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covae

cpgl

CPGL: Prediction of compound-protein interaction by integrating graph attention network with long short-term memory neural network

cpi-igae

An inductive graph neural network model for compound-protein interaction prediction based on a homogeneous graph

cpi_prediction

This is a code for compound-protein interaction (CPI) prediction based on a graph neural network (GNN) for compounds and a convolutional neural network (CNN) for proteins.

csi

datadta

deepbindgcn

deepcda

DeepCDA

deepdock

Code related to : O. Mendez-Lucio, M. Ahmad, E.A. del Rio-Chanona, J.K. Wegner, A Geometric Deep Learning Approach to Predict Binding Conformations of Bioactive Molecules

deepdta

deepembedding-dti

deepfusiondta

Hybrid neural network and ensemble learning architecture via multi-source fusion for drug-target binding affinity prediction

deepglstm

deepmgt-dti

deepnc

deeppurpose

Forked from DeepPurpose

deeprmsd-vina_optimization

DeepRMSD+Vina is a computational framework that integrates ligand binding pose optimization and screening.

deepscreen

DEEPScreen: Virtual Screening with Deep Convolutional Neural Networks Using Compound Images

deepstack-dtis

DeepStack-DTIs: Predicting Drug-target Interactions Using LightGBM Feature Selection and Deep Stacked Ensemble Classifier

deep_compound_net

dgraphdta

a novel DTA predition method using graph neural network

dl-plbap

Structure-based, deep-learning models for PLBAP

dlm-dti_hint-based-learning

dlssaffinity

DLSSAffinity: protein-ligand binding affinity prediction via a deep learning model

dl_protein_ligand_affinity

From Proteins to Ligands: Decoding Deep Learning Methods for Binding Affinity Prediction

dnncc

dowker-complex-based-ml

drug-interaction-research

drug-target

drug-target-interaction-prediciton-method

This repository provides an implementation of the DTiGEMSplus tool, a network-based method for computational Drug-Target Interaction prediction using graph embedding, graph mining, and similarity-based techniques