We Need Better Benchmarks for Machine Learning in Drug Discovery
practicalcheminformatics.blogspot.com · 4,234 words · saved by 1 readers
Most papers describing new methods for machine learning (ML) in drug discovery report some sort of benchmark comparing their algorithm and/o...
We Need Better Benchmarks for Machine Learning in Drug Discovery Get link Facebook X Pinterest Email Other Apps August 03, 2023 Most papers describing new methods for machine learning (ML) in drug discovery report some sort of benchmark comparing their algorithm and/or molecular representation with the current state of the art. In the past, I’ve written extensively about statistics and how methods should be compared . In this post, I’d like to focus instead on the datasets we use to benchmark and compare methods. Many papers I’ve read recently use the MoleculeNet dataset, rel
related reading
- Five things to keep in mind while reading biology ML papersowlposting.com
- Practical Cheminformatics Index - Practical Cheminformaticspatwalters.github.io
- The ML drug discovery startup trying really, really hard to not cheat (Leash Bio)owlposting.com
- Preface - The Emerging Science of Machine Learning Benchmarksmlbenchmarks.org
- TorchDrug: A Powerful and Flexible Machine Learning Platform for Drug Discoveryarxiv.org
- A primer on why computational predictive toxicology is hardowlposting.com
- A primer on why computational predictive toxicology is hardsubstack.com
- Gap Mapgap-map.org
- Devising ML Metrics | CAISsafe.ai
- So where are we with deep learning for biochem?ladanuzhna.xyz
- Giovanni D'Antoniogiovannidantonio.com
- Quantum Chemistry in Drug Discovery | Rowanrowansci.com