计算机科学
工作流程
药物开发
药物发现
人工智能
药品
机器学习
风险分析(工程)
制药工业
生化工程
工程类
医学
药理学
生物信息学
数据库
生物
作者
Pauric Bannigan,Matteo Aldeghi,Zeqing Bao,Florian Häse,Alán Aspuru‐Guzik,Christine Allen
标识
DOI:10.1016/j.addr.2021.05.016
摘要
Machine learning (ML) has enabled ground-breaking advances in the healthcare and pharmaceutical sectors, from improvements in cancer diagnosis, to the identification of novel drugs and drug targets as well as protein structure prediction. Drug formulation is an essential stage in the discovery and development of new medicines. Through the design of drug formulations, pharmaceutical scientists can engineer important properties of new medicines, such as improved bioavailability and targeted delivery. The traditional approach to drug formulation development relies on iterative trial-and-error, requiring a large number of resource-intensive and time-consuming in vitro and in vivo experiments. This review introduces the basic concepts of ML-directed workflows and discusses how these tools can be used to aid in the development of various types of drug formulations. ML-directed drug formulation development offers unparalleled opportunities to fast-track development efforts, uncover new materials, innovative formulations, and generate new knowledge in drug formulation science. The review also highlights the latest artificial intelligence (AI) technologies, such as generative models, Bayesian deep learning, reinforcement learning, and self-driving laboratories, which have been gaining momentum in drug discovery and chemistry and have potential in drug formulation development.
科研通智能强力驱动
Strongly Powered by AbleSci AI