Quantum-mechanics methodologies in drug discovery: applications of docking and scoring in lead optimization.
Abstract
The development and application of quantum mechanics (QM) methodologies in computer- aided drug design have flourished in the last 10 years. Despite the natural advantage of QM methods to predict binding affinities with a higher level of theory than those methods based on molecular mechanics (MM), there are only a few examples where diverse sets of protein-ligand targets have been evaluated simultaneously. In this work, we review recent advances in QM docking and scoring for those cases in which a systematic analysis has been performed. In addition, we introduce and validate a simplified QM/MM expression to compute protein-ligand binding energies. Overall, QMbased scoring functions are generally better to predict ligand affinities than those based on classical mechanics. However, the agreement between experimental activities and calculated binding energies is highly dependent on the specific chemical series considered. The advantage of more accurate QM methods is evident in cases where charge transfer and polarization effects are important, for example when metals are involved in the binding process or when dispersion forces play a significant role as in the case of hydrophobic or stacking interactions.
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Advances in QM methods were essential to overcome the bottleneck of high computational cost and are allowing increasing use of QM methods in the prediction of protein-ligand binding affinities.
“These advances were essential to overcome the bottleneck of the high computational cost and are allowing the increasing use of QM methods in the prediction of protein–ligand binding affinities (Crespo et al., 2017).”
From Empirical scoring functions for structure-based virtual screening: applications, critical aspects, and challenges (2018) · cites this paper as Crespo et al., 2017
The use of quantum mechanical methods can improve the description of protein-ligand interactions and, in principle, could provide a more accurate binding affinity.
“The use of quantum mechanical methods can improve the description of protein–ligand interactions and, in principle, could provide a more accurate binding affinity (Raha and Merz, 2005; Chaskar et al., 2017; Crespo et al., 2017; Cavasotto et al., 2018).”
From Empirical scoring functions for structure-based virtual screening: applications, critical aspects, and challenges (2018) · cites this paper as Crespo et al., 2017
QM-based approaches still face the same problems associated with the correct estimation of solvent and other entropic effects to the protein-ligand binding free energy.
“and they still face the same problems associated with the correct estimation of the solvent and other entropic effects to the protein–ligand binding free energy.”
From Empirical scoring functions for structure-based virtual screening: applications, critical aspects, and challenges (2018) · cites this paper as Crespo et al., 2017
It is not guaranteed that QM-based approaches will always outperform standard scoring functions.
“Nevertheless, it is important to note that it is not guaranteed that QM-based approaches will always outperform standard scoring functions (Crespo et al., 2017)”
From Empirical scoring functions for structure-based virtual screening: applications, critical aspects, and challenges (2018) · cites this paper as Crespo et al., 2017
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- Authors
- Crespo, A., Rodriguez-Granillo, A., & Lim, V. T.
- Affiliations
- Merck & Co., Inc., Rahway, NJ, USA (United States) (US)
- University of California, Irvine (US)
- Corresponding author
- Crespo, Alejandro
- Venue
- Current Topics in Medicinal Chemistry
- Publisher
- Bentham Science Publishers Ltd.
- Published
- 2017-08-08
- Volume
- 17
- Issue
- 23
- Pages
- 2663-2680
- ISSN
- 1568-0266 (ISSN-L)
- OpenAlex
- W2727116964
- Cited by
- 34
- References
- 0
- Citations by year
- 2025: 2 · 2024: 4 · 2023: 1 · 2022: 1 · 2021: 3 · 2020: 9 · 2019: 10 · 2018: 2 · 2017: 2
- FWCI
- 4.72
- Type
- journal article
- Language
- en
- Open access
- closed
- Locations
- Current Topics in Medicinal Chemistry (publishedVersion)
- PubMed (publishedVersion)
- Topics
- Physical Sciences › Computer Science › Computational Theory and Mathematics › Computational Drug Discovery Methods
- Physical Sciences › Physics and Astronomy › Atomic and Molecular Physics, and Optics › Advanced Chemical Physics Studies
- Life Sciences › Biochemistry, Genetics and Molecular Biology › Molecular Biology › Protein Interaction Studies and Fluorescence Analysis
- Keywords
- quantum mechanics, protein-ligand binding affinity prediction, QM/MM, binding energy calculation, lead optimization, charge transfer, polarization effects, metal-ligand binding, dispersion interactions, hydrophobic interactions, stacking interactions, computer-aided drug design
- MeSH terms
- Ligands, Proteins, Quantum Theory, Drug Discovery, Molecular Docking Simulation
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