ArticleJournal of computer-aided molecular design2015
A comparative study of family-specific protein-ligand complex affinity prediction based on random forest approach.
Article in Journal of computer-aided molecular design, 2015. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 22 papers.
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Who cites it
22 citing papers in PubMed.
- Artificial Intelligence Revolution in Pharmaceutical Sciences : Advancements, Clinical Impacts, and Applications.Current pharmaceutical biotechnology · 2026Review
- Recent trends in machine learning and deep learning-based prediction of G-protein coupled receptor-ligand binding affinities.Frontiers in bioinformatics · 2025Review
- Recent Development, Applications, and Patents of Artificial Intelligence in Drug Design and Development.Current drug discovery technologies · 2025Review
- Trends of Artificial Intelligence (AI) Use in Drug Targets, Discovery and Development: Current Status and Future Perspectives.Current drug targets · 2025Review
- Edge-enhanced interaction graph network for protein-ligand binding affinity prediction.PloS one · 2025Article
- The Artificial Intelligence-Powered New Era in Pharmaceutical Research and Development: A Review.AAPS PharmSciTech · 2024Review
- MD-Ligand-Receptor: A High-Performance Computing Tool for Characterizing Ligand-Receptor Binding Interactions in Molecular Dynamics Trajectories.International journal of molecular sciences · 2023Article
- Artificial intelligence in the prediction of protein-ligand interactions: recent advances and future directions.Briefings in bioinformatics · 2022Review
- Protein-Specific Prediction of RNA-Binding Sites Based on Information Entropy.Computational intelligence and neuroscience · 2022Article
- Recent trends in artificial intelligence-driven identification and development of anti-neurodegenerative therapeutic agents.Molecular diversity · 2021Article
- Artificial intelligence in drug discovery and development.Drug discovery today · 2021Review
- A Review on Applications of Computational Methods in Drug Screening and Design.Molecules (Basel, Switzerland) · 2020Review
- Characterizing Protein-Ligand Binding Using Atomistic Simulation and Machine Learning: Application to Drug Resistance in HIV-1 Protease.Journal of chemical theory and computation · 2020Article
- Target-Specific Prediction of Ligand Affinity with Structure-Based Interaction Fingerprints.Journal of chemical information and modeling · 2019Article
- Key Topics in Molecular Docking for Drug Design.International journal of molecular sciences · 2019Review
- Individually double minimum-distance definition of protein-RNA binding residues and application to structure-based prediction.Journal of computer-aided molecular design · 2018Article
- Implicit-descriptor ligand-based virtual screening by means of collaborative filtering.Journal of cheminformatics · 2018Article
- A machine learning approach towards the prediction of protein-ligand binding affinity based on fundamental molecular properties.RSC advances · 2018Article
- Empirical Scoring Functions for Structure-Based Virtual Screening: Applications, Critical Aspects, and Challenges.Frontiers in pharmacology · 2018Review
- Recognition of activities of daily living in healthy subjects using two ad-hoc classifiers.Biomedical engineering online · 2015Article
Corrections and comments
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Authors and funding
7 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
The assessment of binding affinity between ligands and the target proteins plays an essential role in drug discovery and design process. As an alternative to widely used scoring approaches, machine learning methods have also been proposed for fast prediction of the binding affinity with promising results, but most of them were developed as all-purpose models despite of the specific functions of different protein families, since proteins from different function families always have different structures and physicochemical features. In this study, we proposed a random forest method to predict the protein-ligand binding affinity based on a comprehensive feature set covering protein sequence, binding pocket, ligand structure and intermolecular interaction. Feature processing and compression was respectively implemented for different protein family datasets, which indicates that different features contribute to different models, so individual representation for each protein family is necessary. Three family-specific models were constructed for three important protein target families of HIV-1 protease, trypsin and carbonic anhydrase respectively. As a comparison, two generic models including diverse protein families were also built. The evaluation results show that models on family-specific datasets have the superior performance to those on the generic datasets and the Pearson and Spearman correlation coefficients (R p and Rs) on the test sets are 0.740, 0.874, 0.735 and 0.697, 0.853, 0.723 for HIV-1 protease, trypsin and carbonic anhydrase respectively. Comparisons with the other methods further demonstrate that individual representation and model construction for each protein family is a more reasonable way in predicting the affinity of one particular protein family.
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Registered trials
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