Mechanistic QSAR models for key human health endpoints
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Updated
Dec 14, 2022
Mechanistic QSAR models for key human health endpoints
Master Thesis Unisa-Ensicaen
Nextcast: a software suite to analyse and model toxicogenomics data
Classify acetylcholinesterase inhibitor with LightGBM
Reference implementation of the Vanishing Ranking Kernels (VRK) method
A machine learning app to assess the aggregation potential of Small Colloidally-Aggregating Molecules (SCAMS).
QSAR Bioactivity Predictor is a Python application that allows users to create QSAR models to predict bioactivity for a specific target.
Supplementary repository to the publication "Hybrid machine learning and experimental studies of antiviral potential of ionic liquids against P100, MS2, and Phi6"
ProtMetrics is a library to compute molecular descriptors that can be used for QSAR and machine learning modeling.
This repository contains a QSAR model that predicts the ability of a chemical compound to inhibit the gene associated with Alzheimer's, Beta-Secratese 1
QSAR models and data used for MAO-A and MAO-B virtual screening.
Prediction of partition coefficient
self learning and reference material on QSAR moleculear modelling
Descriptive analysis and QSAR modelling for tox_21 datasets
Training data for "Prediction of clinically relevant drug-induced liver injury from structure using machine learning" (Hammann et al., J Appl Toxicol . 2019 Mar;39(3):412-419)
Machine learning models to obtain drug candidates for Diabetes and Pompe's disease
AI-based Quantitative structure Activity relationship study for Alzheimer's disease
Tools for converting Biobyte QSAR database to SQL
Predict Skin Irritation based on pIC50 using command-line tool application
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