ArticleJournal of chemical information and modeling2019
AGL-Score: Algebraic Graph Learning Score for Protein-Ligand Binding Scoring, Ranking, Docking, and Screening.
Article in Journal of chemical information and modeling, 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 88 papers.
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Who cites it
88 citing papers in PubMed.
- Robustness of Protein-Ligand Binding Affinity Prediction Models to Docked and Predicted Structures.Journal of chemical information and modeling · 2026Article
- Structural Knowledge Is What Matters in Protein-Ligand Binding Affinity Prediction.Molecules (Basel, Switzerland) · 2026Article
- Correlated clustering and projection for dimensionality reduction.Machine learning: science and technology · 2026Article
- Reindeer: a protein-ligand feature generator software for machine learning algorithms.BMC bioinformatics · 2026Article
- Commutative Algebra Modeling in Materials Science - A Case Study on Metal-Organic Frameworks (MOFs).Journal of chemical information and modeling · 2026Article
- RA-PLA: Retrieval-augmented graph convolutional networks for protein-ligand binding affinity prediction.PloS one · 2026Article
- A geometric graph-based deep learning model for drug-target affinity prediction.BMC bioinformatics · 2025Article
- A Review of Topological Data Analysis and Topological Deep Learning in Molecular Sciences.Journal of chemical information and modeling · 2025Review
- An efficient deep learning-based strategy to screen inhibitors for GluN1/GluN3A receptor.Acta pharmacologica Sinica · 2025Article
- Geometry-aware lightweight convolutional network for efficient molecular property prediction.Scientific reports · 2025Article
- The Role of Carbenicillin as an Inhibitor of the Biofilm Regulator CsgD in Salmonella Typhimurium.MicrobiologyOpen · 2025Article
- A 4D tensor-enhanced multi-dimensional convolutional neural network for accurate prediction of protein-ligand binding affinity.Molecular diversity · 2025Article
- Enhancing energy predictions in multi-atom systems with multiscale topological learning.Journal of materials chemistry. A · 2025Article
- Persistent Directed Flag Laplacian (PDFL)-Based Machine Learning for Protein-Ligand Binding Affinity Prediction.Journal of chemical theory and computation · 2025Article
- Category-specific topological learning of metal-organic frameworks.Journal of materials chemistry. A · 2025Article
- Mayer-Homology Learning Prediction of Protein-Ligand Binding Affinities.Journal of computational biophysics and chemistry · 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
- Persistent Mayer Dirac.Journal of physics. Complexity · 2024Article
- Protein engineering in the deep learning era.mLife · 2024Review
28 more citing papers are in PubMed but not listed here.
Corrections and comments
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Authors and funding
2 authors.
Funding
Abstract
Although algebraic graph theory-based models have been widely applied in physical modeling and molecular studies, they are typically incompetent in the analysis and prediction of biomolecular properties, confirming the common belief that "one cannot hear the shape of a drum". A new development in the century-old question about the spectrum-geometry relationship is provided. Novel algebraic graph learning score (AGL-Score) models are proposed to encode high-dimensional physical and biological information into intrinsically low-dimensional representations. The proposed AGL-Score models employ multiscale weighted colored subgraphs to describe crucial molecular and biomolecular interactions in terms of graph invariants derived from graph Laplacian, its pseudo-inverse, and adjacency matrices. Additionally, AGL-Score models are integrated with an advanced machine learning algorithm to predict biomolecular macroscopic properties from the low-dimensional graph representation of biomolecular structures. The proposed AGL-Score models are extensively validated for their scoring power, ranking power, docking power, and screening power via a number of benchmark datasets, namely CASF-2007, CASF-2013, and CASF-2016. Numerical results indicate that the proposed AGL-Score models are able to outperform other state-of-the-art scoring functions in protein-ligand binding scoring, ranking, docking, and screening. This study indicates that machine learning methods are powerful tools for molecular docking and virtual screening. It also indicates that spectral geometry or spectral graph theory has the ability to infer geometric properties.
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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.