ArticleCommunications chemistry2022
An open-source molecular builder and free energy preparation workflow.
Article in Communications chemistry, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 8 papers.
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Who cites it
8 citing papers in PubMed.
- Free energy calculations in molecular modeling: from classical methods to machine learning.Journal of molecular modeling · 2026Review
- Developments and challenges in hit progression within fragment-based drug discovery.Nature communications · 2026Review
- The Evolution of Machine Learning in Medicinal Chemistry: A Comprehensive Bibliometric Analysis.Current neuropharmacology · 2026Review
- A Graph Neural Network Charge Model Targeting Accurate Electrostatic Properties of Organic Molecules.Journal of chemical theory and computation · 2025Article
- CACHE Challenge #2: Targeting the RNA Site of the SARS-CoV-2 Helicase Nsp13.Journal of chemical information and modeling · 2025Article
- Active learning driven prioritisation of compounds from on-demand libraries targeting the SARS-CoV-2 main protease.Digital discovery · 2025Article
- Navigating structure-based drug discovery with emerging innovations in physics- and knowledge-based approaches.npj drug discovery · 2025Review
- Exploring Scoring Function Space: Developing Computational Models for Drug Discovery.Current medicinal chemistry · 2024Review
Corrections and comments
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Authors and funding
6 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Automated free energy calculations for the prediction of binding free energies of congeneric series of ligands to a protein target are growing in popularity, but building reliable initial binding poses for the ligands is challenging. Here, we introduce the open-source FEgrow workflow for building user-defined congeneric series of ligands in protein binding pockets for input to free energy calculations. For a given ligand core and receptor structure, FEgrow enumerates and optimises the bioactive conformations of the grown functional group(s), making use of hybrid machine learning/molecular mechanics potential energy functions where possible. Low energy structures are optionally scored using the gnina convolutional neural network scoring function, and output for more rigorous protein-ligand binding free energy predictions. We illustrate use of the workflow by building and scoring binding poses for ten congeneric series of ligands bound to targets from a standard, high quality dataset of protein-ligand complexes. Furthermore, we build a set of 13 inhibitors of the SARS-CoV-2 main protease from the literature, and use free energy calculations to retrospectively compute their relative binding free energies. FEgrow is freely available at https://github.com/cole-group/FEgrow, along with a tutorial.
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Registered trials
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.