Soohyun Ko  Chihyun Park  Jaegyoon Ahn

10.5626/JOK.2021.48.2.234

Machine Learning Deep Learning Reinforcement Learning Drug design

Numerous computer-based methods have been investigated in attempts to reduce the time and cost of drug development. In particular, with the recent development of deep learning techniques, various generation models for generating the chemical formulas of candidate compounds and reinforcement learning models to generate chemical formulas that satisfy specific conditions have been presented. In this paper, we propose a reinforcement learning model that exploits predicted binding affinity information between specific proteins and generated compounds. More specifically, the generative model used in this paper is Stack-RNN, and reinforcement learning is implemented by using Stack-RNN as a policy to ensure that the generated formula has specific chemical properties and high binding affinity with specific proteins. The proposed model generates paper, we generated the chemical formulas of compounds that are similar to three anti-cancer drugs (Sorafenib, Sunitinib, and Dasatinib) by using the target protein information of these three anti-cancer drugs.