A primer on why computational predictive toxicology is hard
owlposting.com · 3,540 words · saved by 1 readers
3.4k words, 16 minutes reading time
Primers A primer on why computational predictive toxicology is hard 3.4k words, 16 minutes reading time Abhishaike Mahajan May 05, 2024 24 8 2 Share Introduction Some background The hard stuff The relevance of toxicity datasets to the clinical problem Methodological problems in toxicity datasets Intraspecies toxicity variability Toxicity synergism Conclusions Introduction There are now (claimed) foundation models for protein sequences , DNA sequences , RNA sequences , molecules , scRNA-seq , chromatin accessibility , pathology slides , medical images , electronic health records , and clinical
saved by
related reading
- A primer on why computational predictive toxicology is hardsubstack.com
- AI After Drug Developmentasteriskmag.substack.com
- Generative ML in chemistry is bottlenecked by synthesisowlposting.com
- So where are we with deep learning for biochem?ladanuzhna.xyz
- The ML drug discovery startup trying really, really hard to not cheat (Leash Bio)owlposting.com
- Why AI alignment could be hard with modern deep learningcold-takes.com
- Zayd's Blog - Why is machine learning 'hard'?ai.stanford.edu
- TorchDrug: A Powerful and Flexible Machine Learning Platform for Drug Discoveryarxiv.org
- AI for medicine is overhyped right now - by Dan Eltonmoreisdifferent.substack.com
- AI Safety Seems Hard to Measurecold-takes.com
- Democratizing Computer-Assisted Drug Design: How Are We Doing?corinwagen.github.io
- OCTO - Noetiknoetik.ai