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Recently, deep generative models have achieved inspiring progress in generating drug-like compounds. However, the models prioritize a single target drug generation for pharmacological intervention, neglecting the complicated inherent mechanisms of diseases, and influenced by multiple factors. Consequently, developing novel multi-target drugs that simultaneously target specific targets can enhance anti-tumor efficacy and address issues related to resistance mechanisms. To address this issue and inspired by Generative Pre-trained Transformers (GPT) models, we propose an upgraded GPT model with generative adversarial imitation learning for multi-target molecular generation called MTMol-GPT. The multi-target molecular generator employs a dual discriminator model using the Inverse Reinforcement Learning (IRL) method for a concurrently multi-target molecular generation. Extensive results show that MTMol-GPT generates various valid, novel, and effective multi-target molecules for various complex diseases, demonstrating robustness and generalization capability. In addition, molecular docking and pharmacophore mapping experiments demonstrate the drug-likeness properties and effectiveness of generated molecules potentially improve neuropsychiatric interventions. Furthermore, our model\u2019s generalizability is exemplified by a case study focusing on the multi-targeted drug design for breast cancer. As a broadly applicable solution for multiple targets, MTMol-GPT provides new insight into future directions to enhance potential complex disease therapeutics by generating high-quality multi-target molecules in drug discovery.<\/jats:p>","DOI":"10.1371\/journal.pcbi.1012229","type":"journal-article","created":{"date-parts":[[2024,6,26]],"date-time":"2024-06-26T17:49:37Z","timestamp":1719424177000},"page":"e1012229","update-policy":"https:\/\/doi.org\/10.1371\/journal.pcbi.corrections_policy","source":"Crossref","is-referenced-by-count":60,"title":["MTMol-GPT: De novo multi-target molecular generation with transformer-based generative adversarial imitation learning"],"prefix":"10.1371","volume":"20","author":[{"ORCID":"https:\/\/orcid.org\/0000-0003-1314-660X","authenticated-orcid":true,"given":"Chengwei","family":"Ai","sequence":"first","affiliation":[]},{"ORCID":"https:\/\/orcid.org\/0009-0001-9101-3063","authenticated-orcid":true,"given":"Hongpeng","family":"Yang","sequence":"additional","affiliation":[]},{"given":"Xiaoyi","family":"Liu","sequence":"additional","affiliation":[]},{"given":"Ruihan","family":"Dong","sequence":"additional","affiliation":[]},{"given":"Yijie","family":"Ding","sequence":"additional","affiliation":[]},{"ORCID":"https:\/\/orcid.org\/0000-0001-8346-0798","authenticated-orcid":true,"given":"Fei","family":"Guo","sequence":"additional","affiliation":[]}],"member":"340","published-online":{"date-parts":[[2024,6,26]]},"reference":[{"issue":"8","key":"pcbi.1012229.ref001","doi-asserted-by":"crossref","first-page":"675","DOI":"10.1007\/s10822-013-9672-4","article-title":"Estimation of the size of drug-like chemical space based on GDB-17 data","volume":"27","author":"PG Polishchuk","year":"2013","journal-title":"Journal of Computer-Aided Molecular Design"},{"issue":"6","key":"pcbi.1012229.ref002","doi-asserted-by":"crossref","first-page":"bbab344","DOI":"10.1093\/bib\/bbab344","article-title":"Molecular design in drug discovery: a comprehensive review of deep generative models","volume":"22","author":"Y Cheng","year":"2021","journal-title":"Briefings in Bioinformatics"},{"issue":"6","key":"pcbi.1012229.ref003","doi-asserted-by":"crossref","first-page":"bbad393","DOI":"10.1093\/bib\/bbad393","article-title":"MVML-MPI: Multi-View Multi-Label Learning for Metabolic Pathway Inference","volume":"24","author":"X Liu","year":"2023","journal-title":"Briefings in Bioinformatics"},{"issue":"3","key":"pcbi.1012229.ref004","doi-asserted-by":"crossref","first-page":"188","DOI":"10.1038\/nrd3368","article-title":"Impact of high-throughput screening in biomedical research","volume":"10","author":"R Macarron","year":"2011","journal-title":"Nature Reviews Drug Discovery"},{"issue":"20","key":"pcbi.1012229.ref005","doi-asserted-by":"crossref","first-page":"2235","DOI":"10.2174\/1568026617666170224121313","article-title":"Structure-based virtual screening approaches in kinase-directed drug discovery","volume":"17","author":"D Bajusz","year":"2017","journal-title":"Current Topics in Medicinal Chemistry"},{"issue":"6","key":"pcbi.1012229.ref006","doi-asserted-by":"crossref","first-page":"981","DOI":"10.1111\/cbdd.12043","article-title":"Biological Effects of AL622, a Molecule Rationally Designed to Release an EGFR and a c-Src Kinase Inhibitor","volume":"80","author":"AL Larroque-Lombard","year":"2012","journal-title":"Chemical Biology & Drug Design"},{"key":"pcbi.1012229.ref007","doi-asserted-by":"crossref","first-page":"115810","DOI":"10.1016\/j.eswa.2021.115810","article-title":"Drug-target continuous binding affinity prediction using multiple sources of information","volume":"186","author":"B Tanoori","year":"2021","journal-title":"Expert Systems with Applications"},{"key":"pcbi.1012229.ref008","doi-asserted-by":"crossref","first-page":"119312","DOI":"10.1016\/j.eswa.2022.119312","article-title":"DAEM: Deep attributed embedding based multi-task learning for predicting adverse drug\u2013drug interaction","volume":"215","author":"J Zhu","year":"2023","journal-title":"Expert Systems with Applications"},{"key":"pcbi.1012229.ref009","doi-asserted-by":"crossref","first-page":"116825","DOI":"10.1016\/j.eswa.2022.116825","article-title":"Artificial intelligence-based decision support model for new drug development planning","volume":"198","author":"YL Jung","year":"2022","journal-title":"Expert Systems with Applications"},{"key":"pcbi.1012229.ref010","doi-asserted-by":"crossref","first-page":"116165","DOI":"10.1016\/j.eswa.2021.116165","article-title":"Relation-aware Heterogeneous Graph Transformer based drug repurposing","volume":"190","author":"X Mei","year":"2022","journal-title":"Expert Systems with Applications"},{"issue":"1","key":"pcbi.1012229.ref011","doi-asserted-by":"crossref","first-page":"31","DOI":"10.1021\/ci00057a005","article-title":"SMILES, a chemical language and information system. 1. 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