Predicting chemical structure using reinforcement learning with a stack-augmented conditional variational autoencoder

Journal of Cheminformatics

Research

Hwanhee Kim¹  Jaegyoon Ahn¹  Soohyun Ko²  Byung Ju Kim³  Sung Jin Ryu³ 

[1] Department of Computer Science and Engineering, Incheon National University, 22012, Incheon, Republic of Korea;GenesisEgo, 04382, Seoul, Republic of Korea;UBLBio Corporation, 16679, Suwon, Republic of Korea;

关键词: De novo drug design;    Reinforcement learning;    Conditional Variational AutoEencoder;    Sorafenib;    Raf kinases;   

DOI  :  10.1186/s13321-022-00666-9

 received in 2022-02-11, accepted in 2022-12-03,  发布年份 2022

来源: Springer

【 摘 要 】

In this paper, a reinforcement learning model is proposed that can maximize the predicted binding affinity between a generated molecule and target proteins. The model used to generate molecules in the proposed model was the Stacked Conditional Variation AutoEncoder (Stack-CVAE), which acts as an agent in reinforcement learning so that the resulting chemical formulas have the desired chemical properties and show high binding affinity with specific target proteins. We generated 1000 chemical formulas using the chemical properties of sorafenib and the three target kinases of sorafenib. Then, we confirmed that Stack-CVAE generates more of the valid and unique chemical compounds that have the desired chemical properties and predicted binding affinity better than other generative models. More detailed analysis for 100 of the top scoring molecules show that they are novel ones not found in existing chemical databases. Moreover, they reveal significantly higher predicted binding affinity score for Raf kinases than for other kinases. Furthermore, they are highly druggable and synthesizable.

【 授权许可】

CC BY   
© The Author(s) 2022

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