/tools/QSAR-activity-cliff-experiments
QSAR-activity-cliff-experiments
MarkusFerdinandDablander/QSAR-activity-cliff-experiments
summary
This repository explores QSAR models for predicting activity cliffs in small-molecule inhibitors, providing datasets and methodologies for molecular property prediction. It includes clean data for various targets and allows for the reproduction of experiments related to binding affinity and activity classification.
description
Exploring QSAR Models for Activity-Cliff Prediction
topics
deep-learningextended-connectivity-fingerprintsgraph-neural-networksmachine-learningmolecular-property-predictionmolecular-representationqsar-modelsactivity-cliff-predictionactivity-cliffsbinding-affinity-predictionphysicochemical-descriptorsgraph-isomorphism-networkscircular-fingerprintsmorgan-fingerprintsk-nearest-neighboursmultilayer-perceptronsrandom-forestssupervised-machine-learning
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