ArticleJournal of chemical theory and computation2025
Accurate Predictions of Molecular Properties of Proteins via Graph Neural Networks and Transfer Learning.
Article in Journal of chemical theory and computation, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 6 papers.
What it found
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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.
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Who cites it
6 citing papers in PubMed.
- Predicting Enzyme pH Optima from Structure Using Equivariant Graph Neural Networks.Journal of chemical information and modeling · 2026Article
- Protein Electrostatic Properties are Finetuned Through Evolution.bioRxiv : the preprint server for biology · 2026Article
- Enhancing bioinformatics engineering by utilizing graph therapeutic properties for clinically approved antitoxin drugs in zoonotic diseases.Scientific reports · 2026Article
- Graph-Based Deep Learning Models for Predicting pJournal of chemical information and modeling · 2026Article
- UniPocket: Unified Ligand and Cryptic Pocket Prediction from Protein Language Model Embeddings.ACM-BCB ... ... : the ... ACM Conference on Bioinformatics, Computational Biology and Biomedicine. ACM Conference on Bioinformatics, Computational Biology and Biomedicine · 2026Article
- Computation suggests that the cell adhesion sub-proteome is enriched for sites of pH-dependence and charge burial.PloS one · 2025Article
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3 authors.
Funding
Abstract
Machine learning has emerged as a promising approach for predicting molecular properties of proteins, as it addresses limitations of experimental and traditional computational methods. Here, we introduce GSnet, a graph neural network (GNN) trained to predict physicochemical and geometric properties including solvation-free energies, diffusion constants, and hydrodynamic radii, based on three-dimensional protein structures. By leveraging transfer learning, pretrained GSnet embeddings were adapted to predict solvent-accessible surface area (SASA) and residue-specific p
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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.