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: Chemoinformatics, Drug discovery, Artificial intelligence, Machine learning, Deep learning, QSAR analysis,
Generative models, Data/graph mining.
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