Pérez-Sianes Javier, Pérez-Sánchez Horacio, Díaz Fernando
Departamento de Informática, University of Valladolid, Valladolid, Spain.
Bioinformatics and High Performance Computing Research Group (BIO-HPC), Computer Engineering Department, Universidad Católica San Antonio de Murcia (UCAM), Murcia, Spain.
Curr Comput Aided Drug Des. 2019;15(1):6-28. doi: 10.2174/1573409914666181018141602.
Automated compound testing is currently the de facto standard method for drug screening, but it has not brought the great increase in the number of new drugs that was expected. Computer- aided compounds search, known as Virtual Screening, has shown the benefits to this field as a complement or even alternative to the robotic drug discovery. There are different methods and approaches to address this problem and most of them are often included in one of the main screening strategies. Machine learning, however, has established itself as a virtual screening methodology in its own right and it may grow in popularity with the new trends on artificial intelligence.
This paper will attempt to provide a comprehensive and structured review that collects the most important proposals made so far in this area of research. Particular attention is given to some recent developments carried out in the machine learning field: the deep learning approach, which is pointed out as a future key player in the virtual screening landscape.
自动化化合物测试目前是药物筛选的实际标准方法，但它并未带来预期的新药数量大幅增加。计算机辅助化合物搜索，即虚拟筛选，已显示出作为机器人药物发现的补充甚至替代方法对该领域的益处。有不同的方法和途径来解决这个问题，并且它们中的大多数通常包含在主要筛选策略之一中。然而，机器学习本身已成为一种虚拟筛选方法，并且随着人工智能的新趋势，它可能会越来越受欢迎。
本文将尝试提供全面且结构化的综述，收集该研究领域迄今为止提出的最重要建议。特别关注机器学习领域的一些最新进展：深度学习方法，它被指出是虚拟筛选领域未来的关键参与者。
