ArticlePLoS computational biology2017
TopologyNet: Topology based deep convolutional and multi-task neural networks for biomolecular property predictions.
Article in PLoS computational biology, 2017. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 155 papers.
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155 citing papers in PubMed, 336 citations in OpenAlex.
- Persistent Local Laplacian Prediction of Protein-Ligand Binding Affinities.Journal of chemical information and modeling · 2026Article
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- Artificial intelligence-assisted lead optimization in drug discovery: bridging computational advances and translational challenges.Medicinal chemistry research : an international journal for rapid communications on design and mechanisms of action of biologically active agents · 2026Review
- Topology-enhanced machine learning for speech signal processing.Nature communications · 2026Article
- Topological learning prediction of virus-like particle stoichiometry and stability.Biophysical journal · 2026Article
- Graph identification of proteins in tomograms (GRIP-Tomo) 2.0: Topologically aware classification for proteins.Protein science : a publication of the Protein Society · 2026Article
- 3D Geometric Equivariant Graph Neural Networks with Contrastive Learning for Protein-Ligand Binding Affinity Prediction.Interdisciplinary sciences, computational life sciences · 2026Article
- UniPept: A Universal Model for Predicting Peptide Properties Based on Atom-Residue Dual-Scale Integration.Interdisciplinary sciences, computational life sciences · 2026Article
- Persistent sheaf Laplacian analysis of protein stability and solubility changes upon mutation.Protein science : a publication of the Protein Society · 2026Article
- Article
- Topological deep learning for drug-target interaction, virtual screening, and docking scoring: a practical, benchmark-driven review.Briefings in bioinformatics · 2026Review
- 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
- The Last Mile Problem: A Critical Assessment of Physics-Based and AI Tools for Small Molecule Binding Prediction in Virtual Screening.Journal of chemical information and modeling · 2026Article
- A DNN Biophysics Model with Topological and Electrostatic Features.Journal of chemical information and modeling · 2026Article
- Spectral Graph Features for Reference-free RNA 3D Quality Assessment.bioRxiv : the preprint server for biology · 2026Article
- Manifold topological deep learning for biomedical data.Nature communications · 2026Article
- Persistent Stanley-Reisner Theory.Foundations of data science (Springfield, Mo.) · 2026Article
- Computational Drug Repurposing for Alzheimer's Disease via Sheaf Theoretic Population-Scale Analysis of snRNA-Seq Data.Journal of medicinal chemistry · 2026Article
- Topological data analysis and topological deep learning beyond persistent homology: a review.Artificial intelligence review · 2026Article
95 more citing papers are in PubMed but not listed here.
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2 authors at 1 institution in 1 country.
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No grant is acknowledged in the PubMed record.
Abstract
Although deep learning approaches have had tremendous success in image, video and audio processing, computer vision, and speech recognition, their applications to three-dimensional (3D) biomolecular structural data sets have been hindered by the geometric and biological complexity. To address this problem we introduce the element-specific persistent homology (ESPH) method. ESPH represents 3D complex geometry by one-dimensional (1D) topological invariants and retains important biological information via a multichannel image-like representation. This representation reveals hidden structure-function relationships in biomolecules. We further integrate ESPH and deep convolutional neural networks to construct a multichannel topological neural network (TopologyNet) for the predictions of protein-ligand binding affinities and protein stability changes upon mutation. To overcome the deep learning limitations from small and noisy training sets, we propose a multi-task multichannel topological convolutional neural network (MM-TCNN). We demonstrate that TopologyNet outperforms the latest methods in the prediction of protein-ligand binding affinities, mutation induced globular protein folding free energy changes, and mutation induced membrane protein folding free energy changes. AVAILABILITY: weilab.math.msu.edu/TDL/.
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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.