Using computers to ESKAPE the antibiotic resistance crisis. Issue 2 (February 2022)

• Record Type: Journal Article
• Authors: da Silva, Thiago H.; Hachigian, Timothy Z.; Lee, Jeunghoon; King, Matthew D.
• Is Part Of: Drug discovery today. Volume 27:Issue 2(2022)
• Page Start: 456
• Page End: 470
• Publication Date: 2022-02
• Subjects: Computer-aided drug design -- CADD -- Quantum computing -- Antibiotic resistance -- QSAR -- Artificial intelligence; Drugs -- Design -- Periodicals; Drugs -- Research -- Periodicals; 615.1
• DOI: 10.1016/j.drudis.2021.10.005
• Languages: English
• ISSNs: 1359-6446

Abstract

Abstract : Since the discovery of penicillin, the development and use of antibiotics have promoted safe and effective control of bacterial infections. However, the number of antibiotic-resistance cases has been ever increasing over time. Thus, the drug discovery process demands fast, efficient and cost-effective alternative approaches for developing lead candidates with outstanding performance. Computational approaches are appealing techniques to develop lead candidates in an in silico fashion. In this review, we provide an overview of the implementation of current in silico state-of-the-art techniques, including machine learning (ML) and deep learning (DL), in drug discovery. We also discuss the development of quantum computing and its potential benefits for antibiotics research and current bottlenecks that limit computational drug discovery advancement.