LARN group

De-Novo-Drug-Design

Optimizing blood-brain barrier permeation through Deep Reinforcement Learning for de novo drug design

DTITR

DTITR: End-to-End Drug-Target Binding Affinity Prediction with Transformers

DiverseDRL

GAN-Drug-Generator

Generative Adversarial Network: Optimization in Targeted Design

Explainable-Deep-DT-Representations

Explainable Deep Drug-Target Representations for Binding Affinity Prediction

FSM-DDTR

TAG-DTA

TAG-DTA: Binding Region-Guided Strategy to Predict Drug-Target Affinity Using Transformers

Agnostic_DL_Protein_Protein_Interaction

Dataset agnostic deep learning architectures to predict protein-protein interactions

DTI-End-to-End-DL

Drug-Target Interaction Prediction: End-to-End Deep Learning

One_Shot_Siamese_Net_Drug_Discovery

RNN-Drug-Generation

A Study on Recurrent Architecture for De Novo Drug Generation

transf_rl_usp7

FS-GCvTR

Convolutional Transformer via Graph Embeddings for Few-shot Toxicity and Side Effect Prediction

targeted_generation_stereo

Prediction of Biological Activity and Generation of molecular hits using Stereochemical Information

-GCNNs-

CRoP

DeepModel-Anticancer-Predictor

Deep Modelling for Anticancer Drug Response through Genomic Profiles and Compound Structures

DTIpred

Drug-Target Interaction Prediction

KL_divergence_loss

Variational Autoencoder with kl divergence loss function implemented with tensorflow and keras

larngroup.github.io

Multiobjective Reinforcement Learning Description

MBTR

Metal-Binding Transformer - Zinc Application

moleculardocking

molecular docking