Machine Learning Methods in Drug Discovery

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.

Machine learning for perturbational single-cell omics

“…There exists a large body of literature that addresses the above modeling objectives with both systems biology and DL approaches applied on bulk epigenomics, transcriptomics, and proteomics, small molecule phenotypic screens, in vitro cell line characterizations, and clinical measurements, which is reviewed, among others, by Patel et al (2020) and Camacho et al (2018). In the context of the more recently developed single-cell genomics and mass cytometry methods, the literature is relatively scarce.…”

Applications of Virtual Screening in Bioprospecting: Facts, Shifts, and Perspectives to Explore the Chemo-Structural Diversity of Natural Products

“…In contrast, the unsupervised algorithms recognize patterns in the dataset of compounds without the presence of inactive ones, thus trying to organize the data in a logical form. These methods have been used for exploratory analyses using clustering data (Patel et al, 2020 ). Unsupervised algorithms include clustering methods, such as the hidden Markov model, hierarchical clustering, and k -means (Vamathevan et al, 2019 ).…”

Deep Learning Approach for Discovery of In Silico Drugs for Combating COVID-19

“…In [ 21 ], the authors have built a multitask DNN model and compared the results with a single-task DNN model. In [ 22 ], various machine learning and deep learning algorithms used for drug discovery are reviewed, and their applications were discussed. However, various studies suggest deep learning for drug discovery or detecting COVID-19 lacks proper practical implementation with results.…”