药物发现
制药工业
背景(考古学)
计算机科学
过程(计算)
普通合伙企业
数据科学
管道(软件)
人工智能
管理科学
风险分析(工程)
业务
工程类
生物技术
生物信息学
古生物学
财务
生物
程序设计语言
操作系统
作者
Claudio N. Cavasotto,Juan I. Di Filippo
标识
DOI:10.1016/j.abb.2020.108730
摘要
Although the use of computational methods within the pharmaceutical industry is well established, there is an urgent need for new approaches that can improve and optimize the pipeline of drug discovery and development. In spite of the fact that there is no unique solution for this need for innovation, there has recently been a strong interest in the use of Artificial Intelligence for this purpose. As a matter of fact, not only there have been major contributions from the scientific community in this respect, but there has also been a growing partnership between the pharmaceutical industry and Artificial Intelligence companies. Beyond these contributions and efforts there is an underlying question, which we intend to discuss in this review: can the intrinsic difficulties within the drug discovery process be overcome with the implementation of Artificial Intelligence? While this is an open question, in this work we will focus on the advantages that these algorithms provide over the traditional methods in the context of early drug discovery.
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