Predict the binding affinity of protein-protein complexes from structural data
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Updated
Oct 14, 2024 - Python
Predict the binding affinity of protein-protein complexes from structural data
PSICHIC (pronounced Psychic) - PhySIcoCHemICal graph neural network for learning protein-ligand interaction fingerprints from sequence data
Affinity Protein-Protein Transformers—State of the art protein-protein binding affinity in seconds!
The official implementation of DiffAbXL benchmarked in the paper "Exploring Log-Likelihood Scores for Ranking Antibody Sequence Designs", formerly titled "Benchmarking Generative Models for Antibody Design".
Predicting the effect of mutations on protein stability and protein binding affinity using pretrained neural networks and a ranking objective function.
Dataset with quantitative binding scores of scFv-format antibodies against SARS-CoV-2 target peptide
Official Implementation of CompassDock
Predict binding affinity of ligand-protein complexes using Graph Neural Networks. The model is implemented using PyTorch Geometric and based on the method in "Predicting drug-target interaction using 3D structure-embedded graph representations from graph neural networks"
NeuralMD for protein-ligand binding simulation
Generate customized voxel representations of protein-ligand complexes using GPU.
Predicting the effect of mutations on protein stability and protein-protein interaction affinity.
Repository for the Taba tool
Comprehensive ensembling of protein-ligand structure and affinity prediction methods (CASP16)
Prediction of Absolute Ligand-Protein Binding Affinity
Predicts the dissociation coefficients between drug-protein (target) pairs using molecular fingerprints and physicochemical protein properties.
TAG-DTA: Binding Region-Guided Strategy to Predict Drug-Target Affinity Using Transformers
Molecule oracle for predicting clinical viability from binding affinity and historical clinical trial data.
Final Project of Deep Learning for Healthcare Spring 2022 - Reproducing DeepDTA
Code for Binding Affinity Prediction with Graph Neural Networks
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