Abstract
AbstractIn the fields of drug discovery and development, machine learning algorithms have been used for the development of novel drug candidates. The methods for designing drug targets and novel drug discovery now routinely combine machine learning and deep learning algorithms to enhance the efficiency, efficacy, and quality of developed outputs. The generation and incorporation of big data, through technologies such as high-throughput screening and high through-put computational analysis of databases used for both lead and target discovery, has increased the reliability of the machine learning and deep learning incorporated Algorithms. In this work, the applications that produce promising results and methods will be reviewed. The use of virtual screening and encompassing online information has also been highlighted in developing lead synthesis pathways.
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