ReviewWiley interdisciplinary reviews. Computational molecular science
Machine-learning scoring functions to improve structure-based binding affinity prediction and virtual screening.
Review in Wiley interdisciplinary reviews. Computational molecular science. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 164 papers, 1 of them a synthesis that pooled it.
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
164 citing papers in PubMed, 1 synthesis or guideline pooled it.
- The Impact and Role of Artificial Intelligence (AI) in Healthcare: Systematic Review.Current topics in medicinal chemistry · 2025Pooled it
- Integrative Computational Chemistry Approaches in Modern Drug Discovery: Advances in Docking, Pharmacophore Modeling, Molecular Dynamics, and Virtual Screening.Pharmaceutics · 2026Review
- Reindeer: a protein-ligand feature generator software for machine learning algorithms.BMC bioinformatics · 2026Article
- AI-driven virtual screening platform identifies novel NSUN2 inhibitor candidates for targeted cancer therapy: a computational drug discovery approach.NPJ precision oncology · 2026Article
- Prediction of Protein-Ligand Binding Affinities Using Atomic Surface Site Interaction Points.Journal of chemical information and modeling · 2026Article
- PepScorer::RMSD: An Improved Machine Learning Scoring Function for Protein-Peptide Docking.International journal of molecular sciences · 2026Article
- Article
- SMILES-based degree molecular descriptors and machine learning for QSPR modeling of anti-alkaptonuria drugs.Frontiers in chemistry · 2026Article
- Deep Learning Pipeline for Accelerating Virtual Screening in Drug Discovery.Methods in molecular biology (Clifton, N.J.) · 2026Article
- Physics-Informed Artificial Intelligence Design of Picomolar Nanobodies Enables Deep Tumor Penetration and High-Contrast Imaging.Research (Washington, D.C.) · 2026Article
- CarsiDock-Cov: A deep learning-guided approach for automated covalent docking and screening.Acta pharmaceutica Sinica. B · 2025Article
- Aptamers as Potential Inhibitors of Ethylene Biosynthesis: Identification and In Silico Selection.International journal of molecular sciences · 2025Article
- Cross-disciplinary perspectives on the potential for artificial intelligence across chemistry.Chemical Society reviews · 2025Review
- A beginner's approach to deep learning applied to VS and MD techniques.Journal of cheminformatics · 2025Review
- GRADE and X-GRADE: Unveiling Novel Protein-Ligand Interaction Fingerprints Based on GRAIL Scores.Journal of chemical information and modeling · 2025Article
- FeatureDock for protein-ligand docking guided by physicochemical feature-based local environment learning using transformer.npj drug discovery · 2025Article
- Convergent Protocols for Computing Protein-Ligand Interaction Energies Using Fragment-Based Quantum Chemistry.Journal of chemical theory and computation · 2025Article
- The algebraic extended atom-type graph-based model for precise ligand-receptor binding affinity prediction.Journal of cheminformatics · 2025Article
- A comprehensive survey of scoring functions for protein docking models.BMC bioinformatics · 2025Article
- In-silico inquest reveal the efficacy ofIn silico pharmacology · 2025Article
104 more citing papers are in PubMed but not listed here.
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
Abstract
Docking tools to predict whether and how a small molecule binds to a target can be applied if a structural model of such target is available. The reliability of docking depends, however, on the accuracy of the adopted scoring function (SF). Despite intense research over the years, improving the accuracy of SFs for structure-based binding affinity prediction or virtual screening has proven to be a challenging task for any class of method. New SFs based on modern machine-learning regression models, which do not impose a predetermined functional form and thus are able to exploit effectively much larger amounts of experimental data, have recently been introduced. These machine-learning SFs have been shown to outperform a wide range of classical SFs at both binding affinity prediction and virtual screening. The emerging picture from these studies is that the classical approach of using linear regression with a small number of expert-selected structural features can be strongly improved by a machine-learning approach based on nonlinear regression allied with comprehensive data-driven feature selection. Furthermore, the performance of classical SFs does not grow with larger training datasets and hence this performance gap is expected to widen as more training data becomes available in the future. Other topics covered in this review include predicting the reliability of a SF on a particular target class, generating synthetic data to improve predictive performance and modeling guidelines for SF development.
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.