Machine Learning Methods in Drug Discovery
Lauv Patela, Tripti Shuklaa, Xiuzhen Huangb, David Wayne Usseryc, Shanzhi Wanga✉
- aChemistry Department, University of Arkansas at Little Rock, Little Rock, AR 72204, USA
- bDepartment of Computer Science, Arkansas State University, Jonesboro, AR 72467, USA
- cDepartment of Biomedical Informatics, University of Arkansas for Medical Sciences, Little Rock, AR 72205, USA
- ✉Corresponding author
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Abstract
The advancements of information technology and related processing techniques have created a fertile base for progress in many scientific fields and industries. In the fields of drug discovery and development, machine learning techniques 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 techniques. The use of these virtual screening and encompassing online information has also been highlighted in developing lead synthesis pathways. In this review, machine learning and deep learning algorithms utilized in drug discovery and associated techniques will be discussed. The applications that produce promising results and methods will be reviewed.
TL;DRIn this review, machine learning and deep learning algorithms utilized in drug discovery and associated techniques will be discussed and the applications that produce promising results and methods will be reviewed. (Semantic Scholar)