Representability of algebraic topology for biomolecules in machine learning based scoring and virtual screening
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This work introduces a number of algebraic topology approaches, including multi-component persistent homology, multi-level persistent homology, and electrostatic persistence for the representation, characterization, and description of small molecules and biomolecular complexes. In contrast to the conventional persistent homology, multi-component persistent homology retains critical chemical and biological information during the topological simplification of biomolecular geometric complexity. Multi-level persistent homology enables a tailored topological description of inter- and/or intra-molecular interactions of interest. Electrostatic persistence incorporates partial charge information into topological invariants. These topological methods are paired with Wasserstein distance to characterize similarities between molecules and are further integrated with a variety of machine learning algorithms, including k-nearest neighbors, ensemble of trees, and deep convolutional neural networks, to manifest their descriptive and predictive powers for protein-ligand binding analysis and virtual screening of small molecules. Extensive numerical experiments involving 4,414 protein-ligand complexes from the PDBBind database and 128,374 ligand-target and decoy-target pairs in the DUD database are performed to test respectively the scoring power and the discriminatory power of the proposed topological learning strategies. It is demonstrated that the present topological learning outperforms other existing methods in protein-ligand binding affinity prediction and ligand-decoy discrimination.
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Applying convolutional neural networks and multi-task learning in the paper's predictions further advanced the capability of the models.
“We applied convolutional neural networks and multi-task learning in our predictions which further advanced the capability of our models [22, 23].”
From Mathematical deep learning for pose and binding affinity prediction and ranking in D3R Grand Challenges (2019) · cites this paper as [23]
For small molecules, multilevel element specific persistent homology can describe both covalent bonds and noncovalent interactions.
“For the characterization of small molecules, we can use a multilevel element specific persistent homology to both describe the covalent bonds and noncovalent interactions in the molecule [23].”
From Mathematical deep learning for pose and binding affinity prediction and ranking in D3R Grand Challenges (2019) · cites this paper as [23]
Characterizing non-additive contributions of functional groups helps machine learning-based scoring functions consistently maintain accuracy in binding affinity predictions across a variety of protein-ligand complexes.
“Such a characterization can help machine learning-based SFs consistently maintain their accuracy in binding affinity predictions for a variety of protein–ligand complexes [19–23].”
From Mathematical deep learning for pose and binding affinity prediction and ranking in D3R Grand Challenges (2019) · cites this paper as [23]
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Self-citation
- Authors
- Cang, Z., Mu, L., & Wei, G.-W.
- Affiliations
- Michigan State University (US)
- Oak Ridge National Laboratory (US)
- Corresponding author
- Wei, Guo-Wei
- Venue
- PLOS Computational Biology
- Publisher
- Public Library of Science (PLoS)
- Published
- 2018-01-08
- Volume
- 14
- Issue
- 1
- Pages
- e1005929
- ISSN
- 1553-734X (ISSN-L)
- 1553-7358
- PMC
- PMC5774846
- OpenAlex
- W2963833291
- Cited by
- 257
- References
- 140
- Citations by year
- 2026: 21 · 2025: 34 · 2024: 28 · 2023: 37 · 2022: 35 · 2021: 35 · 2020: 39 · 2019: 17 · 2018: 11
- FWCI
- 18.85
- Type
- journal article
- Language
- en
- Update notices
- new_version (2018-01-19)
- Open access
- gold
- doi.org
- Licence
- Locations
- PLoS Computational Biology (publishedVersion, cc-by)
- Europe PMC (PubMed Central) (publishedVersion, cc-by)
- PubMed Central (publishedVersion, cc-by)
- PubMed (publishedVersion)
- arXiv (Cornell University) (submittedVersion)
- OSTI OAI (U.S. Department of Energy Office of Scientific and Technical Information) (submittedVersion)
- RePEc: Research Papers in Economics (submittedVersion)
- DOAJ (DOAJ: Directory of Open Access Journals) (submittedVersion, cc-by-sa)
- arXiv (Cornell University)
- Funders
- National Science Foundation (DMS-1721024, IIS-1302285)
- Topics
- Physical Sciences › Computer Science › Computational Theory and Mathematics › Computational Drug Discovery Methods
- Physical Sciences › Computer Science › Computational Theory and Mathematics › Topological and Geometric Data Analysis
- Life Sciences › Biochemistry, Genetics and Molecular Biology › Molecular Biology › Machine Learning in Bioinformatics
- Keywords
- algebraic topology, persistent homology, Wasserstein distance, protein-ligand binding affinity prediction, virtual screening, biomolecular complexes, PDBbind
- MeSH terms
- Machine Learning, Algorithms, Humans, Ligands, Models, Neurological, Nucleic Acids, Protein Binding, Proteins, Neural Networks, Computer, Computational Biology, Area Under Curve, Protein Interaction Mapping, Databases, Protein, Static Electricity, Molecular Dynamics Simulation
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