Multi-modal co-attention for drug-target interaction annotation and Its Application to SARS-CoV-2

Last update: Nov 14, 2022

Related tags

Deep Learning CoaDTI

Overview

CoaDTI

Multi-modal co-attention for drug-target interaction annotation and Its Application to SARS-CoV-2

Abstract

Environment

The test was conducted in the linux server with GTX2080Ti and the running environment is as follows:

python 3.7
pytorch 1.7.1
rdkit
pytorch geometric 1.6.3
Cuda 10.0.130

Data

Human dataset

C.elegans dataset

Binding_DB dataset

How to run

CoaDTI

Run ./code/data_prepare.py to preprocess the dataset.
Run ./code/train.py to train the CoaDTI.

CoaDTI-pro

Run ./code/data_prepare.py to preprocess the dataset.
Run ./code/train.py to train the CoaDTI-pro.

Owner

Layne_Huang

Bioinformatics, BioNLP

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Multi-modal co-attention for drug-target interaction annotation and Its Application to SARS-CoV-2

Related tags

Overview

CoaDTI

Abstract

Environment

Data

Human dataset

C.elegans dataset

Binding_DB dataset

How to run

CoaDTI

CoaDTI-pro

Owner

Layne_Huang

Microscopy Image Cytometry Toolkit

Python package for missing-data imputation with deep learning

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Pre-training of Graph Augmented Transformers for Medication Recommendation

hySLAM is a hybrid SLAM/SfM system designed for mapping

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Code for KHGT model, AAAI2021

Info and sample codes for "NTU RGB+D Action Recognition Dataset"

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(CVPR2021) DANNet: A One-Stage Domain Adaptation Network for Unsupervised Nighttime Semantic Segmentation

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