Official implementation of DrugGEN
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Updated
May 27, 2024 - Python
Official implementation of DrugGEN
Providing interactions between drugs and genes sourced from a variety of publications and knowledgebases
Targeted and non-targeted anticancer drugs and drug regimens
A powerful abstraction of gene databases
DEEPScreen: Virtual Screening with Deep Convolutional Neural Networks Using Compound Images
Using ESM2 protein embeddings and MolecularTransformer drug embeddings to train a linear classifier to predict potential drug-targets interactions
HyperPCM: Robust task-conditioned modeling of drug-target interactions
Automatic extraction of interacting compound-target pairs from ChEMBL.
A PyTorch-based system for highly accurate drug-target interaction predictions utilizing multi-modal large language models to discern structural affinities in drug-target pairs.
ANN4EEG is a convolutional neural network toolkit for EEG-mediated drug-target interaction prediction. This approach for drug-target interaction prediction can explain the mechanisms underlying complicated drug actions, as it allows the identification of similarities in the mechanisms of action and effects of psychotropic drugs.
DeepDrugDomain: A versatile Python toolkit for streamlined preprocessing and accurate prediction of drug-target interactions and binding affinities, leveraging deep learning for advancing computational drug discovery.
My Machine Learning Projects
DEEPScreen: Virtual Screening with Deep Convolutional Neural Networks Using Compound Images-with test scripts
A Deep Learning Toolkit for DTI, Drug Property, PPI, DDI, Protein Function Prediction (Bioinformatics)
Integrated View Predictive GAN for Drug-Target Indication Prediction
Official implementation of "Weighted Weighted Edit Distance optimized using Genetic Algorithm for SMILES-based Compound Similarity, PAAA(SCIE)".
A Knowledge Graph to Illuminate the Druggable Genome
Electrophysiology Chemical Drug Signal Identification, Fentanyl data...
Fetch pharmacological knowledge on chemical compounds and squeeze it into semantic triples. For analysis of chemical screens and training and evaluation of algorithms.
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