ArticleBMC bioinformatics2022
Sfcnn: a novel scoring function based on 3D convolutional neural network for accurate and stable protein-ligand affinity prediction.
Article in BMC bioinformatics, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 23 papers.
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Who cites it
23 citing papers in PubMed.
- Predicting Adsorbate-Solvent Interaction Energies in Zeolite Pores Using Convolutional Neural Networks with Attention Mechanisms.Journal of chemical information and modeling · 2026Article
- Artificial intelligence and ultra-high performance computing methods and experiments for drug discovery: virtual screening, deep learning, molecular dynamics simulations, ADMET modelling, and experimental validation.Molecular biomedicine · 2026Review
- Critical Artifacts Improve Reproducibility of Protein-Ligand Binding Affinity Prediction Models on CASF-2016.Journal of chemical information and modeling · 2026Article
- Distillation enables scalable high-fidelity virtual screening across ultra-large chemical libraries.bioRxiv : the preprint server for biology · 2026Article
- CLIMBS: Assessing Carbohydrate-Protein Interactions through a Graph Neural Network Classifier Using Synthetic Negative Data.Journal of chemical information and modeling · 2026Article
- GRIPHIN: grids of pharmacophore interaction fields for affinity prediction.Journal of cheminformatics · 2026Article
- Artificial intelligence-driven drug discovery: a deep learning paradigm shift in pharmaceutical research and development.Frontiers in pharmacology · 2026Review
- Discovery of novel peptidomimetics against HSP90-HOP interactions towards improved cancer therapeutics using machine learning strategies.Frontiers in bioinformatics · 2026Article
- Accurate and Rapid Ranking of Protein-Ligand Binding Affinities Using Density Matrix Fragmentation and Physics-Informed Machine Learning Dispersion Potentials.Chemphyschem : a European journal of chemical physics and physical chemistry · 2025Article
- A 4D tensor-enhanced multi-dimensional convolutional neural network for accurate prediction of protein-ligand binding affinity.Molecular diversity · 2025Article
- Pairwise Performance Comparison of Docking Scoring Functions: Computational Approach Using InterCriteria Analysis.Molecules (Basel, Switzerland) · 2025Article
- SPLIF-Enhanced Attention-Driven 3D CNNs for Precise and Reliable Protein-Ligand Interaction Modeling for METTL3.ACS omega · 2025Article
- SG-ML-PLAP: A structure-guided machine learning-based scoring function for protein-ligand binding affinity prediction.Protein science : a publication of the Protein Society · 2025Article
- CENsible: Interpretable Insights into Small-Molecule Binding with Context Explanation Networks.Journal of chemical information and modeling · 2024Article
- Distance plus attention for binding affinity prediction.Journal of cheminformatics · 2024Article
- Article
- Structure-based, deep-learning models for protein-ligand binding affinity prediction.Journal of cheminformatics · 2024Review
- A comprehensive review of the recent advances on predicting drug-target affinity based on deep learning.Frontiers in pharmacology · 2024Review
- Advances in Protein-Ligand Binding Affinity Prediction via Deep Learning: A Comprehensive Study of Datasets, Data Preprocessing Techniques, and Model Architectures.Current drug targets · 2024Review
- CENsible: Interpretable Insights into Small-Molecule Binding with Context Explanation Networks.bioRxiv : the preprint server for biology · 2023Article
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3 authors.
Funding
Abstract
backgroundComputer-aided drug design provides an effective method of identifying lead compounds. However, success rates are significantly bottlenecked by the lack of accurate and reliable scoring functions needed to evaluate binding affinities of protein-ligand complexes. Therefore, many scoring functions based on machine learning or deep learning have been developed to improve prediction accuracies in recent years. In this work, we proposed a novel featurization method, generating a new scoring function model based on 3D convolutional neural network.
resultsThis work showed the results from testing four architectures and three featurization methods, and outlined the development of a novel deep 3D convolutional neural network scoring function model. This model simplified feature engineering, and in combination with Grad-CAM made the intermediate layers of the neural network more interpretable. This model was evaluated and compared with other scoring functions on multiple independent datasets. The Pearson correlation coefficients between the predicted binding affinities by our model and the experimental data achieved 0.7928, 0.7946, 0.6758, and 0.6474 on CASF-2016 dataset, CASF-2013 dataset, CSAR_HiQ_NRC_set, and Astex_diverse_set, respectively. Overall, our model performed accurately and stably enough in the scoring power to predict the binding affinity of a protein-ligand complex.
conclusionsThese results indicate our model is an excellent scoring function, and performs well in scoring power for accurately and stably predicting the protein-ligand affinity. Our model will contribute towards improving the success rate of virtual screening, thus will accelerate the development of potential drugs or novel biologically active lead compounds.
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