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MolTrans: Molecular Interaction Transformer for Drug Target Interaction Prediction (Bioinformatics)
Effective drug-target interaction prediction with mutual interaction neural network
NeoDTI: Neural integration of neighbor information from a heterogeneous network for discovering new drug-target interactions
An end-to-end heterogeneous graph representation learning-based framework for drug-target interaction prediction
This is The repository for Paper "AttentionSiteDTI: Attention Based Model for Predicting Drug-Target Interaction Using Graph Representation of Ligands and 3D Structure of Protein Binding Sites"
Awesome Papers About Performing Prompting On Graphs
GraphPrompt: Unifying Pre-Training and Downstream Tasks for Graph Neural Networks
Sequence-to-drug concept adds a perspective on drug design. It can serve as an alternative method to SBDD, particularly for proteins that do not yet have high-quality 3D structures available.
TransformerCPI: Improving compound–protein interaction prediction by sequence-based deep learning with self-attention mechanism and label reversal experiments(BIOINFORMATICS 2020) https://doi.org/1…
SchNet - a deep learning architecture for quantum chemistry
cG-SchNet - a conditional generative neural network for 3d molecular structures
A curated list of papers on pre-training for graph neural networks (Pre-train4GNN).
A Unified Python Library for Graph Prompting
[ICLR 2023] One Transformer Can Understand Both 2D & 3D Molecular Data (official implementation)
SMILES enumeration for QSAR modelling using LSTM recurrent neural networks
A Deep Learning Toolkit for DTI, Drug Property, PPI, DDI, Protein Function Prediction (Bioinformatics)
The official implementation of 3D Equivariant Diffusion for Target-Aware Molecule Generation and Affinity Prediction (ICLR 2023)
A Spatial-temporal Gated Attention Module for Molecular Property Prediction Based on Molecular Geometry
A unified framework for predicting drug-target interactions, binding affinities and activation/inhibition mechanisms.