Artificial learning and machine learning is playing a pivotal role is the society especially in the field of medicinal chemistry and drug discovery. Particularly its algorithms, neural networks or other recurrent networks drive this area. In this review, we have taken into account the diverse use of AI in a number of pharmaceutical industries including discovery of drugs, repurposing, development of pharmaceutical drug and its clinical trials. In addition, the efficiency of these artificial or machine learning programs in achieving the target drugs in short time period, along with accurate dosage and cost effectively of the drug has also been discussed. Numerous applications of AI in property prediction such as ADMET have been used for prediction of strength of this technology in QSAR. In case of de-novo synthesis, it results in generation of novel drug molecules with unique design proving this a promising field fir drug design. Moreover, its involvement in synthetic planning, ease of synthesis and much more contribute to automated drug discovery in near future.

Artificial learning and machine learning are playing pivotal roles in the society, especially in the field of medicinal chemistry and drug discovery. Particularly its algorithms, neural networks, or other recurrent networks drive this area. In this review, the authors have taken into account the diverse use of AI in a number of pharmaceutical industries including discovery of drugs, repurposing, development of pharmaceutical drugs, and clinical trials. In addition, the efficiency of these artificial or machine learning programs in achieving the target drugs in short time period along with accurate dosage and cost of the drug have also been discussed. Numerous applications of AI in property prediction such as ADMET have been used for prediction of strength of this technology in QSAR. In case of de-novo synthesis, it results in generation of novel drug molecules with unique design making this a promising field for drug design. Moreover, its involvement in synthetic planning, ease of synthesis, and much more contribute to automated drug discovery in the near future.