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.
Keywords: Artificial intelligence; Chemoinformatics; Data/graph mining; Deep learning; Drug discovery; Generative models; Machine learning; QSAR analysis..
Copyright© Bentham Science Publishers; For any queries, please email at email@example.com.