A Survey on Evolutionary Computation-Based Drug Discovery

Qiyuan Yu, Qiuzhen Lin, Junkai Ji, Wei Zhou, Shan He, Zexuan Zhu, Kay Chen Tan

Abstract: Drug discovery is an expensive and risky process. To combat the challenges in drug discovery, an increasing number of researchers and pharmaceutical companies recognize the benefits of utilizing computational techniques. Evolutionary computation (EC) offers promise as most drug discovery problems are essentially complex optimization problems beyond conventional optimization algorithms. EC methods have been widely applied to solve these complex optimization problems especially in lead compound generation and molecular virtual evaluation, substantially speeding up the process of drug discovery and development. This article presents a comprehensive survey of EC-based drug discovery methods. Particularly, a new taxonomy of the methods is provided and the advantages and limitations of the methods are reviewed. In addition, the potential future directions of EC-based drug discovery are discussed and the publicly available resources, including databases and computational tools are compiled for the convenience of researchers seeking to pursue this field.

External IDs:doi:10.1109/tevc.2024.3382145