider G (2013) Chemically Advanced Template Search (CATS) for scaffold-hopping and prospective target prediction for \u201corphan\u201d molecules. Mol Inform 32(2):133\u2013138","journal-title":"Mol Inform"},{"issue":"13","key":"565_CR15","doi-asserted-by":"publisher","first-page":"1336","DOI":"10.1002\/cmdc.201800176","volume":"13","author":"G Floresta","year":"2018","unstructured":"Floresta G, Amata E, Dichiara M, Marrazzo A, Salerno L, Romeo G, Prezzavento O, Pittala V, Rescifina A (2018) Identification of potentially potent heme oxygenase 1 inhibitors through 3D-QSAR coupled to scaffold-hopping analysis. ChemMedChem 13(13):1336\u20131342","journal-title":"ChemMedChem"},{"issue":"10","key":"565_CR16","doi-asserted-by":"publisher","first-page":"e45964","DOI":"10.1371\/journal.pone.0045964","volume":"7","author":"G Saluste","year":"2012","unstructured":"Saluste G, Albarran MI, Alvarez RM, Rabal O, Ortega MA, Blanco C, Kurz G, Salgado A, Pevarello P, Bischoff JR, Pastor J, Oyarzabal J (2012) Fragment-hopping-based discovery of a novel chemical series of proto-oncogene PIM-1 kinase inhibitors. PLoS ONE 7(10):e45964","journal-title":"PLoS ONE"},{"issue":"1","key":"565_CR17","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1016\/S1093-3263(99)00015-7","volume":"17","author":"FL Stahura","year":"1999","unstructured":"Stahura FL, Xue L, Godden JW, Bajorath J (1999) Molecular scaffold-based design and comparison of combinatorial libraries focused on the ATP-binding site of protein kinases. J Mol Graph Model 17(1):1\u20139","journal-title":"J Mol Graph Model"},{"issue":"7","key":"565_CR18","doi-asserted-by":"publisher","first-page":"1825","DOI":"10.1021\/ci4001019","volume":"53","author":"MJ Vainio","year":"2013","unstructured":"Vainio MJ, Kogej T, Raubacher F, Sadowski J (2013) Scaffold hopping by fragment replacement. J Chem Inf Model 53(7):1825\u20131835","journal-title":"J Chem Inf Model"},{"issue":"1","key":"565_CR19","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1021\/ci500542e","volume":"55","author":"O Rabal","year":"2015","unstructured":"Rabal O, Amr FI, Oyarzabal J (2015) Novel Scaffold FingerPrint (SFP): applications in scaffold hopping and scaffold-based selection of diverse compounds. J Chem Inf Model 55(1):1\u201318","journal-title":"J Chem Inf Model"},{"issue":"9","key":"565_CR20","doi-asserted-by":"publisher","first-page":"2952","DOI":"10.1021\/jm801513z","volume":"52","author":"WR Pitt","year":"2009","unstructured":"Pitt WR, Parry DM, Perry BG, Groom CR (2009) Heteroaromatic rings of the future. J Med Chem 52(9):2952\u20132963","journal-title":"J Med Chem"},{"issue":"10","key":"565_CR21","doi-asserted-by":"publisher","first-page":"4629","DOI":"10.1021\/acs.jcim.0c00622","volume":"60","author":"L Stojanovic","year":"2020","unstructured":"Stojanovic L, Popovic M, Tijanic N, Rakocevic G, Kalinic M (2020) Improved Scaffold Hopping in Ligand-Based Virtual Screening Using Neural Representation Learning. J Chem Inf Model 60(10):4629\u20134639","journal-title":"J Chem Inf Model"},{"issue":"11","key":"565_CR22","doi-asserted-by":"publisher","first-page":"2864","DOI":"10.1021\/ci300415d","volume":"52","author":"L Ruddigkeit","year":"2012","unstructured":"Ruddigkeit L, van Deursen R, Blum LC, Reymond JL (2012) Enumeration of 166 billion organic small molecules in the chemical universe database GDB-17. J Chem Inf Model 52(11):2864\u20132875","journal-title":"J Chem Inf Model"},{"issue":"6","key":"565_CR23","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","journal-title":"Drug Discov Today"},{"issue":"6","key":"565_CR24","doi-asserted-by":"publisher","first-page":"567","DOI":"10.4155\/fmc-2018-0358","volume":"11","author":"Y Xu","year":"2019","unstructured":"Xu Y, Lin K, Wang S, Wang L, Cai C, Song C, Lai L, Pei J (2019) Deep learning for molecular generation. Future Med Chem 11(6):567\u2013597","journal-title":"Future Med Chem"},{"issue":"3","key":"565_CR25","doi-asserted-by":"publisher","first-page":"1045","DOI":"10.21437\/Interspeech.2010-343","volume":"2","author":"T Mikolov","year":"2010","unstructured":"Mikolov, T.; Karafi\u00e1t, M.; Burget, L.; \u010cernock\u00fd, J.; Khudanpur, S. (2010) Recurrent neural network based language model. Interspeech 2(3):1045-1048","journal-title":"Interspeech"},{"issue":"1","key":"565_CR26","doi-asserted-by":"publisher","first-page":"120","DOI":"10.1021\/acscentsci.7b00512","volume":"4","author":"MHS Segler","year":"2018","unstructured":"Segler MHS, Kogej T, Tyrchan C, Waller MP (2018) Generating Focused Molecule Libraries for Drug Discovery with Recurrent Neural Networks. ACS Cent Sci 4(1):120\u2013131","journal-title":"ACS Cent Sci"},{"issue":"1","key":"565_CR27","doi-asserted-by":"publisher","first-page":"5","DOI":"10.1186\/s13321-019-0328-9","volume":"11","author":"S Zheng","year":"2019","unstructured":"Zheng S, Yan X, Gu Q, Yang Y, Du Y, Lu Y, Xu J (2019) QBMG: quasi-biogenic molecule generator with deep recurrent neural network. J Cheminform 11(1):5","journal-title":"J Cheminform"},{"issue":"2","key":"565_CR28","doi-asserted-by":"publisher","first-page":"268","DOI":"10.1021\/acscentsci.7b00572","volume":"4","author":"R Gomez-Bombarelli","year":"2018","unstructured":"Gomez-Bombarelli R, Wei JN, Duvenaud D, Hernandez-Lobato JM, Sanchez-Lengeling B, Sheberla D, Aguilera-Iparraguirre J, Hirzel TD, Adams RP, Aspuru-Guzik A (2018) Automatic chemical design using a data-driven continuous representation of molecules. ACS Cent Sci 4(2):268\u2013276","journal-title":"ACS Cent Sci"},{"issue":"3","key":"565_CR29","doi-asserted-by":"publisher","first-page":"1205","DOI":"10.1021\/acs.jcim.8b00706","volume":"59","author":"M Skalic","year":"2019","unstructured":"Skalic M, Jimenez J, Sabbadin D, De Fabritiis G (2019) Shape-based generative modeling for de novo drug design. J Chem Inf Model 59(3):1205\u20131214","journal-title":"J Chem Inf Model"},{"key":"565_CR30","unstructured":"De Cao, N.; Kipf, T., MolGAN: An implicit generative model for small molecular graphs. arXiv preprint arXiv:1805.11973 2018."},{"issue":"1","key":"565_CR31","doi-asserted-by":"publisher","first-page":"31","DOI":"10.1021\/ci00057a005","volume":"28","author":"D Weininger","year":"1988","unstructured":"Weininger D (1988) SMILES, a chemical language and information system. 1. Introduction to methodology and encoding rules. J Chem Inform Comput Sci 28(1):31\u201336","journal-title":"J Chem Inform Comput Sci"},{"issue":"1","key":"565_CR32","doi-asserted-by":"publisher","first-page":"33","DOI":"10.1186\/s13321-018-0287-6","volume":"10","author":"Y Li","year":"2018","unstructured":"Li Y, Zhang L, Liu Z (2018) Multi-objective de novo drug design with conditional graph generative model. J Cheminform 10(1):33","journal-title":"J Cheminform"},{"issue":"1","key":"565_CR33","doi-asserted-by":"publisher","first-page":"22104","DOI":"10.1038\/s41598-020-78537-2","volume":"10","author":"W Jeon","year":"2020","unstructured":"Jeon W, Kim D (2020) Autonomous molecule generation using reinforcement learning and docking to develop potential novel inhibitors. Sci Rep 10(1):22104","journal-title":"Sci Rep"},{"issue":"1","key":"565_CR34","doi-asserted-by":"publisher","first-page":"39","DOI":"10.1186\/s13321-021-00516-0","volume":"13","author":"M Thomas","year":"2021","unstructured":"Thomas M, Smith RT, O\u2019Boyle NM, de Graaf C, Bender A (2021) Comparison of structure- and ligand-based scoring functions for deep generative models: a GPCR case study. J Cheminform 13(1):39","journal-title":"J Cheminform"},{"issue":"7","key":"565_CR35","doi-asserted-by":"publisher","first-page":"3166","DOI":"10.1021\/acs.jcim.9b00325","volume":"59","author":"N Stahl","year":"2019","unstructured":"Stahl N, Falkman G, Karlsson A, Mathiason G, Bostrom J (2019) Deep reinforcement learning for multiparameter optimization in de novo drug design. J Chem Inf Model 59(7):3166\u20133176","journal-title":"J Chem Inf Model"},{"issue":"4","key":"565_CR36","doi-asserted-by":"publisher","first-page":"1153","DOI":"10.1039\/C9SC04503A","volume":"11","author":"J Lim","year":"2020","unstructured":"Lim J, Hwang S-Y, Moon S, Kim S, Kim WY (2020) Scaffold-based molecular design with a graph generative model. Chem Sci 11(4):1153\u20131164","journal-title":"Chem Sci"},{"issue":"1","key":"565_CR37","doi-asserted-by":"publisher","first-page":"77","DOI":"10.1021\/acs.jcim.9b00727","volume":"60","author":"Y Li","year":"2020","unstructured":"Li Y, Hu J, Wang Y, Zhou J, Zhang L, Liu Z (2020) DeepScaffold: a comprehensive tool for scaffold-based de novo drug discovery using deep learning. J Chem Inf Model 60(1):77\u201391","journal-title":"J Chem Inf Model"},{"issue":"1","key":"565_CR38","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1186\/s13321-020-00441-8","volume":"12","author":"J Ar\u00fas-Pous","year":"2020","unstructured":"Ar\u00fas-Pous J, Patronov A, Bjerrum EJ, Tyrchan C, Reymond J-L, Chen H, Engkvist O (2020) SMILES-based deep generative scaffold decorator for de-novo drug design. J Cheminformatics 12(1):1\u201318","journal-title":"J Cheminformatics"},{"issue":"4","key":"565_CR39","doi-asserted-by":"publisher","first-page":"1983","DOI":"10.1021\/acs.jcim.9b01120","volume":"60","author":"F Imrie","year":"2020","unstructured":"Imrie F, Bradley AR, van der Schaar M, Deane CM (2020) Deep generative models for 3D linker design. J Chem Inf Model 60(4):1983\u20131995","journal-title":"J Chem Inf Model"},{"issue":"31","key":"565_CR40","doi-asserted-by":"publisher","first-page":"8312","DOI":"10.1039\/D0SC03126G","volume":"11","author":"Y Yang","year":"2020","unstructured":"Yang Y, Zheng S, Su S, Zhao C, Xu J, Chen H (2020) SyntaLinker: automatic fragment linking with deep conditional transformer neural networks. Chem Sci 11(31):8312\u20138322","journal-title":"Chem Sci"},{"key":"565_CR41","doi-asserted-by":"publisher","first-page":"D1100","DOI":"10.1093\/nar\/gkr777","volume":"40","author":"A Gaulton","year":"2012","unstructured":"Gaulton A, Bellis LJ, Bento AP, Chambers J, Davies M, Hersey A, Light Y, McGlinchey S, Michalovich D, Al-Lazikani B, Overington JP (2012) ChEMBL: a large-scale bioactivity database for drug discovery. Nucleic Acids Res 40:D1100\u2013D1107","journal-title":"Nucleic Acids Res"},{"issue":"6","key":"565_CR42","doi-asserted-by":"publisher","first-page":"2103","DOI":"10.1021\/jm051201m","volume":"49","author":"NT Southall","year":"2006","unstructured":"Southall NT (2006) Ajay, Kinase patent space visualization using chemical replacements. J Med Chem 49(6):2103\u20132109","journal-title":"J Med Chem"},{"key":"565_CR43","first-page":"2831","volume":"2020","author":"NT Southall","year":"2020","unstructured":"Song, Y.; Zheng, S.; Niu, Z.; Fu, Z.-H.; Lu, Y.; Yang, Y. (2020) Communicative representation learning on attributed molecular graphs. IJCAI 2020:2831-2838","journal-title":"IJCAI"},{"issue":"16","key":"565_CR44","doi-asserted-by":"publisher","first-page":"8723","DOI":"10.1021\/acs.jmedchem.9b00855","volume":"63","author":"X Li","year":"2020","unstructured":"Li X, Li Z, Wu X, Xiong Z, Yang T, Fu Z, Liu X, Tan X, Zhong F, Wan X, Wang D, Ding X, Yang R, Hou H, Li C, Liu H, Chen K, Jiang H, Zheng M (2020) Deep learning enhancing kinome-wide polypharmacology profiling: model construction and experiment validation. J Med Chem 63(16):8723\u20138737","journal-title":"J Med Chem"},{"issue":"3","key":"565_CR45","doi-asserted-by":"publisher","first-page":"339","DOI":"10.1021\/ci900450m","volume":"50","author":"J Hussain","year":"2010","unstructured":"Hussain J, Rea C (2010) Computationally efficient algorithm to identify matched molecular pairs (MMPs) in large data sets. J Chem Inf Model 50(3):339\u2013348","journal-title":"J Chem Inf Model"},{"issue":"5","key":"565_CR46","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","journal-title":"J Chem Inf Model"},{"issue":"15","key":"565_CR47","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","journal-title":"J Med Chem"},{"issue":"12","key":"565_CR48","doi-asserted-by":"publisher","first-page":"751","DOI":"10.1007\/s10822-006-9085-8","volume":"20","author":"GA Landrum","year":"2006","unstructured":"Landrum GA, Penzotti JE, Putta S (2006) Feature-map vectors: a new class of informative descriptors for computational drug discovery. J Comput Aided Mol Des 20(12):751\u2013762","journal-title":"J Comput Aided Mol Des"},{"issue":"9","key":"565_CR49","doi-asserted-by":"publisher","first-page":"3313","DOI":"10.1021\/jm049066l","volume":"48","author":"S Putta","year":"2005","unstructured":"Putta S, Landrum GA, Penzotti JE (2005) Conformation mining: an algorithm for finding biologically relevant conformations. J Med Chem 48(9):3313\u20133318","journal-title":"J Med Chem"},{"issue":"13","key":"565_CR50","doi-asserted-by":"publisher","first-page":"1658","DOI":"10.1093\/bioinformatics\/btl158","volume":"22","author":"W Li","year":"2006","unstructured":"Li W, Godzik A (2006) Cd-hit: a fast program for clustering and comparing large sets of protein or nucleotide sequences. Bioinformatics 22(13):1658\u20131659","journal-title":"Bioinformatics"},{"key":"565_CR51","unstructured":"Vaswani, A.; Shazeer, N.; Parmar, N.; Uszkoreit, J.; Jones, L.; Gomez, A. N.; Kaiser, \u0141.; Polosukhin, I. In: Attention is all you need, Advances in neural information processing systems, 2017; pp 5998\u20136008."},{"key":"565_CR52","doi-asserted-by":"crossref","unstructured":"Wang, Q.; Li, B.; Xiao, T.; Zhu, J.; Li, C.; Wong, D. F.; Chao, L. S., Learning deep transformer models for machine translation. arXiv preprint arXiv:1906.01787 2019.","DOI":"10.18653\/v1\/P19-1176"},{"issue":"1","key":"565_CR53","doi-asserted-by":"publisher","first-page":"47","DOI":"10.1021\/acs.jcim.9b00949","volume":"60","author":"S Zheng","year":"2020","unstructured":"Zheng S, Rao J, Zhang Z, Xu J, Yang Y (2020) Predicting retrosynthetic reactions using self-corrected transformer neural networks. J Chem Inf Model 60(1):47\u201355","journal-title":"J Chem Inf Model"},{"key":"565_CR54","doi-asserted-by":"crossref","unstructured":"Danel, T.; Spurek, P.; Tabor, J.; \u015amieja, M.; Struski, \u0141.; S\u0142owik, A.; Maziarka, \u0141., Spatial Graph Convolutional Networks. arXiv preprint arXiv:1909.05310 2019.","DOI":"10.1007\/978-3-030-63823-8_76"},{"key":"565_CR55","unstructured":"Chung, J.; Gulcehre, C.; Cho, K.; Bengio, Y., Empirical evaluation of gated recurrent neural networks on sequence modeling. arXiv preprint arXiv:1412.3555 2014."},{"key":"565_CR56","doi-asserted-by":"crossref","unstructured":"Rao, R.; Bhattacharya, N.; Thomas, N.; Duan, Y.; Chen, P.; Canny, J.; Abbeel, P.; Song, Y. In Evaluating protein transfer learning with TAPE, Advances in Neural Information Processing Systems, 2019; pp 9689\u20139701.","DOI":"10.1101\/676825"},{"key":"565_CR57","unstructured":"Nair, Vinod, and Geoffrey E. Hinton. \"Rectified linear units improve restricted boltzmann machines.\" Icml. 2010."},{"key":"565_CR58","unstructured":"Ba, J.; Kiros, J. R.; Hinton, G. E., Layer Normalization. arXiv:1607.06450."},{"key":"565_CR59","doi-asserted-by":"crossref","unstructured":"Barrault, L.; Bojar, O. e.; Costa-juss\u00e0, M. R.; Federmann, C.; Fishel, M.; Graham, Y.; Haddow, B.; Huck, M.; Koehn, P.; Malmasi, S.; Monz, C.; M\u00fcller, M.; Pal, S.; Post, M.; Zampieri, M. In: Findings of the 2019 Conference on Machine Translation (WMT19), Proceedings of the Fourth Conference on Machine Translation (Volume 2: Shared Task Papers, Day 1), Florence, Italy, aug; Association for Computational Linguistics: Florence, Italy, 2019; pp 1\u201361.","DOI":"10.18653\/v1\/W19-5301"},{"key":"565_CR60","doi-asserted-by":"crossref","unstructured":"He, K.; Zhang, X.; Ren, S.; Sun, J., Deep Residual Learning for Image Recognition. CoRR 2015, abs\/1512.03385.","DOI":"10.1109\/CVPR.2016.90"},{"issue":"10","key":"565_CR61","doi-asserted-by":"publisher","first-page":"1103","DOI":"10.1021\/acscentsci.7b00303","volume":"3","author":"B Liu","year":"2017","unstructured":"Liu B, Ramsundar B, Kawthekar P, Shi J, Gomes J, Luu Nguyen Q, Ho S, Sloane J, Wender P, Pande V (2017) Retrosynthetic reaction prediction using neural sequence-to-sequence models. ACS Cent Sci 3(10):1103\u20131113","journal-title":"ACS Cent Sci"},{"key":"565_CR62","unstructured":"Jin, W.; Yang, K.; Barzilay, R.; Jaakkola, T., Learning Multimodal Graph-to-Graph Translation for Molecule Optimization. International Conference on Learning Representations. 2018."},{"key":"565_CR63","doi-asserted-by":"crossref","unstructured":"Klein, G.; Kim, Y.; Deng, Y.; Senellart, J.; Rush, A. M., OpenNMT: Open-source toolkit for neural machine translation. CoRR 2017, abs\/1701.02810.","DOI":"10.18653\/v1\/P17-4012"},{"key":"565_CR64","unstructured":"Python Core Team. Python: A dynamic, open source programming language. Python Software Foundation. https:\/\/www.python.org\/."},{"issue":"1","key":"565_CR65","doi-asserted-by":"publisher","first-page":"35","DOI":"10.1080\/00207548808947840","volume":"26","author":"PS Ow","year":"1988","unstructured":"Ow PS, Morton TE (1988) Filtered beam search in scheduling\u2020. Int J Prod Res 26(1):35\u201362","journal-title":"Int J Prod Res"},{"issue":"8","key":"565_CR66","doi-asserted-by":"publisher","first-page":"3370","DOI":"10.1021\/acs.jcim.9b00237","volume":"59","author":"K Yang","year":"2019","unstructured":"Yang K, Swanson K, Jin W, Coley C, Eiden P, Gao H, Guzman-Perez A, Hopper T, Kelley B, Mathea M (2019) Analyzing learned molecular representations for property prediction. J Chem Inf Model 59(8):3370\u20133388","journal-title":"J Chem Inf Model"},{"issue":"2","key":"565_CR67","doi-asserted-by":"crossref","first-page":"455","DOI":"10.1002\/jcc.21334","volume":"31","author":"O Trott","year":"2010","unstructured":"Trott O, Olson AJ (2010) AutoDock Vina: improving the speed and accuracy of docking with a new scoring function, efficient optimization, and multithreading. J Comput Chem 31(2):455\u2013461","journal-title":"J Comput Chem"},{"issue":"22","key":"565_CR68","doi-asserted-by":"publisher","first-page":"5580","DOI":"10.1016\/j.bmcl.2016.09.067","volume":"26","author":"RP Wurz","year":"2016","unstructured":"Wurz RP, Sastri C, D\u2019Amico DC, Herberich B, Jackson CLM, Pettus LH, Tasker AS, Wu B, Guerrero N, Lipford JR, Winston JT, Yang Y, Wang P, Nguyen Y, Andrews KL, Huang X, Lee MR, Mohr C, Zhang JD, Reid DL, Xu Y, Zhou Y, Wang HL (2016) Discovery of imidazopyridazines as potent Pim-1\/2 kinase inhibitors. Bioorg Med Chem Lett 26(22):5580\u20135590","journal-title":"Bioorg Med Chem Lett"},{"issue":"10","key":"565_CR69","doi-asserted-by":"publisher","first-page":"2507","DOI":"10.1021\/ci2003945","volume":"51","author":"R Li","year":"2011","unstructured":"Li R, Stumpfe D, Vogt M, Geppert H, Bajorath J (2011) Development of a method to consistently quantify the structural distance between scaffolds and to assess scaffold hopping potential. J Chem Inf Model 51(10):2507\u20132514","journal-title":"J Chem Inf Model"}],"container-title":["Journal of Cheminformatics"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1186\/s13321-021-00565-5.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/article\/10.1186\/s13321-021-00565-5\/fulltext.html","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1186\/s13321-021-00565-5.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2023,11,12]],"date-time":"2023-11-12T10:53:00Z","timestamp":1699786380000},"score":1,"resource":{"primary":{"URL":"https:\/\/jcheminf.biomedcentral.com\/articles\/10.1186\/s13321-021-00565-5"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2021,11,13]]},"references-count":69,"journal-issue":{"issue":"1","published-online":{"date-parts":[[2021,12]]}},"alternative-id":["565"],"URL":"https:\/\/doi.org\/10.1186\/s13321-021-00565-5","relation":{"has-preprint":[{"id-type":"doi","id":"10.26434\/chemrxiv.13011767.v1","asserted-by":"object"}]},"ISSN":["1758-2946"],"issn-type":[{"value":"1758-2946","type":"electronic"}],"subject":[],"published":{"date-parts":[[2021,11,13]]},"assertion":[{"value":"7 June 2021","order":1,"name":"received","label":"Received","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"31 October 2021","order":2,"name":"accepted","label":"Accepted","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"13 November 2021","order":3,"name":"first_online","label":"First Online","group":{"name":"ArticleHistory","label":"Article History"}},{"order":1,"name":"Ethics","group":{"name":"EthicsHeading","label":"Declarations"}},{"value":"Z.L., H.A. and D.D. are employees of Fermion Technology Co., Ltd. S.Z. currently works directly or indirectly for Galixir.","order":2,"name":"Ethics","group":{"name":"EthicsHeading","label":"Competing interests"}}],"article-number":"87"}}