Web Interface for ChEMBL @ EMBL-EBI

Search across publicly available datasets to find instances where a drug or cell line of interest has been profiled.

Targeted and non-targeted anticancer drugs and drug regimens

Automatic extraction of interacting compound-target pairs from ChEMBL.

Benchmarking study of machine learning methods for prediction of synthetic lethality

Initial data release for drug-target interactions of the cancer targetome.

Repository for the HackBio'2021 Internship for Team Drug-Development-A

Official implementation of DrugGEN in PyTorch

Integration of pharmacological and drop-out CRISPR-Cas9 screens

KR4SL is a machine learning method that leverages knowledge graph reasoning to predict synthetic lethality (SL) partners for a given primary gene, capturing the structural semantics of a knowledge graph by efficiently constructing and learning from relational digraphs.

Search across publicly available datasets to find instances where a drug or cell line of interest has been profiled.

PyMOL extension to map a protein pocket, generated from PockDrug, to the full protein structure.

Description of work done at Merck pharmaceutical company in the summer of 2018 as a Computational Drug Discovery Intern at West Point, PA. Information excludes all proprietary information belonging to Merck & Co.

This repository contain all the file necessary to download, compute and statistically validate the pairwise interaction between Drug side effects and Drug Targets

Repository for Multi Omics data Integration for Transcriptomics and Metabolomics in RA mouse models

Retrieval of the disease association and evidence from the Open Targets Platform, annotation of the association evidence through Clinvar, Sequence Ontology and PanelApp APIs, and finally filter and prioritise the protective targets when they possess LoF.

Personalised scoring system for lung cancer patients that enables ranking of the activated interventional nodes.

Using Multi-Label KNN to predict drug activity

The Sample MATLAB Code and dataset for “Systems Biology and Machine Learning Approaches Identify Drug Targets in Diabetic Nephropathy” paper, by Maryam Abedi, Hamid Reza Marateb, Mohammad Reza Mohebian, Seyed Hamid Aghaee-Bakhtiari, Seyed Mahdi Nassiri, and Yousof Gheisari, submitted to Scientific Reports.