{"about":{"site":"https://codewithpapers.app","non_affiliation":"Code with Papers and Syntology are not affiliated with, endorsed by, or sponsored by Papers with Code, Meta, or the pwc-archive mirror.","licence":"CC BY-SA 4.0","licence_url":"https://creativecommons.org/licenses/by-sa/4.0/legalcode","attribution":"https://codewithpapers.app/attribution","modified":"archive material modified by Syntology; see the attribution page"},"url":"/paper/optimization-of-binding-affinities-in","title":"Optimization of binding affinities in chemical space with generative pretrained transformer and deep reinforcement learning","arxiv_id":null,"date":"2023-04-03","proceeding":"F1000Research 2023 4","authors":["Xiaopeng Xu","Juexiao Zhou","Chen Zhu","Qing Zhan","Zhongxiao Li","Ruochi Zhang","Yu Wang","Xingyu Liao","Xin Gao"],"abstract":"Background: The key challenge in drug discovery is to discover novel compounds with desirable properties. Among the properties, binding affinity to a target is one of the prerequisites and usually evaluated by molecular docking or quantitative structure activity relationship (QSAR) models. Methods: In this study, we developed Simplified molecular input line entry system Generative Pretrained Transformer with Reinforcement Learning (SGPT-RL), which uses a transformer decoder as the policy network of the reinforcement learning agent to optimize the binding affinity to a target. SGPT-RL was evaluated on the Moses distribution learning benchmark and two goal-directed generation tasks, with Dopamine Receptor D2 (DRD2) and Angiotensin-Converting Enzyme 2 (ACE2) as the targets. Both QSAR model and molecular docking were implemented as the optimization goals in the tasks. The popular Reinvent method was used as the baseline for comparison. Results: The results on Moses benchmark showed that SGPT-RL learned good property distributions and generated molecules with high validity and novelty. On the two goal-directed generation tasks, both SGPT-RL and Reinvent were able to generate valid molecules with improved target scores. The SGPT-RL method achieved better results than Reinvent on the ACE2 task, where molecular docking was used as the optimization goal. Further analysis shows that SGPT-RL learned conserved scaffold patterns during exploration. Conclusions: The superior performance of SGPT-RL in the ACE2 task indicates that it can be applied to the virtual screening process where molecular docking is widely used as the criteria. Besides, the scaffold patterns learned by SGPT-RL during the exploration process can assist chemists to better design and discover novel lead candidates.","url_abs":"https://chemrxiv.org/engage/chemrxiv/article-details/64272436a029a26b4cb49451","url_pdf":"https://chemrxiv.org/engage/api-gateway/chemrxiv/assets/orp/resource/item/64272436a029a26b4cb49451/original/optimization-of-binding-affinities-in-chemical-space-with-generative-pretrained-transformer-and-deep-reinforcement-learning.pdf","source":{"archive":"pwc-archive (Hugging Face), CC BY-SA 4.0","snapshot":"2025-07-28","licence_url":"https://creativecommons.org/licenses/by-sa/4.0/legalcode","row_kind":"abstracts"},"code_links":[{"paper_slug":"optimization-of-binding-affinities-in","repo_url":"https://github.com/charlesxu90/sgpt","is_official":0,"mentioned_in_paper":1,"mentioned_in_github":0,"framework":"pytorch","reach":null}],"tasks":[{"task_slug":"deep-reinforcement-learning","task_name":"Deep Reinforcement Learning"},{"task_slug":"drug-discovery","task_name":"Drug Discovery"},{"task_slug":"molecular-docking","task_name":"Molecular Docking"}],"methods":[{"method_slug":"dropout","method_name":"Dropout"},{"method_slug":"layer-normalization","method_name":"Layer Normalization"},{"method_slug":"linear-layer","method_name":"Linear Layer"},{"method_slug":"multi-head-attention","method_name":"Multi-Head Attention"},{"method_slug":"position-wise-feed-forward-layer","method_name":"Position-Wise Feed-Forward Layer"},{"method_slug":"reinforce","method_name":"REINFORCE"},{"method_slug":"residual-connection","method_name":"Residual Connection"},{"method_slug":"transformer","method_name":"Transformer"}],"datasets_introduced":[],"methods_introduced":[],"results":[],"syntology":{"syntology_url":null,"atlas_url":null,"mcp":null,"developers":"https://syntology.ai/developers"},"arxiv_metadata":null,"syntology_extracted_results":null}