A Comprehensive Review on Machine Learning and Deep Learning Methods in Drug Discovery

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Sneha Khaire
Pawan Bhaladhare


Due to its ability to drastically cut the time and money required to develop new medicines, artificial intelligence (AI) based drug discovery has recently attracted a lot of attention. The fields of drug research and development have made use of machine learning (ML) and deep learning (DL) technologies to develop new medication prospects. Machine learning and deep learning-based techniques are emerging at every level of the drug development process as a result of the proliferation of drug-related data. Preclinical testing of a target of interest has proven to be particularly tough for pharmaceutical chemists, who face significant challenges in selecting and developing effective drugs. Machine learning and deep learning algorithms are now extensively used in approaches for generating therapeutic targets and innovative medication development in order to increase the accuracy, efficiency, and quality of created outputs. This review focuses on the application of machine learning and deep learning algorithms in drug development, as well as related approaches. We'll look at the approaches and methods that seem most promising in terms of their potential impact.

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How to Cite
Khaire, S. ., & Bhaladhare, P. . (2022). A Comprehensive Review on Machine Learning and Deep Learning Methods in Drug Discovery. International Journal on Recent and Innovation Trends in Computing and Communication, 10(10), 01–08. https://doi.org/10.17762/ijritcc.v10i10.5728


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