GNN_DTI
Molecular docking predictor
This project implements a deep learning approach to predicting docking affinities for molecules with proteins
66 stars
1 watching
16 forks
Language: Python
last commit: over 4 years ago
Linked from 1 awesome list
Related projects:
Repository | Description | Stars |
---|---|---|
| An open-source Python framework for developing machine learning models to predict quantum-mechanical observables of molecular systems. | 78 |
| A Python script for training and testing deep learning models to predict drug-target interactions | 73 |
| An open-source project evaluating deep learning methods for predicting ligands and drugs in chemogenomics | 3 |
| A toolkit for molecular modeling and prediction tasks using deep learning | 988 |
| A deep learning framework for predicting new drug-target interactions by integrating neighbor information from heterogeneous networks | 75 |
| A deep learning framework for predicting protein-compound affinity from molecular sequences and structures | 137 |
| A Python framework for deep learning-based drug-target interaction prediction using a DBN architecture. | 49 |
| A deep learning framework for predicting protein-protein interactions based on sequence data | 89 |
| A system that predicts drug-target binding affinity using convolutional neural networks and protein sequences. | 228 |
| A deep learning-based system for generating precise grasp poses for robots in real-time | 525 |
| A framework for predicting pairwise non-covalent interactions and binding affinities between compounds and proteins using machine learning | 100 |
| CPI prediction tool using graph neural networks and convolutional neural networks | 159 |
| A computational pipeline to predict novel drug-target interactions from heterogeneous networks | 175 |
| A Python project that uses machine learning to improve the representation of molecules in drug discovery | 60 |
| Develops a deep learning model to predict compound-protein interactions by leveraging sequence-based learning and self-attention mechanisms | 134 |