Data Processing Method for AI‐Driven Predictive Models for CNS Drug Discovery

药物发现 药品 计算机科学 人工智能 机器学习 计算生物学 数据科学 医学 生物信息学 药理学 生物
作者
Ajantha Devi Vairamani,Sudipta Adhikary,Kaushik Banerjee
标识
DOI:10.1002/9781394234196.ch8
摘要

In the challenging field of central nervous system (CNS) drug discovery, machine learning (ML) and artificial intelligence (AI) have recently come into their own as potent tools. This industry is infamous for its lengthy lead times and high failure rates. However, the combination of AI/ML and contemporary experimental technology has unlocked the potential to fundamentally alter how CNS illness treatments are created. Biomedical data's rapid expansion has made it possible for AI/ML-driven solutions to flourish. This chapter explores the revolutionary effects of AI/ML on the creation of CNS medications, demonstrates how AI/ML might quicken the creation of effective treatments for neurological diseases, particularly when it comes to predicting blood–brain barrier permeability, a key factor in medication development and shed insights on the current state of AI/ML-driven CNS drug discovery and its potential to address current methodological issues. The development of CNS drugs can be made much more successful and efficient by utilizing AI/ML, giving patients suffering from crippling neurological illnesses hope. The chapter concludes by highlighting the impressive advancements made in the discovery of CNS medicine powered by AI and ML and looks ahead to a bright future. Utilizing AI/ML capabilities, pharmaceutical companies and researchers are collaborating to make ground-breaking improvements that will give patients fresh hope.

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