Artificial intelligence in virtual screening: Models versus experiments. Issue 7 (July 2022)

• Record Type: Journal Article
• Authors: Arul Murugan, N.; Ruba Priya, Gnana; Narahari Sastry, G.; Markidis, Stefano
• Is Part Of: Drug discovery today. Volume 27:Issue 7(2022)
• Page Start: 1913
• Page End: 1923
• Publication Date: 2022-07
• Subjects: Computational drug discovery -- Scoring functions -- Machine learning-based scoring -- Binding affinity -- Binding assay studies -- Chemical spaces; Drugs -- Design -- Periodicals; Drugs -- Research -- Periodicals; 615.1
• DOI: 10.1016/j.drudis.2022.05.013
• Languages: English
• ISSNs: 1359-6446

Abstract

Graphical abstract: Highlight: Drug discovery projects can be benefited from the machine learning and deep learning based scoring functions. The current review highlights various success stories in the identification of lead compounds using such scoring functions which are verified from experimental bioassay and characterization studies. Abstract: A typical drug discovery project involves identifying active compounds with significant binding potential for selected disease-specific targets. Experimental high-throughput screening (HTS) is a traditional approach to drug discovery, but is expensive and time-consuming when dealing with huge chemical libraries with billions of compounds. The search space can be narrowed down with the use of reliable computational screening approaches. In this review, we focus on various machine-learning (ML) and deep-learning (DL)-based scoring functions developed for solving classification and ranking problems in drug discovery. We highlight studies in which ML and DL models were successfully deployed to identify lead compounds for which the experimental validations are available from bioassay studies.