Molecular Generation for CNS Drug Discovery and Design.

Journal: ACS chemical neuroscience
PMID: 40080541

Abstract

Computational drug design is a rapidly evolving field, especially the latest breakthroughs in generative artificial intelligence (GenAI) to create new compounds. However, the potential of GenAI to address the challenges in designing central nervous system (CNS) drugs that can effectively cross the blood-brain barrier (BBB) and engage their targets remains largely unexplored. The integration of GenAI techniques with experimental data sets and advanced evaluation metrics provides a unique opportunity to enhance CNS drug discovery. In this viewpoint, we will introduce the definition of CNS drug-like properties and data resources in CNS drug discovery, highlighting the need to train specialized GenAI models aimed at designing novel CNS drug candidates by efficiently exploring the CNS drug-like space.

Authors

  • Shengneng Chen
    School of Pharmaceutical Sciences, Chongqing University, Chongqing 401331, China.
  • Ding Luo
    Beijing Traditional Chinese Medicine Office for Cancer Prevention and Control, Xiyuan Hospital, China Academy of Chinese Medical Science, Beijing, China.
  • Weiwei Xue
    School of Pharmaceutical Sciences, Chongqing University, Chongqing, China.

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