{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,10,15]],"date-time":"2025-10-15T04:16:11Z","timestamp":1760501771978,"version":"build-2065373602"},"reference-count":55,"publisher":"Springer Science and Business Media LLC","issue":"1","license":[{"start":{"date-parts":[[2025,10,14]],"date-time":"2025-10-14T00:00:00Z","timestamp":1760400000000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by-nc-nd\/4.0"},{"start":{"date-parts":[[2025,10,14]],"date-time":"2025-10-14T00:00:00Z","timestamp":1760400000000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by-nc-nd\/4.0"}],"funder":[{"name":"Robert Bosch Centre for Data Science and AI"},{"name":"Centre for Integrative Biology and Systems Medicine"},{"name":"National Science Foundation","award":["IIS-2133650","IIS-2133650"],"award-info":[{"award-number":["IIS-2133650","IIS-2133650"]}]}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":["J Cheminform"],"DOI":"10.1186\/s13321-025-01090-5","type":"journal-article","created":{"date-parts":[[2025,10,14]],"date-time":"2025-10-14T15:52:03Z","timestamp":1760457123000},"update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["PURE: policy-guided unbiased REpresentations for structure-constrained molecular generation"],"prefix":"10.1186","volume":"17","author":[{"given":"Abhor","family":"Gupta","sequence":"first","affiliation":[]},{"given":"Barathi","family":"Lenin","sequence":"additional","affiliation":[]},{"given":"Sean","family":"Current","sequence":"additional","affiliation":[]},{"given":"Rohit","family":"Batra","sequence":"additional","affiliation":[]},{"given":"Balaraman","family":"Ravindran","sequence":"additional","affiliation":[]},{"given":"Karthik","family":"Raman","sequence":"additional","affiliation":[]},{"given":"Srinivasan","family":"Parthasarathy","sequence":"additional","affiliation":[]}],"member":"297","published-online":{"date-parts":[[2025,10,14]]},"reference":[{"issue":"6","key":"1090_CR1","doi-asserted-by":"publisher","first-page":"344","DOI":"10.1093\/bib\/bbab344","volume":"22","author":"Y Cheng","year":"2021","unstructured":"Cheng Y, Gong Y, Liu Y, Song B, Zou Q (2021) Molecular design in drug discovery: a comprehensive review of deep generative models. Brief Bioinform 22(6):344","journal-title":"Brief Bioinform"},{"issue":"9","key":"1090_CR2","doi-asserted-by":"publisher","first-page":"929","DOI":"10.1080\/17460441.2021.1887133","volume":"16","author":"II Baskin","year":"2021","unstructured":"Baskin II (2021) Practical constraints with machine learning in drug discovery. Expert Opin Drug Discov 16(9):929\u2013931","journal-title":"Expert Opin Drug Discov"},{"issue":"1","key":"1090_CR3","doi-asserted-by":"publisher","first-page":"8","DOI":"10.1186\/s13321-023-00679-y","volume":"15","author":"J Choi","year":"2023","unstructured":"Choi J, Seo S, Park S (2023) Coma: efficient structure-constrained molecular generation using contractive and margin losses. J Cheminform 15(1):8","journal-title":"J Cheminform"},{"key":"1090_CR4","first-page":"12","volume":"31","author":"J You","year":"2018","unstructured":"You J, Liu B, Ying Z, Pande V, Leskovec J (2018) Graph convolutional policy network for goal-directed molecular graph generation. Adv Neural Inf Process Syst 31:12","journal-title":"Adv Neural Inf Process Syst"},{"issue":"1","key":"1090_CR5","doi-asserted-by":"publisher","first-page":"10752","DOI":"10.1038\/s41598-019-47148-x","volume":"9","author":"Z Zhou","year":"2019","unstructured":"Zhou Z, Kearnes S, Li L, Zare RN, Riley P (2019) Optimization of molecules via deep reinforcement learning. Sci Rep 9(1):10752","journal-title":"Sci Rep"},{"key":"1090_CR6","doi-asserted-by":"crossref","unstructured":"I\u015f\u0131k R, Tan M (2021) Automated molecule generation using deep q-learning and graph neural networks. In: 2021 IEEE International Conference on Bioinformatics and Biomedicine (BIBM), 2237\u20132244. IEEE","DOI":"10.1109\/BIBM52615.2021.9669667"},{"issue":"1","key":"1090_CR7","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":"7","key":"1090_CR8","doi-asserted-by":"publisher","first-page":"3166","DOI":"10.1021\/acs.jcim.9b00325","volume":"59","author":"N St\u00e5hl","year":"2019","unstructured":"St\u00e5hl 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":"1","key":"1090_CR9","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1186\/s13321-017-0235-x","volume":"9","author":"M Olivecrona","year":"2017","unstructured":"Olivecrona M, Blaschke T, Engkvist O, Chen H (2017) Molecular de-novo design through deep reinforcement learning. J Cheminform 9(1):1\u201314","journal-title":"J Cheminform"},{"issue":"23","key":"1090_CR10","doi-asserted-by":"publisher","first-page":"5907","DOI":"10.1021\/acs.jcim.2c00982","volume":"62","author":"Y Tan","year":"2022","unstructured":"Tan Y, Dai L, Huang W, Guo Y, Zheng S, Lei J, Chen H, Yang Y (2022) Drlinker: Deep reinforcement learning for optimization in fragment linking design. J Chem Inf Model 62(23):5907\u20135917","journal-title":"J Chem Inf Model"},{"key":"1090_CR11","unstructured":"Jin W, Barzilay R, Jaakkola T (2018) Junction tree variational autoencoder for molecular graph generation. In: International Conference on Machine Learning, 2323\u20132332. PMLR"},{"key":"1090_CR12","unstructured":"Jin W, Yang K, Barzilay R, Jaakkola T (2018) Learning multimodal graph-to-graph translation for molecular optimization. arXiv preprint arXiv:1812.01070"},{"key":"1090_CR13","first-page":"638","volume":"34","author":"T Fu","year":"2020","unstructured":"Fu T, Xiao C, Sun J (2020) Core: Automatic molecule optimization using copy & refine strategy. Proc AAAI Conf Artif Intell 34:638\u2013645","journal-title":"Proc AAAI Conf Artif Intell"},{"key":"1090_CR14","unstructured":"Jin W, Barzilay R, Jaakkola T (2020) Hierarchical generation of molecular graphs using structural motifs. In: International Conference on Machine Learning, 4839\u20134848. PMLR"},{"issue":"5","key":"1090_CR15","doi-asserted-by":"publisher","first-page":"1244","DOI":"10.1093\/bioinformatics\/btab817","volume":"38","author":"Y Fan","year":"2022","unstructured":"Fan Y, Xia Y, Zhu J, Wu L, Xie S, Qin T (2022) Back translation for molecule generation. Bioinformatics 38(5):1244\u20131251","journal-title":"Bioinformatics"},{"key":"1090_CR16","doi-asserted-by":"crossref","unstructured":"Barshatski G, Radinsky K (2021) Unpaired generative molecule-to-molecule translation for lead optimization. In: Proceedings of the 27th ACM SIGKDD Conference on Knowledge Discovery & Data Mining, 2554\u20132564","DOI":"10.1145\/3447548.3467120"},{"issue":"1","key":"1090_CR17","doi-asserted-by":"publisher","first-page":"95","DOI":"10.1186\/s13321-024-00887-0","volume":"16","author":"J He","year":"2024","unstructured":"He J, Tibo A, Janet JP, Nittinger E, Tyrchan C, Czechtizky W, Engkvist O (2024) Evaluation of reinforcement learning in transformer-based molecular design. J Cheminform 16(1):95","journal-title":"J Cheminform"},{"issue":"1","key":"1090_CR18","doi-asserted-by":"publisher","first-page":"7315","DOI":"10.1038\/s41467-024-51672-4","volume":"15","author":"A Tibo","year":"2024","unstructured":"Tibo A, He J, Janet JP, Nittinger E, Engkvist O (2024) Exhaustive local chemical space exploration using a transformer model. Nat Commun 15(1):7315","journal-title":"Nat Commun"},{"key":"1090_CR19","first-page":"125","volume":"35","author":"T Fu","year":"2021","unstructured":"Fu T, Xiao C, Li X, Glass LM, Sun J (2021) Mimosa: Multi-constraint molecule sampling for molecule optimization. Proc AAAI Conf Artif Intell 35:125\u2013133","journal-title":"Proc AAAI Conf Artif Intell"},{"issue":"2","key":"1090_CR20","doi-asserted-by":"publisher","first-page":"1002380","DOI":"10.1371\/journal.pcbi.1002380","volume":"8","author":"M Hartenfeller","year":"2012","unstructured":"Hartenfeller M, Zettl H, Walter M, Rupp M, Reisen F, Proschak E, Weggen S, Stark H, Schneider G (2012) Dogs: reaction-driven de novo design of bioactive compounds. PLoS Comput Biol 8(2):1002380","journal-title":"PLoS Comput Biol"},{"issue":"1","key":"1090_CR21","doi-asserted-by":"publisher","first-page":"49","DOI":"10.1021\/ci400418c","volume":"54","author":"K Kawai","year":"2014","unstructured":"Kawai K, Nagata N, Takahashi Y (2014) De novo design of drug-like molecules by a fragment-based molecular evolutionary approach. J Chem Inf Model 54(1):49\u201356","journal-title":"J Chem Inf Model"},{"key":"1090_CR22","doi-asserted-by":"crossref","unstructured":"Sun M, Xing J, Meng H, Wang H, Chen B, Zhou J (2022) Molsearch: search-based multi-objective molecular generation and property optimization. In: Proceedings of the 28th ACM SIGKDD Conference on Knowledge Discovery and Data Mining, 4724\u20134732","DOI":"10.1145\/3534678.3542676"},{"key":"1090_CR23","doi-asserted-by":"publisher","DOI":"10.1016\/j.patcog.2022.108581","volume":"126","author":"J Yu","year":"2022","unstructured":"Yu J, Xu T, Rong Y, Huang J, He R (2022) Structure-aware conditional variational auto-encoder for constrained molecule optimization. Pattern Recogn 126:108581","journal-title":"Pattern Recogn"},{"key":"1090_CR24","unstructured":"Bresson X, Laurent T (2019) A two-step graph convolutional decoder for molecule generation. arXiv preprint arXiv:1906.03412"},{"key":"1090_CR25","unstructured":"Kong X, Tan Z, Liu Y (2021) Graph piece: Efficiently generating high-quality molecular graphs with substructures"},{"key":"1090_CR26","doi-asserted-by":"crossref","unstructured":"Ma C, Zhang X (2021) Gf-vae: a flow-based variational autoencoder for molecule generation. In: Proceedings of the 30th ACM International Conference on Information & Knowledge Management, 1181\u20131190","DOI":"10.1145\/3459637.3482260"},{"key":"1090_CR27","unstructured":"Luo Y, Yan K, Ji S (2021) Graphdf: A discrete flow model for molecular graph generation. In: International Conference on Machine Learning, 7192\u20137203. PMLR"},{"key":"1090_CR28","first-page":"8226","volume":"35","author":"M Kuznetsov","year":"2021","unstructured":"Kuznetsov M, Polykovskiy D (2021) Molgrow: A graph normalizing flow for hierarchical molecular generation. Proc AAAI Conf Artif Intell 35:8226\u20138234","journal-title":"Proc AAAI Conf Artif Intell"},{"key":"1090_CR29","unstructured":"Shi C, Xu M, Zhu Z, Zhang W, Zhang M, Tang J (2020) Graphaf: a flow-based autoregressive model for molecular graph generation. arXiv preprint arXiv:2001.09382"},{"key":"1090_CR30","doi-asserted-by":"crossref","unstructured":"Zang C, Wang F (2020) Moflow: an invertible flow model for generating molecular graphs. In: Proceedings of the 26th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining, 617\u2013626","DOI":"10.1145\/3394486.3403104"},{"issue":"1","key":"1090_CR31","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1186\/s13321-019-0404-1","volume":"12","author":"\u0141 Maziarka","year":"2020","unstructured":"Maziarka \u0141, Pocha A, Kaczmarczyk J, Rataj K, Danel T, Warcho\u0142 M (2020) Mol-cyclegan: a generative model for molecular optimization. J Cheminform 12(1):1\u201318","journal-title":"J Cheminform"},{"key":"1090_CR32","first-page":"1","volume":"12","author":"S Piao","year":"2023","unstructured":"Piao S, Choi J, Seo S, Park S (2023) Self-edit: Structure-constrained molecular optimisation using selfies editing transformer. Appl Intell 12:1\u201313","journal-title":"Appl Intell"},{"key":"1090_CR33","doi-asserted-by":"crossref","unstructured":"Huang H, Sun L, Du B, Lv W (2023) Conditional diffusion based on discrete graph structures for molecular graph generation. arXiv preprint arXiv:2301.00427","DOI":"10.1609\/aaai.v37i4.25549"},{"key":"1090_CR34","unstructured":"Lee Y, Choi G, Yoon M, Kim C (2021) Genetic algorithm for constrained molecular inverse design. arXiv preprint arXiv:2112.03518"},{"issue":"12","key":"1090_CR35","doi-asserted-by":"publisher","first-page":"1040","DOI":"10.1038\/s42256-021-00410-2","volume":"3","author":"Z Chen","year":"2021","unstructured":"Chen Z, Min MR, Parthasarathy S, Ning X (2021) A deep generative model for molecule optimization via one fragment modification. Nature Machine Intell 3(12):1040\u20131049","journal-title":"Nature Machine Intell"},{"issue":"4","key":"1090_CR36","doi-asserted-by":"publisher","first-page":"1701","DOI":"10.1021\/acs.jmedchem.8b00686","volume":"62","author":"MD Shultz","year":"2018","unstructured":"Shultz MD (2018) Two decades under the influence of the rule of five and the changing properties of approved oral drugs: miniperspective. J Med Chem 62(4):1701\u20131714","journal-title":"J Med Chem"},{"key":"1090_CR37","first-page":"35603","volume":"35","author":"B Eysenbach","year":"2022","unstructured":"Eysenbach B, Zhang T, Levine S, Salakhutdinov RR (2022) Contrastive learning as goal-conditioned reinforcement learning. Adv Neural Inf Process Syst 35:35603\u201335620","journal-title":"Adv Neural Inf Process Syst"},{"key":"1090_CR38","first-page":"12","volume":"30","author":"W Jin","year":"2017","unstructured":"Jin W, Coley C, Barzilay R, Jaakkola T (2017) Predicting organic reaction outcomes with weisfeiler-lehman network. Adv Neural Inf Process Syst 30:12","journal-title":"Adv Neural Inf Process Syst"},{"key":"1090_CR39","doi-asserted-by":"publisher","first-page":"239","DOI":"10.1016\/bs.podrm.2018.11.003","volume":"44","author":"AA Abdelgalil","year":"2019","unstructured":"Abdelgalil AA, Alkahtani HM, Al-Jenoobi FI (2019) Sorafenib. Profiles of drug substances xcipients and related methodology 44:239\u2013266","journal-title":"Profiles Drug Substances Excipients Related Methodol"},{"issue":"5","key":"1090_CR40","doi-asserted-by":"publisher","first-page":"614","DOI":"10.1038\/aps.2017.5","volume":"38","author":"Y-J Zhu","year":"2017","unstructured":"Zhu Y-J, Zheng B, Wang H-Y, Chen L (2017) New knowledge of the mechanisms of sorafenib resistance in liver cancer. Acta Pharmacol Sin 38(5):614\u2013622","journal-title":"Acta Pharmacol Sin"},{"issue":"1","key":"1090_CR41","doi-asserted-by":"publisher","first-page":"87","DOI":"10.1038\/s41392-020-0187-x","volume":"5","author":"W Tang","year":"2020","unstructured":"Tang W, Chen Z, Zhang W, Cheng Y, Zhang B, Wu F, Wang Q, Wang S, Rong D, Reiter F et al (2020) The mechanisms of sorafenib resistance in hepatocellular carcinoma: theoretical basis and therapeutic aspects. Signal Transduct Target Ther 5(1):87","journal-title":"Signal Transduct Target Ther"},{"key":"1090_CR42","first-page":"24","volume":"31","author":"AV Nair","year":"2018","unstructured":"Nair AV, Pong V, Dalal M, Bahl S, Lin S, Levine S (2018) Visual reinforcement learning with imagined goals. Adv Neural Inf Process Syst 31:24","journal-title":"Adv Neural Inf Process Syst"},{"key":"1090_CR43","unstructured":"Kaelbling LP (1993) Learning to achieve goals. In: IJCAI, 2, 1094\u20138. Citeseer"},{"key":"1090_CR44","unstructured":"Xu K, Hu W, Leskovec J, Jegelka S (2018) How powerful are graph neural networks? arXiv preprint arXiv:1810.00826"},{"issue":"24","key":"1090_CR45","doi-asserted-by":"publisher","first-page":"3955","DOI":"10.1093\/bioinformatics\/btx481","volume":"33","author":"A Sankar","year":"2017","unstructured":"Sankar A, Ranu S, Raman K (2017) Predicting novel metabolic pathways through subgraph mining. Bioinformatics 33(24):3955\u20133963","journal-title":"Bioinformatics"},{"key":"1090_CR46","doi-asserted-by":"crossref","unstructured":"Buehrer G, Parthasarathy S, Chen Y-K (2006) Adaptive parallel graph mining for cmp architectures. In: Sixth International Conference on Data Mining (ICDM\u201906), 97\u2013106. IEEE","DOI":"10.1109\/ICDM.2006.15"},{"key":"1090_CR47","unstructured":"Jin W, Yang K, Barzilay R, Jaakkola T (2018) Learning multimodal graph-to-graph translation for molecule optimization. In: International Conference on Learning Representations"},{"issue":"22\u201323","key":"1090_CR48","first-page":"5545","volume":"36","author":"K Huang","year":"2020","unstructured":"Huang K, Fu T, Glass LM, Zitnik M, Xiao C, Sun J (2020) Deeppurpose: a deep learning library for drug-target interaction prediction. Bioinformatics 36(22\u201323):5545\u20135547","journal-title":"Bioinformatics"},{"key":"1090_CR49","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1186\/s13321-020-00456-1","volume":"12","author":"AP Bento","year":"2020","unstructured":"Bento AP, Hersey A, F\u00e9lix E, Landrum G, Gaulton A, Atkinson F, Bellis LJ, Veij M, Leach AR (2020) An open source chemical structure curation pipeline using rdkit. J Cheminform 12:1\u201316","journal-title":"J Cheminform"},{"key":"1090_CR50","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1186\/1758-2946-3-1","volume":"3","author":"NM O\u2019Boyle","year":"2011","unstructured":"O\u2019Boyle NM, Banck M, James CA, Morley C, Vandermeersch T, Hutchison GR (2011) Open babel: an open chemical toolbox. J Cheminform 3:1\u201314","journal-title":"J Cheminform"},{"issue":"8","key":"1090_CR51","doi-asserted-by":"publisher","first-page":"3891","DOI":"10.1021\/acs.jcim.1c00203","volume":"61","author":"J Eberhardt","year":"2021","unstructured":"Eberhardt J, Santos-Martins D, Tillack AF, Forli S (2021) Autodock vina 1.2. 0: new docking methods, expanded force field, and python bindings. J Chem Inf Model 61(8):3891\u20133898","journal-title":"J Chem Inf Model"},{"issue":"16","key":"1090_CR52","doi-asserted-by":"publisher","first-page":"2785","DOI":"10.1002\/jcc.21256","volume":"30","author":"GM Morris","year":"2009","unstructured":"Morris GM, Huey R, Lindstrom W, Sanner MF, Belew RK, Goodsell DS, Olson AJ (2009) Autodock4 and autodocktools4: automated docking with selective receptor flexibility. J Comput Chem 30(16):2785\u20132791","journal-title":"J Comput Chem"},{"issue":"1","key":"1090_CR53","first-page":"82","volume":"40","author":"WL DeLano","year":"2002","unstructured":"DeLano WL et al (2002) Pymol: An open-source molecular graphics tool. CCP4 Newsl Protein Crystallogr 40(1):82\u201392","journal-title":"CCP4 Newsl Protein Crystallogr"},{"issue":"W1","key":"1090_CR54","doi-asserted-by":"publisher","first-page":"443","DOI":"10.1093\/nar\/gkv315","volume":"43","author":"S Salentin","year":"2015","unstructured":"Salentin S, Schreiber S, Haupt VJ, Adasme MF, Schroeder M (2015) Plip: fully automated protein-ligand interaction profiler. Nucleic Acids Res 43(W1):443\u2013447","journal-title":"Nucleic Acids Res"},{"issue":"1","key":"1090_CR55","doi-asserted-by":"publisher","first-page":"42717","DOI":"10.1038\/srep42717","volume":"7","author":"A Daina","year":"2017","unstructured":"Daina A, Michielin O, Zoete V (2017) Swissadme: a free web tool to evaluate pharmacokinetics, drug-likeness and medicinal chemistry friendliness of small molecules. Sci Rep 7(1):42717","journal-title":"Sci Rep"}],"container-title":["Journal of Cheminformatics"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1186\/s13321-025-01090-5.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/article\/10.1186\/s13321-025-01090-5\/fulltext.html","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1186\/s13321-025-01090-5.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,15]],"date-time":"2025-10-15T03:47:47Z","timestamp":1760500067000},"score":1,"resource":{"primary":{"URL":"https:\/\/jcheminf.biomedcentral.com\/articles\/10.1186\/s13321-025-01090-5"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2025,10,14]]},"references-count":55,"journal-issue":{"issue":"1","published-online":{"date-parts":[[2025,12]]}},"alternative-id":["1090"],"URL":"https:\/\/doi.org\/10.1186\/s13321-025-01090-5","relation":{},"ISSN":["1758-2946"],"issn-type":[{"type":"electronic","value":"1758-2946"}],"subject":[],"published":{"date-parts":[[2025,10,14]]},"assertion":[{"value":"23 February 2025","order":1,"name":"received","label":"Received","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"28 August 2025","order":2,"name":"accepted","label":"Accepted","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"14 October 2025","order":3,"name":"first_online","label":"First Online","group":{"name":"ArticleHistory","label":"Article History"}},{"order":1,"name":"Ethics","group":{"name":"EthicsHeading","label":"Declarations"}},{"value":"The authors declare no conflict of interest.","order":2,"name":"Ethics","group":{"name":"EthicsHeading","label":"Competing interests"}}],"article-number":"156"}}