DeepPurpose
Deep modeling toolkit
A toolkit for molecular modeling and prediction tasks using deep learning
A Deep Learning Toolkit for DTI, Drug Property, PPI, DDI, Protein Function Prediction (Bioinformatics)
974 stars
31 watching
272 forks
Language: Jupyter Notebook
last commit: 5 months ago
Linked from 2 awesome lists
bioinformaticscovid19ddideep-learningdrug-discoverydrug-drug-interactiondrug-property-predictiondrug-repurposingdrug-target-interactiondrug-target-interactionsdti-predictionppiprotein-function-predictionprotein-protein-interactionqsarrepurposing-drugsside-effectstoolkitvirtual-screening
lmmentel/awesome-python-chemistry
inoue0426/awesome-computational-biologyRelated projects:
| Repository | Description | Stars |
|---|---|---|
 gist-csbl/deepconv-dti | A Python script for training and testing deep learning models to predict drug-target interactions | 71 |
 fangpingwan/deepcpi | A deep learning framework for large-scale in silico drug screening | 14 |
 bplaye/nnk_dti | An open-source project evaluating deep learning methods for predicting ligands and drugs in chemogenomics | 3 |
 jaechanglim/gnn_dti | This project implements a deep learning approach to predicting docking affinities for molecules with proteins | 66 |
 jppbsi/libdeep | An open-source C library providing tools and components for developing artificial intelligence-based techniques using deep learning. | 23 |
 bjoux2/deepdtis_dbn | A Python framework for deep learning-based drug-target interaction prediction using a DBN architecture. | 48 |
| fangpingwan/neodti | A deep learning framework for predicting new drug-target interactions by integrating neighbor information from heterogeneous networks | 74 |
 hkmztrk/deepdta | A system that predicts drug-target binding affinity using convolutional neural networks and protein sequences. | 225 |
 zhanghang1989/pytorch-encoding | A Python framework for building deep learning models with optimized encoding layers and batch normalization. | 2,041 |
 astrazeneca/chemicalx | A deep learning library for predicting the effects of combining two drugs | 714 |
 lifanchen-simm/transformercpi | Develops a deep learning model to predict compound-protein interactions by leveraging sequence-based learning and self-attention mechanisms | 134 |
 joncox123/cortexsys | A toolbox for building and training deep neural networks in Matlab | 70 |
 shen-lab/deepaffinity | A deep learning framework for predicting protein-compound affinity from molecular sequences and structures | 137 |
 mariewelt/openchem | A deep learning toolkit for computational chemistry and drug design research | 680 |
 atomistic-machine-learning/dtnn | An open-source Python framework for developing machine learning models to predict quantum-mechanical observables of molecular systems. | 77 |