ArticleMolecules (Basel, Switzerland)2015
Low-Quality Structural and Interaction Data Improves Binding Affinity Prediction via Random Forest.
Article in Molecules (Basel, Switzerland), 2015. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 42 papers.
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Who cites it
42 citing papers in PubMed.
- Identifying Potential BACE1 Inhibitors from the ChEMBL Database Using Machine Learning and Atomistic Simulation Approaches.ACS omega · 2026Article
- Contrastive learning-based drug screening model for GluN1/GluN3A inhibitors.Acta pharmacologica Sinica · 2025Article
- CoBdock-2: enhancing blind docking performance through hybrid feature selection combining ensemble and multimodel feature selection approaches.Journal of computer-aided molecular design · 2025Article
- RETRACTED: Optimizing chemotherapeutic targets in non-small cell lung cancer with transfer learning for precision medicine.PloS one · 2025Article
- G-Computational and structural biotechnology journal · 2024Article
- Antihypertensive and Antidiabetic Drug Candidates from Milkfish (Foods (Basel, Switzerland) · 2024Article
- DLM-DTI: a dual language model for the prediction of drug-target interaction with hint-based learning.Journal of cheminformatics · 2024Article
- Cobdock: an accurate and practical machine learning-based consensus blind docking method.Journal of cheminformatics · 2024Article
- A comprehensive review of the recent advances on predicting drug-target affinity based on deep learning.Frontiers in pharmacology · 2024Review
- Improving structure-based protein-ligand affinity prediction by graph representation learning and ensemble learning.PloS one · 2024Article
- Predicting drug-target binding affinity with cross-scale graph contrastive learning.Briefings in bioinformatics · 2023Article
- SCORCH: Improving structure-based virtual screening with machine learning classifiers, data augmentation, and uncertainty estimation.Journal of advanced research · 2023Article
- DoubleSG-DTA: Deep Learning for Drug Discovery: Case Study on the Non-Small Cell Lung Cancer withPharmaceutics · 2023Article
- Prediction of protein-ligand binding affinity with deep learning.Computational and structural biotechnology journal · 2023Review
- DeepLPI: a novel deep learning-based model for protein-ligand interaction prediction for drug repurposing.Scientific reports · 2022Article
- Article
- Scoring Functions for Protein-Ligand Binding Affinity Prediction using Structure-Based Deep Learning: A Review.Frontiers in bioinformatics · 2022Article
- Multi-TransDTI: Transformer for Drug-Target Interaction Prediction Based on Simple Universal Dictionaries with Multi-View Strategy.Biomolecules · 2022Article
- Machine-learning scoring functions trained on complexes dissimilar to the test set already outperform classical counterparts on a blind benchmark.Briefings in bioinformatics · 2021Article
- Using diverse potentials and scoring functions for the development of improved machine-learned models for protein-ligand affinity and docking pose prediction.Journal of computer-aided molecular design · 2021Article
Corrections and comments
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Authors and funding
4 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Docking scoring functions can be used to predict the strength of protein-ligand binding. It is widely believed that training a scoring function with low-quality data is detrimental for its predictive performance. Nevertheless, there is a surprising lack of systematic validation experiments in support of this hypothesis. In this study, we investigated to which extent training a scoring function with data containing low-quality structural and binding data is detrimental for predictive performance. We actually found that low-quality data is not only non-detrimental, but beneficial for the predictive performance of machine-learning scoring functions, though the improvement is less important than that coming from high-quality data. Furthermore, we observed that classical scoring functions are not able to effectively exploit data beyond an early threshold, regardless of its quality. This demonstrates that exploiting a larger data volume is more important for the performance of machine-learning scoring functions than restricting to a smaller set of higher data quality.
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