Official Python client for accessing ChEMBL API

Access to Biological Web Services from Python.

Write reproducible code for getting and processing ChEMBL

Web Interface for ChEMBL @ EMBL-EBI

Go REST API to replace Genbank, Uniprot, Rhea, and CHEMBL

Open-Sourced. Millions of small molecules will be downloaded from the ZINC database in 3D SDF file format, with one click, to get ready for a large scale virtual screening for certain protein target.

Fetch pharmacological knowledge on chemical compounds and squeeze it into semantic triples. For analysis of chemical screens and training and evaluation of algorithms.

Machine learning project to evaluate machine learning approaches in drug discovery.

An iterative process that uses two machine learning models to generate the best inhibitors for a target protein to help reduce the time and cost of the drug discovery process

Open Source, machine learning QSAR model with public data or your local data, The model utilises molecular descriptors as the independent variable, bioactivity as the dependent variable, random forest as a mathematical model.

A Python script predicting pIC50 values of compounds obtained from the ChEMBL database against the target protein, aromatase. The molecular descriptor used in this script is PubChem fingerprint.

A Drug Discovery method using CHEMBL database, Lipinski Parameters, molecular fingerprint (padel) for SARS coronavirus 3C-like proteinase.

Classification of bioactivity and effectiveness of the drug based on Lipinski molecular descriptors for ABL-kinase using Machine learning classfiers

MINDPROT: Markov Inside for Drugs and Proteins

Scripts that enable researchers to parse through ChEMBL bioactivity reports in relation to a specific biological target and provides insight into some important for Computer-Aided Drug Design (CADD) features.

This scripts tries to predict the bioactivity of 131 compounds related to Aspartate Racemase enzyme with the aid of decision trees and SVM

explorations with sparql

Code to retrieve drugs against a desired target using the ChEMBL database API

Supervised classification algorithm to predict if potential drug candidate will inhibit a protein involved in cancer metastasis, achieving 84% accuracy.