Recent advances in ligand-based drug design: relevance and utility of the conformationally sampled pharmacophore approach.

By Chayan Acharya, A. Coop, J. Polli, Alexander D. MacKerell. 2011

257 citations recorded by the source corpus.

In the absence of three-dimensional (3D) structures of potential drug targets, ligand-based drug design is one of the popular approaches for drug discovery and lead optimization. 3D structure-activity relationships (3D QSAR) and pharmacophore modeling are the most important and widely used tools in ligand-based drug design that can provide crucial insights into the nature of the interactions between drug target and ligand molecule and provide predictive models suitable for lead compound optimization. This review article will briefly discuss the features and potential application of recent advances in ligand-based drug design, with emphasis on a detailed description of a novel 3D QSAR method based on the conformationally sample pharmacophore (CSP) approach (denoted CSP-SAR). In addition, data from a published study is used to compare the CSP-SAR approach to the Catalyst method, emphasizing the utility of the CSP approach for ligand-based model development.

Subfields: Computational Theory and Mathematics, Organic Chemistry, Computational Chemistry & Drug Discovery

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Citations in the corpus

Molecular Docking and Structure-Based Drug Design Strategies (2015, methodology): “This type of information is used in ligand-based drug design (LBDD) methods [7].”
Computational drug discovery (2012, background): “…pharmacophore modeling, molecular field analysis and 2D or 3D similarity assessment, can provide crucial insights into the nature of the interactions between drug targets and ligands, which allows predictive models that are suitable for lead discovery and optimization to be constructed [12] .”
Computational Methods in Drug Discovery (2014, methodology): “These descriptors are generated through knowledge-based, graph-theoretical methods, molecularmechanical, or quantum-mechanical tools (Acharya et al., 2011; Marrero-Ponce et al., 2012) and are classified according to the “dimensionality” of the chemical representation from which they are computed…”
Introduction to the BioChemical Library (BCL): An Application-Based Open-Source Toolkit for Integrated Cheminformatics and Machine Learning in Computer-Aided Drug Discovery (2022, background): “…conformer generation (Cappel et al., 2015; Kothiwale et al., 2015; Friedrich et al., 2017a, 2019), pharmacophore modeling (Hecker et al., 2002; Acharya et al., 2011; Vlachakis et al., 2015), structure-based (SB) protein-ligand docking (Friesner et al., 2004; Meiler and Baker, 2006; Davis and…”
Potential of quantum computing for drug discovery (2018, methodology): “Most QSAR applications involve linear models, such as multivariable linear regression, principle component analysis, and partial least square analysis [35].”
Utilizing graph machine learning within drug discovery and development (2021, background): “In addition to such structure-based approach, fragment-based (FBDD) [171, 172] and ligand-based drug discovery (LBDD) have also been popular [173, 174].”