Artificial intelligence and machine-learning approaches in structure and ligand-based discovery of drugs affecting central nervous system

• Main Authors: Gautam, Vertika, Gaurav, Anand, Masand, Neeraj, Lee, Vannajan Sanghiran, Patil, Vaishali M.
• Format: Article
• Published: Kluwer (now part of Springer) 2023
• Subjects: QD Chemistry; QH301 Biology; RS Pharmacy and materia medica
• Online Access: http://eprints.um.edu.my/39513/

CNS disorders are indications with a very high unmet medical needs, relatively smaller number of available drugs, and a subpar satisfaction level among patients and caregiver. Discovery of CNS drugs is extremely expensive affair with its own unique challenges leading to extremely high attrition rates and low efficiency. With explosion of data in information age, there is hardly any aspect of life that has not been touched by data driven technologies such as artificial intelligence (AI) and machine learning (ML). Drug discovery is no exception, emergence of big data via genomic, proteomic, biological, and chemical technologies has driven pharmaceutical giants to collaborate with AI oriented companies to revolutionise drug discovery, with the goal of increasing the efficiency of the process. In recent years many examples of innovative applications of AI and ML techniques in CNS drug discovery has been reported. Research on therapeutics for diseases such as schizophrenia, Alzheimer's and Parkinsonism has been provided with a new direction and thrust from these developments. AI and ML has been applied to both ligand-based and structure-based drug discovery and design of CNS therapeutics. In this review, we have summarised the general aspects of AI and ML from the perspective of drug discovery followed by a comprehensive coverage of the recent developments in the applications of AI/ML techniques in CNS drug discovery. GRAPHICS] .