Artificial intelligence in small molecule drug discovery from 2018 to 2023: Does it really work?

Journal: Bioorganic chemistry
Published Date:

Abstract

Utilizing artificial intelligence (AI) in drug design represents an advanced approach for identifying targets and developing new drugs. Integrating AI techniques significantly reduces the workload involved in drug development and enhances the efficiency of early-stage drug discovery. This review aims to present a comprehensive overview of the utilization of AI methods in the field of small drug design, with a specific focus on four key areas: protein structure prediction, molecular virtual screening, molecular design, and absorption, distribution, metabolism, excretion, and toxicity (ADMET) prediction. Additionally, the role and limitations of AI in drug development are explored, and the impact of AI on decision-making processes is studied. It is important to note that while AI can bring numerous benefits to the early stage of drug development, the direction and quality of decision-making should still be emphasized, as AI should be considered as a tool rather than a decisive factor.

Authors

  • Qi Lv
    National Laboratory for Parallel and Distributed Processing, National University of Defense Technology, Changsha, China.
  • Feilong Zhou
    School of Pharmacy, Inflammation and Immune Mediated Diseases Laboratory of Anhui Province, Hefei 230032, PR China.
  • Xinhua Liu
    School of Mechatronic Engineering, China University of Mining & Technology, Xuzhou 221116, China ; Xuyi Mine Equipment and Materials R&D Center, China University of Mining & Technology, Huai'an 211700, China.
  • Liping Zhi
    School of Health Management, Anhui Medical University Hefei, 230032, PR China. Electronic address: 2500788327@qq.com.