Large-Scale Assessment of Binding Free Energy Calculations in Active Drug Discovery Projects

Schindler, Christina; Baumann, Hannah; Blum, Andreas; Böse, Dietrich; Buchstaller, Hans‐Peter; Burgdorf, Lars T.; Cappel, Daniel; Chekler, Eugene L. Piatnitski; Czodrowski, Paul; Dorsch, Dieter; Eguida, Merveille; Follows, Bruce; Fuchß, Thomas; Grädler, Ulrich; Gunera, Jakub; Johnson, Theresa; Lebrun, Catherine Jorand; Karra, Srinivasa; Klein, Markus; Kötzner, Lisa; Knehans, Tim; Krier, Mireille; Leiendecker, Matthias; Leuthner, Birgitta; Li, Liwei; Mochalkin, Igor; Müsil, Djordje; Neagu, Constantin; Rippmann, Friedrich; Schiemann, Kai; Schulz, Robert; Steinbrecher, Thomas; Tanzer, Eva‐Maria; Lopez, Andrea Unzue; Follis, Ariele Viacava; Wegener, Ansgar; Kühn, Daniel

doi:10.26434/chemrxiv.11364884

Cited by 63 publications

(105 citation statements)

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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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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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“…Using HREX in the EQ approach did not enhance the sampling sufficiently ( Figure 20, green line), and other enhanced sampling methods, like GCMC, are likely necessary here 64 unless far longer simulations are used, which would raise computational costs far beyond what is currently standard for these calculations. 5,6,65 Interestingly, inserting the ligand with NEQ transitions "finds" these three water molecules in exactly the same positions as in the crystal structure. 5.6.…”

Section: 5mentioning

confidence: 72%

“…Although all these methods are routinely applied in drug discovery projects, improvements in their accuracy are still needed to increase their impact. [3][4][5][6] The binding free energy, ∆G • , is defined as the difference between the free energy of the ligand in solution at a standard reference concentration, and the free energy of the ligand bound to the protein. This property can be calculated in a variety of ways, but two -which we consider here -are becoming relatively more common.…”

Section: Introductionmentioning

confidence: 99%

Challenges Encountered Applying Equilibrium and Non-Equilibrium Binding Free Energy Calculations

Baumann¹,

Gapsys²,

Groot³

et al. 2021

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<div>Binding free energy calculations have become increasingly valuable to drive decision making in drug discovery projects. </div><div>However, among other issues, inadequate sampling can reduce accuracy, limiting the value of the technique.</div><div>In this paper we apply absolute binding free energy calculations to ligands binding to T4 lysozyme L99A and HSP90 using equilibrium and non-equilibrium approaches. We highlight sampling problems encountered in these systems, such as slow side chain rearrangements and slow changes of water placement upon ligand binding. These same types of challenges are likely to show up in other protein-ligand systems as well and we propose some strategies to diagnose and test for such problems in alchemical free energy calculations. We also explore similarities and differences in how the equilibrium and the non-equilibrium approaches handle these problems. Our results show the large amount of work still to be done to make free energy calculations robust and reliable and provide insight for future research in this area. </div>

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“…Furthermore, validation of the method will be extended considering another recently published large dataset for free energy calculations. 72 ASSOCIATED CONTENT Comparison of the results considering the perturbations run in both directions ("all", as described in the manuscript) or just the growing ("S2L") or shrinking ("L2S") perturbations. Results of the free energy calculations.…”

Section: Resultsmentioning

confidence: 99%

Automated Assessment of Binding Affinity via Alchemical Free Energy Calculations

Kühn¹,

Firth-Clark²,

Tosco³

et al. 2020

Preprint

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Free energy calculations have seen increased usage in structure-based drug design. Despite the rising interest, automation of the complex calculations and subsequent analysis of their results are still hampered by the restricted choice of available tools. In this work, an application for automated setup and processing of free energy calculations is presented. Several sanity checks for assessing the reliability of the calculations were implemented, constituting a distinct advantage over existing open-source tools. The underlying workflow is built on top of the software Sire, SOMD, BioSimSpace and OpenMM and uses the AMBER14SB and GAFF2.1 force fields. It was validated on two datasets originally composed by Schrödinger, consisting of 14 protein structures and 220 ligands. Predicted binding affinities were in good agreement with experimental values. For the larger dataset the average correlation coefficient Rp was 0.70 ± 0.05 and average Kendall’s τ was 0.53 ± 0.05 which is broadly comparable to or better than previously reported results using other methods. <br>

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Large-Scale Assessment of Binding Free Energy Calculations in Active Drug Discovery Projects

Abstract: Here we present an evaluation of the binding affinity prediction accuracy of the free energy calculation method FEP+ on internal active drug discovery projects and on a large new public benchmark set.<br>

Cited by 63 publications

References 1 publication

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

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

Challenges Encountered Applying Equilibrium and Non-Equilibrium Binding Free Energy Calculations

Automated Assessment of Binding Affinity via Alchemical Free Energy Calculations

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