Selective and Effective: Current Progress in Computational Structure-Based Drug Discovery of Targeted Covalent Inhibitors. (December 2020)

• Record Type: Journal Article
• Authors: Bianco, Giulia; Goodsell, David S.; Forli, Stefano
• Is Part Of: Trends in pharmacological sciences. Volume 41:Number 12(2020)
• Page Start: 1038
• Page End: 1049
• Publication Date: 2020-12
• Subjects: covalent docking -- structure-based drug design -- covalent virtual screening -- irreversible binding -- TCI; Pharmacology -- Periodicals; Pharmacology -- trends -- Periodicals; Pharmacologie -- Périodiques; Pharmacology; Electronic journals; Periodicals; 615.1
• DOI: 10.1016/j.tips.2020.10.005
• Languages: English
• ISSNs: 0165-6147

Abstract

Abstract : Targeted covalent inhibitors are currently showing great promise for systems that are normally difficult to target with small molecule therapies. This renewed interest has spurred the refinement of existing computational methods as well as the design of new ones, expanding the toolbox for discovery and optimization of selective and effective covalent inhibitors. Commonly applied approaches are covalent docking methods that predict the conformation of the covalent complex with known residues. More recently, a new predictive method, reactive docking, was developed, building on the growing corpus of data generated by large proteomics experiments. This method was successfully used in several 'inverse drug discovery' programs that use high-throughput techniques to isolate effective compounds based on screening of entire compound libraries based on desired phenotypes. Highlights: Molecular modeling is widely applied in drug discovery, with a number of successful approaches. Only recently have there been applications to the characterization of covalent inhibitors. Several methods have been proposed, but a number of challenges arise from the modeling of the warhead reactivity, prediction of residue propensity to react, and, ultimately, the prediction of reaction outcome. The recent resurgence of interest in covalent inhibitors is generating large amounts of experimental data that can be used to train computational models and improve the accuracy of predictive methods. In order to be useful in drug discovery campaigns, computational methods need to achieve an ideal balance between accuracy and speed, in order to reduce experimental resources required to identify new hits while being applicable in high-throughput fashion.