Protein–Ligand Binding Free Energy Calculations with FEP+

Wang, Lingle; Chambers, Jennifer M.; Abel, Robert

doi:10.1007/978-1-4939-9608-7_9

Cited by 69 publications

(66 citation statements)

References 64 publications

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“…It has been shown that highly accurate binding affinity estimates can be obtained with alchemical free energy methods when reference structures with experimentally determined binding affinities within a congeneric ligand series are available. 19 − 21 …”

Section: Resultsmentioning

confidence: 99%

General Purpose Structure-Based Drug Discovery Neural Network Score Functions with Human-Interpretable Pharmacophore Maps

Brown

Mendenhall

Geanes

et al. 2021

J. Chem. Inf. Model.

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The BioChemical Library (BCL) is an academic open-source cheminformatics toolkit comprising ligand-based virtual high-throughput screening (vHTS) tools such as quantitative structure–activity/property relationship (QSAR/QSPR) modeling, small molecule flexible alignment, small molecule conformer generation, and more. Here, we expand the capabilities of the BCL to include structure-based virtual screening. We introduce two new score functions, BCL-AffinityNet and BCL-DockANNScore, based on novel distance-dependent signed protein–ligand atomic property correlations. Both metrics are conventional feed-forward dropout neural networks trained on the new descriptors. We demonstrate that BCL-AffinityNet is one of the top performing score functions on the comparative assessment of score functions 2016 affinity prediction and affinity ranking tasks. We also demonstrate that BCL-AffinityNet performs well on the CSAR-NRC HiQ I and II test sets. Furthermore, we demonstrate that BCL-DockANNScore is competitive with multiple state-of-the-art methods on the docking power and screening power tasks. Finally, we show how our models can be decomposed into human-interpretable pharmacophore maps to aid in hit/lead optimization. Altogether, our results expand the utility of the BCL for structure-based scoring to aid small molecule discovery and design. BCL-AffinityNet, BCL-DockANNScore, and the pharmacophore mapping application, as well as the remainder of the BCL cheminformatics toolkit, are freely available with an academic license at the BCL Commons site hosted on .

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Section: Resultsmentioning

confidence: 99%

General Purpose Structure-Based Drug Discovery Neural Network Score Functions with Human-Interpretable Pharmacophore Maps

Brown

Mendenhall

Geanes

et al. 2021

J. Chem. Inf. Model.

View full text Add to dashboard Cite

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“…MM force fields are widely used in structure-enabled drug discovery Alchemical free energy calculations are now widely used in structure-enabled drug discovery programs to optimize or maintain potency [1][2][3][4][5]. Typically, relative alchemical free energy methods can predict affinities with accuracies of 1-2 kcal mol −1 in prospective use in well-behaved, structure-enabled programs [3,6].…”

Section: Introductionmentioning

confidence: 99%

Towards chemical accuracy for alchemical free energy calculations with hybrid physics-based machine learning / molecular mechanics potentials

Rufa

Macdonald

Fass

et al. 2020

Preprint

106

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Alchemical free energy methods with molecular mechanics (MM) force fields are now widely used in the prioritization of small molecules for synthesis in structure-enabled drug discovery projects because of their ability to deliver 1–2 kcal mol−1 accuracy in well-behaved protein-ligand systems. Surpassing this accuracy limit would significantly reduce the number of compounds that must be synthesized to achieve desired potencies and selectivities in drug design campaigns. However, MM force fields pose a challenge to achieving higher accuracy due to their inability to capture the intricate atomic interactions of the physical systems they model. A major limitation is the accuracy with which ligand intramolecular energetics—especially torsions—can be modeled, as poor modeling of torsional profiles and coupling with other valence degrees of freedom can have a significant impact on binding free energies. Here, we demonstrate how a new generation of hybrid machine learning / molecular mechanics (ML/MM) potentials can deliver significant accuracy improvements in modeling protein-ligand binding affinities. Using a nonequilibrium perturbation approach, we can correct a standard, GPU-accelerated MM alchemical free energy calculation in a simple post-processing step to efficiently recover ML/MM free energies and deliver a significant accuracy improvement with small additional computational effort. To demonstrate the utility of ML/MM free energy calculations, we apply this approach to a benchmark system for predicting kinase:inhibitor binding affinities—a congeneric ligand series for non-receptor tyrosine kinase TYK2 (Tyk2)—wherein state-of-the-art MM free energy calculations (with OPLS2.1) achieve inaccuracies of 0.93±0.12 kcal mol−1 in predicting absolute binding free energies. Applying an ML/MM hybrid potential based on the ANI2x ML model and AMBER14SB/TIP3P with the OpenFF 1.0.0 (“Parsley”) small molecule force field as an MM model, we show that it is possible to significantly reduce the error in absolute binding free energies from 0.97 [95% CI: 0.68, 1.21] kcal mol−1 (MM) to 0.47 [95% CI: 0.31, 0.63] kcal mol−1 (ML/MM).

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“…Although the available scoring functions are able to suggest the most reliable binding orientation for a ligand, their ability to predict ligand-receptor binding affinity and correctly rank different ligands on this basis is often not satisfactory enough. Among the different approaches that can be applied to evaluate the ligand-receptor interaction, the accurate measurement of binding free energy plays a very important role and, in this field, the most rigorous theoretical method is represented by freeenergy perturbation (FEP), which estimates the difference in free energy between two states by slowly changing one state to another through a number of nonphysical intermediate states, performing extended sampling at each intermediate state [1]. Unfortunately, this method is extremely time consuming and therefore more approximate methods have been developed.…”

Section: Introductionmentioning

confidence: 99%