Department of Biotechnology, Indian Institute of Technology Roorkee, Roorkee 247667, India.
Curr Top Med Chem. 2018;18(20):1804-1826. doi: 10.2174/1568026619666181120150938.
The intertwining of chemoinformatics with artificial intelligence (AI) has given a tremendous fillip to the field of drug discovery. With the rapid growth of chemical data from high throughput screening and combinatorial synthesis, AI has become an indispensable tool for drug designers to mine chemical information from large compound databases for developing drugs at a much faster rate as never before. The applications of AI have gone beyond bioactivity predictions and have shown promise in addressing diverse problems in drug discovery like de novo molecular design, synthesis prediction and biological image analysis. In this article, we provide an overview of all the algorithms under the umbrella of AI, enlist the tools/frameworks required for implementing these algorithms as well as present a compendium of web servers, databases and open-source platforms implicated in drug discovery, Quantitative Structure-Activity Relationship (QSAR), data mining, solvation free energy and molecular graph mining.
化学生物信息学与人工智能 (AI) 的交织为药物发现领域带来了巨大的推动。随着高通量筛选和组合合成产生的化学数据的快速增长，AI 已成为药物设计师从大型化合物数据库中挖掘化学信息以以前所未有的速度开发药物的不可或缺的工具。AI 的应用已经超越了生物活性预测，并在解决药物发现中的各种问题方面显示出了希望，如从头分子设计、合成预测和生物图像分析。在本文中，我们概述了 AI 下的所有算法，列出了实现这些算法所需的工具/框架，并介绍了与药物发现、定量构效关系 (QSAR)、数据挖掘、溶剂化自由能和分子图挖掘相关的网络服务器、数据库和开源平台。
