Machine learning project to evaluate machine learning approaches in drug discovery.
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Updated
May 13, 2019 - Python
Machine learning project to evaluate machine learning approaches in drug discovery.
Classification of bioactivity and effectiveness of the drug based on Lipinski molecular descriptors for ABL-kinase using Machine learning classfiers
This repository is for the project 1 of the AI course.
Supervised classification algorithm to predict if potential drug candidate will inhibit a protein involved in cancer metastasis, achieving 84% accuracy.
Working with ChEMBL Data Web Services to retrieve data.
This scripts tries to predict the bioactivity of 131 compounds related to Aspartate Racemase enzyme with the aid of decision trees and SVM
Web Interface for ChEMBL @ EMBL-EBI
Structured Multi-task Learning for Molecular Property Prediction, AISTATS'22 (https://proceedings.mlr.press/v151/liu22e.html)
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 Python package to process & model ChEMBL data.
Drug Discovery for Multiple Sclerosis Disease
HWs - "Computational Drug Design" - Sharif University of Technology (SUT) - Prof. Ali Mohammadian - Fall 2022 (1401-1).
MINDPROT: Markov Inside for Drugs and Proteins
Code to retrieve drugs against a desired target using the ChEMBL database API
AI-based Quantitative structure Activity relationship study for Alzheimer's disease
Fetch pharmacological knowledge on chemical compounds and squeeze it into semantic triples. For analysis of chemical screens and training and evaluation of algorithms.
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.
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