A tool for evaluating the predictive performance on activity cliff compounds of machine learning models
Molecule Activity Cliff Estimation (MoleculeACE) is a tool for evaluating the predictive performance on activity cliff compounds of machine learning models.
MoleculeACE can be used to:
- Analyze and compare the performance on activity cliffs of machine learning methods typically employed in
QSAR. - Identify best practices to enhance a model’s predictivity in the presence of activity cliffs.
- Design guidelines to consider when developing novel QSAR approaches.
Benchmark study
In a benchmark study we collected and curated bioactivity data on 30 macromolecular targets, which were used to evaluate
the performance of many machine learning algorithms on