Universal Approach to De Novo Drug Design for Target Proteins Using Deep Reinforcement Learning

Yunjiang Zhang; Shuyuan Li; Miaojuan Xing; Qing Yuan; Hong He; Shaorui Sun

doi:10.1021/acsomega.2c06653

Universal Approach to De Novo Drug Design for Target Proteins Using Deep Reinforcement Learning

ACS Omega. 2023 Feb 6;8(6):5464-5474. doi: 10.1021/acsomega.2c06653. eCollection 2023 Feb 14.

Authors

Yunjiang Zhang¹, Shuyuan Li¹, Miaojuan Xing¹, Qing Yuan², Hong He¹, Shaorui Sun¹

Affiliations

¹ Beijing Key Laboratory for Green Catalysis and Separation, The Faculty of Environment and Life, Beijing University of Technology, Beijing100124, PR China.
² Department of Chemistry and Chemical Engineering, Beijing University of Technology, Beijing100124, China.

Abstract

In drug design, the design and manufacture of safe and effective compounds is a long-term, complex, and complicated process. Therefore, developing a new rapid and generalizable drug design method is of great value. This study aimed to propose a general model based on reinforcement learning combined with drug-target interaction, which could be used to design new molecules according to different protein targets. The method adopted recurrent neural network molecular modeling and took the drug-target affinity model as the reward function of optimal molecular generation. It did not need to know the three-dimensional structure and active sites of protein targets but only required the information of a one-dimensional amino acid sequence. This approach was demonstrated to produce drugs highly similar to marketed drugs and design molecules with a better binding energy.