De novo drug design based on Stack-RNN with multi-objective reward-weighted sum and reinforcement learning.

Journal: Journal of molecular modeling
Published Date: Mar 30, 2023

Abstract

CONTEXT: In recent decades, drug development has become extremely important as different new diseases have emerged. However, drug discovery is a long and complex process with a very low success rate, and methods are needed to improve the efficiency of the process and reduce the possibility of failure. Among them, drug design from scratch has become a promising approach. Molecules are generated from scratch, reducing the reliance on trial and error and prefabricated molecular repositories, but the optimization of its molecular properties is still a challenging multi-objective optimization problem.

Authors

  • Pengwei Hu
    The Xinjiang Technical Institute of Physics and Chemistry, Chinese Academy of Sciences, Ürümqi, China.
  • Jinping Zou
    Department of Mathematics, School of Mathematics and Computer Sciences, Nanchang University, Nanchang, 330031, China.
  • Jialin Yu
    Department of Mathematics and Numerical Simulation and High-Performance Computing Laboratory, School of Sciences, Nanchang University, Nanchang 330031, China.
  • Shaoping Shi
    Department of Mathematics and Numerical Simulation and High-Performance Computing Laboratory, School of Sciences, Nanchang University, Nanchang 330031, China. Electronic address: shishaoping@ncu.edu.cn.

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