Computer-Aided Drug Design Approaches for Benzilic Acid and Benzilate Derivatives: From Muscarinic Antagonists to Scaffold-Centred Workflows

Abstract

Benzilic acid (2-hydroxy-2,2-diphenylacetic acid) and its derivatives occupy a distinctive niche in medicinal chemistry because the scaffold naturally combines (i) a bulky, “aryl-rich” hydrophobic domain and (ii) a polar, acid/ester-bearing handle that can be tuned for permeability, residence time, and receptor selectivity. These attributes underpin clinically important antimuscarinic drugs and tool ligands whose binding modes have been structurally resolved, enabling genuinely structure-guided optimisation. In parallel, modern computer-aided drug design (CADD) has matured from “docking-first” workflows to integrated pipelines that explicitly manage protonation/tautomeric uncertainty, conformational flexibility, induced fit, binding kinetics, and developability constraints. This review synthesiseshow contemporary CADD can be applied to benzilic acid derivatives, with emphasis on (a) structure-based design empowered by high-resolution muscarinic acetylcholine receptor structures co-crystallised with benzilate-type ligands and related antagonists, (b) ligand-based modelling that leverages scaffold-centric series and robust negative data, and (c) translation-oriented in silico profiling to triage liabilities early. A structured search was conducted in PubMed, Web of Science, Scopus, and Google Scholar (January 2010 - March 2025) using specific scaffold and computational method terms, including peer-reviewed English articles involving benzilic acid derivatives and explicit CADD components.We outline a concise and reproducible literature-selection strategy, map the chemical and biological design space of benzilic acid derivatives, and present case-study patterns illustrating what has worked (and why) in achieving subtype selectivity and longer duration of action. Finally, we propose practical, reportable best practices—particularly around stereochemistry, protonation states, and benchmarking—that can raise confidence in CADD conclusions for this scaffold class and accelerate the design–make–test–analysecycle.