ArticleACS omega2021
Prediction of Binding Free Energy of Protein-Ligand Complexes with a Hybrid Molecular Mechanics/Generalized Born Surface Area and Machine Learning Method.
Article in ACS omega, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 23 papers.
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.
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.
Who cites it
23 citing papers in PubMed.
- Targeting p53-MDM2 pathway with novel triazole-oxazole hybrids: a fragment-based drug discovery approach for next-generation cancer therapies.Molecular diversity · 2026Article
- Identification of potential MenT3 inhibitors for Mycobacterium tuberculosis using the generative artificial intelligence and SilicoXplore platform.Scientific reports · 2026Article
- Article
- Steroids fromMolecular biology research communications · 2026Article
- In Silico Identification of Potential Biomarker-Binding Proteins for Noninvasive Diagnosis of Buruli Ulcer Disease.Bioinformatics and biology insights · 2026Article
- In silico identification of bilobetin and ginsenoside as dual CK2 and ULK2 inhibitors targeting triple-negative breast cancer.Scientific reports · 2025Article
- In search of a photoswitchable drug for serotonin receptors: a molecular dynamics simulation study.RSC advances · 2025Article
- Molecular docking and molecular dynamics simulations revealed interaction mechanism of acetylcholinesterase with organophosphorus pesticides and their alternatives.Archives of toxicology · 2025Article
- In Vitro and In Silico Evaluation of the Potential Anti-Prostate Cancer Activity ofInternational journal of molecular sciences · 2025Article
- Multi-Objective Loss Function For Free Energy Calculations.ACM-BCB ... ... : the ... ACM Conference on Bioinformatics, Computational Biology and Biomedicine. ACM Conference on Bioinformatics, Computational Biology and Biomedicine · 2025Article
- In-silico identification of phytochemical compounds from various medicinal plants as potent HIV-1 non-nucleoside reverse transcriptase inhibitors utilizing molecular docking and molecular dynamics simulations.Biology methods & protocols · 2025Article
- In silico discovery of alkaloids as VEGFR-2 inhibitors with favorable toxicity profiles: insights from molecular dynamics and MM/GBSA.In silico pharmacology · 2025Article
- Structure-based pharmacophore modeling for precision inhibition of mutant ESR2 in breast cancer: A systematic computational approach.Cancer medicine · 2024Article
- Targeting hexokinase 2 for oral cancer therapy: structure-based design and validation of lead compounds.Frontiers in pharmacology · 2024Article
- Considerations Around Structure-Based Drug Discovery for KRAS Using DOCK.Methods in molecular biology (Clifton, N.J.) · 2024Article
- Article
- Article
- Article
- Staying Ahead of the Game: How SARS-CoV-2 has Accelerated the Application of Machine Learning in Pandemic Management.BioDrugs : clinical immunotherapeutics, biopharmaceuticals and gene therapy · 2023Review
- Open-Source Machine Learning in Computational Chemistry.Journal of chemical information and modeling · 2023Review
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
4 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Accurate prediction of protein-ligand binding free energies is important in enzyme engineering and drug discovery. The molecular mechanics/generalized Born surface area (MM/GBSA) approach is widely used to estimate ligand-binding affinities, but its performance heavily relies on the accuracy of its energy components. A hybrid strategy combining MM/GBSA and machine learning (ML) has been developed to predict the binding free energies of protein-ligand systems. Based on the MM/GBSA energy terms and several features associated with protein-ligand interactions, our ML-based scoring function, GXLE, shows much better performance than MM/GBSA without entropy. In particular, the good transferability of the GXLE model is highlighted by its good performance in ranking power for prediction of the binding affinity of different ligands for either the docked structures or crystal structures. The GXLE scoring function and its code are freely available and can be used to correct the binding free energies computed by MM/GBSA.
Identifiers
What OpenQuestion holds
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.