{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,9,24]],"date-time":"2026-09-24T04:11:31Z","timestamp":1790223091795,"version":"4.1.0"},"reference-count":39,"publisher":"MDPI AG","issue":"1","license":[{"start":{"date-parts":[[2024,12,24]],"date-time":"2024-12-24T00:00:00Z","timestamp":1734998400000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"National Natural Science Foundation of China","award":["81725022"],"award-info":[{"award-number":["81725022"]}]},{"name":"National Natural Science Foundation of China","award":["82173739"],"award-info":[{"award-number":["82173739"]}]},{"name":"National Natural Science Foundation of China","award":["2023YFF1205102"],"award-info":[{"award-number":["2023YFF1205102"]}]},{"name":"National Natural Science Foundation of China","award":["24ZR1413800"],"award-info":[{"award-number":["24ZR1413800"]}]},{"name":"Ministry of Science and Technology of China","award":["81725022"],"award-info":[{"award-number":["81725022"]}]},{"name":"Ministry of Science and Technology of China","award":["82173739"],"award-info":[{"award-number":["82173739"]}]},{"name":"Ministry of Science and Technology of China","award":["2023YFF1205102"],"award-info":[{"award-number":["2023YFF1205102"]}]},{"name":"Ministry of Science and Technology of China","award":["24ZR1413800"],"award-info":[{"award-number":["24ZR1413800"]}]},{"DOI":"10.13039\/100007219","name":"Shanghai Natural Science Foundation","doi-asserted-by":"publisher","award":["81725022"],"award-info":[{"award-number":["81725022"]}],"id":[{"id":"10.13039\/100007219","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/100007219","name":"Shanghai Natural Science Foundation","doi-asserted-by":"publisher","award":["82173739"],"award-info":[{"award-number":["82173739"]}],"id":[{"id":"10.13039\/100007219","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/100007219","name":"Shanghai Natural Science Foundation","doi-asserted-by":"publisher","award":["2023YFF1205102"],"award-info":[{"award-number":["2023YFF1205102"]}],"id":[{"id":"10.13039\/100007219","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/100007219","name":"Shanghai Natural Science Foundation","doi-asserted-by":"publisher","award":["24ZR1413800"],"award-info":[{"award-number":["24ZR1413800"]}],"id":[{"id":"10.13039\/100007219","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Molecules"],"abstract":"<jats:p>As an appealing approach for discovering novel leads, the key advantage of de novo drug design lies in its ability to explore a much broader dimension of chemical space, without being confined to the knowledge of existing compounds. So far, many generative models have been described in the literature, which have completely redefined the concept of de novo drug design. However, many of them lack practical value for real-world drug discovery. In this work, we have developed a graph-based generative model within a reinforcement learning framework, namely, METEOR (Molecular Exploration Through multiplE-Objective Reinforcement). The backend agent of METEOR is based on the well-established GCPN model. To ensure the overall quality of the generated molecular graphs, we implemented a set of rules to identify and exclude undesired substructures. Importantly, METEOR is designed to conduct multi-objective optimization, i.e., simultaneously optimizing binding affinity, drug-likeness, and synthetic accessibility of the generated molecules under the guidance of a special reward function. We demonstrate in a specific test case that without prior knowledge of true binders to the chosen target protein, METEOR generated molecules with superior properties compared to those in the ZINC 250k data set. In conclusion, we have demonstrated the potential of METEOR as a practical tool for generating rational drug-like molecules in the early phase of drug discovery.<\/jats:p>","DOI":"10.3390\/molecules30010018","type":"journal-article","created":{"date-parts":[[2024,12,25]],"date-time":"2024-12-25T19:19:32Z","timestamp":1735154372000},"page":"18","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":4,"title":["Generation of Rational Drug-like Molecular Structures Through a Multiple-Objective Reinforcement Learning Framework"],"prefix":"10.3390","volume":"30","author":[{"given":"Xiangying","family":"Zhang","sequence":"first","affiliation":[{"name":"Department of Medicinal Chemistry, School of Pharmacy, Fudan University, 826 Zhangheng Road, Shanghai 201203, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Haotian","family":"Gao","sequence":"additional","affiliation":[{"name":"Department of Medicinal Chemistry, School of Pharmacy, Fudan University, 826 Zhangheng Road, Shanghai 201203, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Yifei","family":"Qi","sequence":"additional","affiliation":[{"name":"Department of Medicinal Chemistry, School of Pharmacy, Fudan University, 826 Zhangheng Road, Shanghai 201203, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Yan","family":"Li","sequence":"additional","affiliation":[{"name":"Department of Medicinal Chemistry, School of Pharmacy, Fudan University, 826 Zhangheng Road, Shanghai 201203, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-0485-0259","authenticated-orcid":false,"given":"Renxiao","family":"Wang","sequence":"additional","affiliation":[{"name":"Department of Medicinal Chemistry, School of Pharmacy, Fudan University, 826 Zhangheng Road, Shanghai 201203, China"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"1968","published-online":{"date-parts":[[2024,12,24]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"2324","DOI":"10.1021\/acs.jcim.5b00559","article-title":"ZINC 15\u2014Ligand discovery for everyone","volume":"55","author":"Sterling","year":"2015","journal-title":"J. Chem. Inf. Model."},{"key":"ref_2","doi-asserted-by":"crossref","first-page":"823","DOI":"10.1038\/432823a","article-title":"Chemical space","volume":"432","author":"Kirkpatrick","year":"2004","journal-title":"Nature"},{"key":"ref_3","doi-asserted-by":"crossref","first-page":"1496","DOI":"10.1021\/acsmedchemlett.0c00088","article-title":"The advent of generative chemistry","volume":"11","author":"Vanhaelen","year":"2020","journal-title":"ACS Med. Chem. Lett."},{"key":"ref_4","doi-asserted-by":"crossref","first-page":"5343","DOI":"10.1021\/acs.jcim.0c01496","article-title":"Generative deep learning for targeted compound design","volume":"61","author":"Sousa","year":"2021","journal-title":"J. Chem. Inf. Model."},{"key":"ref_5","doi-asserted-by":"crossref","first-page":"4863","DOI":"10.1021\/acs.jcim.2c00838","article-title":"De novo drug design using reinforcement learning with graph-based deep generative models","volume":"62","author":"Atance","year":"2022","journal-title":"J. Chem. Inf. Model."},{"key":"ref_6","doi-asserted-by":"crossref","first-page":"71","DOI":"10.1186\/s13321-019-0393-0","article-title":"Randomized SMILES strings improve the quality of molecular generative models","volume":"11","author":"Johansson","year":"2019","journal-title":"J. Cheminform."},{"key":"ref_7","doi-asserted-by":"crossref","first-page":"5316","DOI":"10.1039\/D1CC07035E","article-title":"Modern machine learning for tackling inverse problems in chemistry: Molecular design to realization","volume":"58","author":"Sridharan","year":"2022","journal-title":"Chem. Commun."},{"key":"ref_8","first-page":"6410","article-title":"Graph convolutional policy network for goal-directed molecular graph generation","volume":"31","author":"You","year":"2018","journal-title":"Adv. Neural Inf. Process. Syst."},{"key":"ref_9","unstructured":"Mariya, P., Mykhailo, S., Junier, O., and Olexandr, I. (2019). MolecularRNN: Generating realistic molecular graphs with optimized properties. arXiv."},{"key":"ref_10","doi-asserted-by":"crossref","unstructured":"Papadopoulos, K., Giblin, K.A., Janet, J.P., Patronov, A., and Engkvist, O. (2021). De novo design with deep generative models based on 3D similarity scoring. Bioorganic Med. Chem., 44.","DOI":"10.1016\/j.bmc.2021.116308"},{"key":"ref_11","doi-asserted-by":"crossref","first-page":"129","DOI":"10.1038\/s42004-022-00733-0","article-title":"Generative and reinforcement learning approaches for the automated de novo design of bioactive compounds","volume":"5","author":"Korshunova","year":"2022","journal-title":"Commun. Chem."},{"key":"ref_12","doi-asserted-by":"crossref","first-page":"3629","DOI":"10.1021\/acs.jcim.3c00434","article-title":"DrugEx: Deep learning models and tools for exploration of drug-like chemical space","volume":"63","author":"Sicho","year":"2023","journal-title":"J. Chem. Inf. Model."},{"key":"ref_13","doi-asserted-by":"crossref","first-page":"3719","DOI":"10.1021\/acs.jcim.3c00293","article-title":"A simple way to incorporate target structural information in molecular generative models","volume":"63","author":"Zhang","year":"2023","journal-title":"J. Chem. Inf. Model."},{"key":"ref_14","doi-asserted-by":"crossref","first-page":"68","DOI":"10.1186\/s13321-020-00473-0","article-title":"Memory-assisted reinforcement learning for diverse molecular de novo design","volume":"12","author":"Blaschke","year":"2020","journal-title":"J. Cheminform."},{"key":"ref_15","doi-asserted-by":"crossref","first-page":"48","DOI":"10.1186\/s13321-017-0235-x","article-title":"Molecular de-novo design through deep reinforcement learning","volume":"9","author":"Olivecrona","year":"2017","journal-title":"J. Cheminform."},{"key":"ref_16","doi-asserted-by":"crossref","first-page":"1038","DOI":"10.1038\/s41587-019-0224-x","article-title":"Deep learning enables rapid identification of potent DDR1 kinase inhibitors","volume":"37","author":"Zhavoronkov","year":"2019","journal-title":"Nat. Biotechnol."},{"key":"ref_17","doi-asserted-by":"crossref","first-page":"386","DOI":"10.1038\/s42256-023-00636-2","article-title":"Testing the limits of SMILES-based de novo molecular generation with curriculum and deep reinforcement learning","volume":"5","author":"Mokaya","year":"2023","journal-title":"Nat. Mach. Intell."},{"key":"ref_18","doi-asserted-by":"crossref","unstructured":"Mazuz, E., Shtar, G., Shapira, B., and Rokach, L. (2023). Molecule generation using transformers and policy gradient reinforcement learning. Sci. Rep., 13.","DOI":"10.1038\/s41598-023-35648-w"},{"key":"ref_19","doi-asserted-by":"crossref","first-page":"e1608","DOI":"10.1002\/wcms.1608","article-title":"Generative models for molecular discovery: Recent advances and challenges","volume":"12","author":"Bilodeau","year":"2022","journal-title":"WIREs Comput. Mol. Sci."},{"key":"ref_20","doi-asserted-by":"crossref","first-page":"5907","DOI":"10.1021\/acs.jcim.2c00982","article-title":"DRlinker: Deep Reinforcement Learning for Optimization in Fragment Linking Design","volume":"62","author":"Tan","year":"2022","journal-title":"J. Chem. Inf. Model."},{"key":"ref_21","unstructured":"Wengong, J., Regina, B., and Tommi, J. (2020). Multi-Objective Molecule Generation using Interpretable Substructures. arXiv."},{"key":"ref_22","doi-asserted-by":"crossref","first-page":"513","DOI":"10.1021\/acscentsci.0c00026","article-title":"Accurate Multiobjective Design in a Space of Millions of Transition Metal Complexes with Neural-Network-Driven Efficient Global Optimization","volume":"6","author":"Janet","year":"2020","journal-title":"ACS Cent. Sci."},{"key":"ref_23","doi-asserted-by":"crossref","first-page":"263","DOI":"10.1038\/s43588-024-00616-5","article-title":"Directional multiobjective optimization of metal complexes at the billion-system scale","volume":"4","author":"Kneiding","year":"2024","journal-title":"Nat. Comput. Sci."},{"key":"ref_24","doi-asserted-by":"crossref","first-page":"2093","DOI":"10.1021\/acs.jcim.1c00777","article-title":"Exploring graph traversal algorithms in graph-based molecular generation","volume":"62","author":"Mercado","year":"2022","journal-title":"J. Chem. Inf. Model."},{"key":"ref_25","doi-asserted-by":"crossref","first-page":"2205","DOI":"10.1021\/acs.jcim.3c00253","article-title":"PLANET: A multi-objective graph neural network model for protein\u2013ligand binding affinity prediction","volume":"64","author":"Zhang","year":"2023","journal-title":"J. Chem. Inf. Model."},{"key":"ref_26","doi-asserted-by":"crossref","unstructured":"McInnes, L., Healy, J., and Melville, J. (2018). UMAP: Uniform manifold approximation and projection for dimension reduction. arXiv.","DOI":"10.21105\/joss.00861"},{"key":"ref_27","first-page":"2224","article-title":"Convolutional networks on graphs for learning molecular fingerprints","volume":"2","author":"Duvenaud","year":"2015","journal-title":"Adv. Neural Inf. Process. Syst."},{"key":"ref_28","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","year":"2016","journal-title":"J. Comput. Aided Mol. Des."},{"key":"ref_29","unstructured":"Kipf, T.N., and Welling, M. (2016). Semi-supervised classification with graph convolutional networks. arXiv."},{"key":"ref_30","first-page":"1","article-title":"Guided policy search","volume":"28","author":"Levine","year":"2013","journal-title":"Proc. Mach. Learn."},{"key":"ref_31","doi-asserted-by":"crossref","first-page":"881","DOI":"10.1038\/nrd2445","article-title":"The influence of drug-like concepts on decision-making in medicinal chemistry","volume":"6","author":"Leeson","year":"2007","journal-title":"Nat. Rev. Drug Discov."},{"key":"ref_32","doi-asserted-by":"crossref","first-page":"760","DOI":"10.1002\/wcms.52","article-title":"Understanding drug-likeness","volume":"1","author":"Ursu","year":"2011","journal-title":"WIREs Comput. Mol. Sci."},{"key":"ref_33","doi-asserted-by":"crossref","first-page":"90","DOI":"10.1038\/nchem.1243","article-title":"Quantifying the chemical beauty of drugs","volume":"4","author":"Bickerton","year":"2012","journal-title":"Nat. Chem."},{"key":"ref_34","doi-asserted-by":"crossref","first-page":"5377","DOI":"10.1021\/acs.jmedchem.3c00134","article-title":"Macrocycles in Drug Discovery: Learning from the Past for the Future","volume":"66","author":"Jimenez","year":"2023","journal-title":"J. Med. Chem."},{"key":"ref_35","doi-asserted-by":"crossref","first-page":"8","DOI":"10.1186\/1758-2946-1-8","article-title":"Estimation of synthetic accessibility score of drug-like molecules based on molecular complexity and fragment contributions","volume":"1","author":"Ertl","year":"2009","journal-title":"J. Cheminform."},{"key":"ref_36","unstructured":"Schulman, J., Wolski, F., Dhariwal, P., Radford, A., and Klimov, O. (2017). Proximal policy optimization algorithms. arXiv."},{"key":"ref_37","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":"Jacquemard","year":"2020","journal-title":"J. Chem. Inf. Model."},{"key":"ref_38","doi-asserted-by":"crossref","first-page":"29052","DOI":"10.1074\/jbc.M705005200","article-title":"Crystal structures of complexes of N-butyl- and N-nonyl-deoxynojirimycin bound to acid \u03b2-glucosidase","volume":"282","author":"Brumshtein","year":"2007","journal-title":"J. Biol. Chem."},{"key":"ref_39","doi-asserted-by":"crossref","first-page":"326","DOI":"10.1038\/s42256-024-00808-8","article-title":"PocketFlow is a data-and-knowledge-driven structure-based molecular generative model","volume":"6","author":"Jiang","year":"2024","journal-title":"Nat. Mach. Intell."}],"container-title":["Molecules"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/1420-3049\/30\/1\/18\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,10]],"date-time":"2025-10-10T16:59:38Z","timestamp":1760115578000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/1420-3049\/30\/1\/18"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2024,12,24]]},"references-count":39,"journal-issue":{"issue":"1","published-online":{"date-parts":[[2025,1]]}},"alternative-id":["molecules30010018"],"URL":"https:\/\/doi.org\/10.3390\/molecules30010018","relation":{},"ISSN":["1420-3049"],"issn-type":[{"value":"1420-3049","type":"electronic"}],"subject":[],"published":{"date-parts":[[2024,12,24]]}}}