An open-source molecular builder and free energy preparation workflow

Bieniek, Mateusz K.; Cree, Ben; Pirie, Rachael; Horton, Joshua; Tatum, Natalie J.; Cole, D. J. A.

doi:10.1038/s42004-022-00754-9

Cited by 6 publications

(4 citation statements)

References 81 publications

(92 reference statements)

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“…10 The mean unsigned error (MUE) between experimental and computed ΔΔG b was 0.45 kcal/mol, and Pearson's r 2 was 0.53. 10 As is well established, 11 the ΔΔG b values obtained from such FEP calculations are accurate enough to guide the prioritization of compound synthesis in a lead series. However, accurate calculation of absolute binding free energy (ABFE), ΔG b , is desirable in many contexts in which more structurally diverse compounds are being compared such as in general screening libraries, corehopping, and de novo design.…”

Section: Introductionmentioning

confidence: 94%

“…to assess ΔΔ G b results obtained from molecular dynamics (MD) simulations with SOMD software . The mean unsigned error (MUE) between experimental and computed ΔΔ G b was 0.45 kcal/mol, and Pearson’s r 2 was 0.53 . As is well established, the ΔΔ G b values obtained from such FEP calculations are accurate enough to guide the prioritization of compound synthesis in a lead series.…”

Section: Introductionmentioning

confidence: 96%

“…2.5 kcal/mol in binding free energy, was later used by Bieniek et al. to assess ΔΔ G b results obtained from molecular dynamics (MD) simulations with SOMD software . The mean unsigned error (MUE) between experimental and computed ΔΔ G b was 0.45 kcal/mol, and Pearson’s r 2 was 0.53 .…”

Section: Introductionmentioning

confidence: 99%

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Computation of Absolute Binding Free Energies for Noncovalent Inhibitors with SARS-CoV-2 Main Protease

Ghahremanpour

Saar

Tirado‐Rives

et al. 2023

J. Chem. Inf. Model.

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Accurate, routine calculation of absolute binding free energies (ABFEs) for protein−ligand complexes remains a key goal of computer-aided drug design since it can enable screening and optimization of drug candidates. For development and testing of related methods, it is important to have high-quality datasets. To this end, from our own experimental studies, we have selected a set of 16 inhibitors of the SARS-CoV-2 main protease (M pro ) with structural diversity and well-distributed BFEs covering a 5 kcal/mol range. There is also minimal structural uncertainty since X-ray crystal structures have been deposited for 12 of the compounds. For methods testing, we report ABFE results from 2 μs molecular dynamics (MD) simulations using free energy perturbation (FEP) theory. The correlation of experimental and computed results is encouraging, with a Pearson's r 2 of 0.58 and a Kendall τ of 0.24. The results indicate that current FEP-based ABFE calculations can be used for identification of active compounds (hits). While their accuracy for lead optimization is not yet sufficient, this activity remains addressable in separate lead series by relative BFE calculations.

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Section: Introductionmentioning

confidence: 94%

Section: Introductionmentioning

confidence: 96%

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Computation of Absolute Binding Free Energies for Noncovalent Inhibitors with SARS-CoV-2 Main Protease

Ghahremanpour

Saar

Tirado‐Rives

et al. 2023

J. Chem. Inf. Model.

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“…The somd application was developed by interfacing sire's IO, energy and computer algebra functionality with GPU-accelerated MD functionality of the OpenMM library to create a single topology alchemical molecular dynamics free energy calculation engine. somd has been used extensively over the years to develop alchemical free energy calculation methodologies, and to support drug discovery projects 6,12,30,[34][35][36][37][38][39][40][41][42][43][44][45][46][47][48] .…”

Section: Scientific Applications Developed With Sirementioning

confidence: 99%

Sire: An Interoperability Engine for Prototyping Algorithms and Exchanging Information Between Molecular Simulation Programs

Woods,

Hedges,

Mulholland

et al. 2024

Preprint

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Sire is a Python/C++ library that is used to both prototype new algorithms and as an interoperability engine for exchanging information between molecular simulation programs. It provides a collection of file parsers and information converters that together make it easier to combine and leverage the functionality of many other programs and libraries. This empowers researchers to use sire to write a single script that can, for example, load a molecule from a PDBx/mmCIF file via Gemmi, perform SMARTS searches via RDKit, parameterise molecules using BioSimSpace, run GPU-accelerated molecular dynamics via OpenMM, and then display the resulting dynamics trajectory in a NGLView Jupyter notebook 3D molecular viewer. This functionality is built on by BioSimSpace, which uses sire’s molecular information engine to interconvert with programs such as GROMACS, NAMD, Amber and AmberTools, for automated molecular parameterisation and running of molecular dynamics, metadynamics and alchemical free energy workflows. Sire comes complete with a powerful molecular information search engine, plus trajectory loading and editing, analysis and energy evaluation engines. This, when combined with an in-built computer algebra system, gives substantial flexibility to researchers to load, search for, edit and combine molecular information from multiple sources, and use that to drive novel algorithms by combining functionality from other programs. Sire is open source (GPL3) and is available via conda and at a free Jupyter notebook server at https://try.openbiosim.org. Sire is supported by the not-for-profit OpenBioSim community interest company.

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SAnDReS 2.0: Development of machine‐learning models to explore the scoring function space

de Azevedo,

Quiroga,

Villarreal

et al. 2024

J Comput Chem

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Classical scoring functions may exhibit low accuracy in determining ligand binding affinity for proteins. The availability of both protein–ligand structures and affinity data make it possible to develop machine‐learning models focused on specific protein systems with superior predictive performance. Here, we report a new methodology named SAnDReS that combines AutoDock Vina 1.2 with 54 regression methods available in Scikit‐Learn to calculate binding affinity based on protein–ligand structures. This approach allows exploration of the scoring function space. SAnDReS generates machine‐learning models based on crystal, docked, and AlphaFold‐generated structures. As a proof of concept, we examine the performance of SAnDReS‐generated models in three case studies. For all three cases, our models outperformed classical scoring functions. Also, SAnDReS‐generated models showed predictive performance close to or better than other machine‐learning models such as KDEEP, CSM‐lig, and ΔVinaRF20. SAnDReS 2.0 is available to download at https://github.com/azevedolab/sandres.

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An open-source molecular builder and free energy preparation workflow

Cited by 6 publications

References 81 publications

Computation of Absolute Binding Free Energies for Noncovalent Inhibitors with SARS-CoV-2 Main Protease

Computation of Absolute Binding Free Energies for Noncovalent Inhibitors with SARS-CoV-2 Main Protease

Sire: An Interoperability Engine for Prototyping Algorithms and Exchanging Information Between Molecular Simulation Programs

SAnDReS 2.0: Development of machine‐learning models to explore the scoring function space

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