Evidence map›Paper›PMID 37106032›Full record

ArticleCommunications chemistry2023

Automated relative binding free energy calculations from SMILES to ΔΔG.

J Harry Moore, Christian Margreitter, Jon Paul Janet, Ola Engkvist, Bert L de Groot, Vytautas Gapsys

Abstract read
In one paragraph

Article in Communications chemistry, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 10 papers.

0numbers the graph read from it
0cells of the map it votes in
10citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

10 citing papers in PubMed.

  1. Benchmarking Docking Protocols on Predicting Alternative Binding Modes.Journal of chemical information and modeling · 2026
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4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

6 authors.

J Harry MooreMolecular AI, Discovery Sciences, R&D, AstraZeneca, Gothenburg, Sweden.ORCID http://orcid.org/0000-0001-5116-3027
Christian MargreitterMolecular AI, Discovery Sciences, R&D, AstraZeneca, Gothenburg, Sweden.
Jon Paul JanetMolecular AI, Discovery Sciences, R&D, AstraZeneca, Gothenburg, Sweden.ORCID http://orcid.org/0000-0001-7825-4797
Ola EngkvistMolecular AI, Discovery Sciences, R&D, AstraZeneca, Gothenburg, Sweden. ola.engkvist@astrazeneca.com.ORCID http://orcid.org/0000-0003-4970-6461
Bert L de GrootComputational Biomolecular Dynamics Group, Department of Theoretical and Computational Biophysics, Max Planck Institute for Multidisciplinary Sciences, Am Fassberg 11, D-37077, Göttingen, Germany. bgroot@gwdg.de.ORCID http://orcid.org/0000-0003-3570-3534
Vytautas GapsysComputational Biomolecular Dynamics Group, Department of Theoretical and Computational Biophysics, Max Planck Institute for Multidisciplinary Sciences, Am Fassberg 11, D-37077, Göttingen, Germany. vgapsys@gwdg.de.ORCID http://orcid.org/0000-0002-6761-7780

Funding

EC | Horizon 2020 Framework Programme (EU Framework Programme for Research and Innovation H2020) H2020-INFRAEDI-02-2018- 823830
6 · The paper itself

Abstract

In drug discovery, computational methods are a key part of making informed design decisions and prioritising experiments. In particular, optimizing compound affinity is a central concern during the early stages of development. In the last 10 years, alchemical free energy (FE) calculations have transformed our ability to incorporate accurate in silico potency predictions in design decisions, and represent the 'gold standard' for augmenting experiment-driven drug discovery. However, relative FE calculations are complex to set up, require significant expert intervention to prepare the calculation and analyse the results or are provided only as closed-source software, not allowing for fine-grained control over the underlying settings. In this work, we introduce an end-to-end relative FE workflow based on the non-equilibrium switching approach that facilitates calculation of binding free energies starting from SMILES strings. The workflow is implemented using fully modular steps, allowing various components to be exchanged depending on licence availability. We further investigate the dependence of the calculated free energy accuracy on the initial ligand pose generated by various docking algorithms. We show that both commercial and open-source docking engines can be used to generate poses that lead to good correlation of free energies with experimental reference data.

Identifiers

PMID37106032
PMCPMC10140266

What OpenQuestion holds

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LicenceCC BY
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Registered trials

None linked

Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.