MONN

Interaction predictor

A framework for predicting pairwise non-covalent interactions and binding affinities between compounds and proteins using machine learning

MONN: a Multi-Objective Neural Network for Predicting Pairwise Non-Covalent Interactions and Binding Affinities between Compounds and Proteins

GitHub

100 stars
6 watching
30 forks
Language: Python
last commit: almost 6 years ago
Linked from 1 awesome list


Backlinks from these awesome lists:

Related projects:

RepositoryDescriptionStars
muhaochen/seq_ppiA deep learning framework for predicting protein-protein interactions based on sequence data89
shen-lab/deepaffinityA deep learning framework for predicting protein-compound affinity from molecular sequences and structures137
luoyunan/dtinetA computational pipeline to predict novel drug-target interactions from heterogeneous networks175
atomistic-machine-learning/dtnnAn open-source Python framework for developing machine learning models to predict quantum-mechanical observables of molecular systems.78
hkmztrk/deepdtaA system that predicts drug-target binding affinity using convolutional neural networks and protein sequences.228
bjoux2/deepdtis_dbnA Python framework for deep learning-based drug-target interaction prediction using a DBN architecture.49
fangpingwan/neodtiA deep learning framework for predicting new drug-target interactions by integrating neighbor information from heterogeneous networks75
jaechanglim/gnn_dtiThis project implements a deep learning approach to predicting docking affinities for molecules with proteins66
masashitsubaki/cpi_predictionCPI prediction tool using graph neural networks and convolutional neural networks159
simonfqy/padmeA deep learning-based framework for predicting drug-target interaction from protein descriptors42
thinng/graphdtaPredicts drug-target binding affinity using graph neural networks230
mrgemy95/visual-interaction-networks-pytorchAn implementation of Deepmind's Visual Interaction Networks using PyTorch to predict future events in physical scenes.166
atomistic-machine-learning/schnetpackA toolbox for training and applying deep neural networks to predict atomistic properties of molecules and materials795
anthony-wang/crabnetA deep learning framework for predicting material properties from composition information.94
mhlee0903/multi_channels_pinnInvestigating neural networks for drug discovery using multiple chemical descriptors.3