ReviewPhysical chemistry chemical physics : PCCP2020
A review of mathematical representations of biomolecular data.
Review in Physical chemistry chemical physics : PCCP, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 57 papers.
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Who cites it
57 citing papers in PubMed.
- Correlated clustering and projection for dimensionality reduction.Machine learning: science and technology · 2026Article
- Commutative Algebra Modeling in Materials Science - A Case Study on Metal-Organic Frameworks (MOFs).Journal of chemical information and modeling · 2026Article
- Interpretability and Representability of Commutative Algebra, Algebraic Topology, and Topological Spectral Theory for Real-World Data.Advanced intelligent discovery · 2025Article
- Machine Learning Approaches in Soft Matter Molecular Simulation and Materials Characterization: Challenges and Perspectives.ChemPlusChem · 2025Article
- A Review of Topological Data Analysis and Topological Deep Learning in Molecular Sciences.Journal of chemical information and modeling · 2025Review
- CAP: Commutative algebra prediction of protein-nucleic acid binding affinities.Machine learning: science and technology · 2025Article
- Molecule generation for target protein binding with hierarchical consistency diffusion model.Chemical science · 2025Article
- CAML: Commutative Algebra Machine Learning─A Case Study on Protein-Ligand Binding Affinity Prediction.Journal of chemical information and modeling · 2025Article
- PERSISTENT SHEAF LAPLACIANS.Foundations of data science (Springfield, Mo.) · 2025Article
- Novel Antimicrobials from Computational Modelling and Drug Repositioning: PotentialMolecules (Basel, Switzerland) · 2025Review
- A review of machine learning methods for imbalanced data challenges in chemistry.Chemical science · 2025Review
- Persistent Directed Flag Laplacian (PDFL)-Based Machine Learning for Protein-Ligand Binding Affinity Prediction.Journal of chemical theory and computation · 2025Article
- Mayer-Homology Learning Prediction of Protein-Ligand Binding Affinities.Journal of computational biophysics and chemistry · 2025Article
- Persistent Topological Laplacians-A Survey.Mathematics (Basel, Switzerland) · 2025Article
- Topology-preserving Hodge decomposition in the Eulerian representation.Beijing journal of pure & applied mathematics · 2025Article
- Multi-Cover Persistence (MCP)-based machine learning for polymer property prediction.Briefings in bioinformatics · 2024Article
- Topological Learning Approach to Characterizing Biological Membranes.Journal of chemical information and modeling · 2024Article
- Multiscale topology-enabled structure-to-sequence transformer for protein-ligand interaction predictions.Nature machine intelligence · 2024Article
- EGGNet, a Generalizable Geometric Deep Learning Framework for Protein Complex Pose Scoring.ACS omega · 2024Article
- Integration of persistent Laplacian and pre-trained transformer for protein solubility changes upon mutation.Computers in biology and medicine · 2024Article
Corrections and comments
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Authors and funding
3 authors.
Funding
Abstract
Recently, machine learning (ML) has established itself in various worldwide benchmarking competitions in computational biology, including Critical Assessment of Structure Prediction (CASP) and Drug Design Data Resource (D3R) Grand Challenges. However, the intricate structural complexity and high ML dimensionality of biomolecular datasets obstruct the efficient application of ML algorithms in the field. In addition to data and algorithm, an efficient ML machinery for biomolecular predictions must include structural representation as an indispensable component. Mathematical representations that simplify the biomolecular structural complexity and reduce ML dimensionality have emerged as a prime winner in D3R Grand Challenges. This review is devoted to the recent advances in developing low-dimensional and scalable mathematical representations of biomolecules in our laboratory. We discuss three classes of mathematical approaches, including algebraic topology, differential geometry, and graph theory. We elucidate how the physical and biological challenges have guided the evolution and development of these mathematical apparatuses for massive and diverse biomolecular data. We focus the performance analysis on protein-ligand binding predictions in this review although these methods have had tremendous success in many other applications, such as protein classification, virtual screening, and the predictions of solubility, solvation free energies, toxicity, partition coefficients, protein folding stability changes upon mutation, etc.
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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.