Peptide library design

Code for peptide ligand design with machine learning models.

HELM-GPT: de novo macrocyclic peptide design using generative pre-trained transformer

Colab differentiable Masif Experiment Code for ZY

This is a Julia implementation of the dMaSIF geometric deep learning model for predicting binding affinity over protein surfaces.

Tool to design cyclic peptides that mimic proteins and target their binding partners.

binding free energy estimator 2

A novel graph neural network strategy with the Vina distance optimization terms to predict protein-ligand binding affinity

Collects software dedicated to predicting specific properties of peptides

A state-of-the-art graph neural network for the prediction of cell-penetrating peptides.

Python code for fine-tuning AlphaFold to perform protein-peptide binding predictions

Making Protein folding accessible to all!

Peptide-MHC I binding affinity prediction

De Novo Mass Spectrometry Peptide Sequencing with a Transformer Model

ET-Tox

AutoDock CrankPep for peptide and disordered protein docking

GPT powered plugin & fine tuned model for natural language interaction with in-silico drug simulators and prediction of drug properties

Transformer Based Language Model for Peptide Property Prediction

Deep Site and Docking Pose (DSDP) is a blind docking strategy accelerated by GPUs, developed by Gao Group. For the site prediction part, several modifications are introduced to PUResNet program. Th…

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Physical energy function for ddG prediction upon amino acid mutation

Deployed Model for Article: Machine learning predicts peptide stability in simulated gastrointestinal fluids

Controlling the usage of hydrophobic residues on AfDesign for binder peptide design with AlphaFold hallucination protocol

antimicrobial peptide prediction in R

Protein-protein, protein-peptide and protein-DNA docking framework based on the GSO algorithm