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

Web Interface for ChEMBL @ EMBL-EBI

Targeted and non-targeted anticancer drugs and drug regimens

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

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

Automatic extraction of interacting compound-target pairs from ChEMBL.

Official implementation of DrugGEN in PyTorch

Integration of pharmacological and drop-out CRISPR-Cas9 screens

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

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.

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

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

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

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

Using Multi-Label KNN to predict drug activity

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.

MDM2pred is a machine learning application based on the KNNRegressor algorithm, it's trained on 1647 known inhibitors of the human E3 ubiquitin ligase (Mouse Double Minute 2; MDM2), the primary negative regulator of the well-known tumor suppressor p53. The KNN model backing MDM2pred achieves ~0.74 R² on test compounds (cross-validated) and has a…

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.

Analysis of COVID19 Candidates using molecular descriptors from mol2 files