Comparing Machine learning methods with Tuberculosis in vitro data
What is it about?
We curated a dataset of compounds tested against TB and used this to compare different classic machine learning algorithms with DL. We used a wide range of metrics, different fingerprints as well as internal and external test sets.
Why is it important?
TB kills around 1.7M people every year. There are issues with drug resistance and we need to discover new drugs. There is plenty of in vitro data and our rationale has been that we can learn from it and find new compounds to fill the pipeline. In this study we show that Bayesian was as good or better than deep learning on external test sets that were diverse.
The following have contributed to this page: Dr Sean Ekins