A primer on why computational predictive toxicology is hard

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

• Sri Nandan Gondi

related reading

• A primer on why computational predictive toxicology is hardsubstack.com
• A Conversation With Navvye Anand (Bindwell) | Rowanrowansci.com
• Generative ML in chemistry is bottlenecked by synthesisowlposting.com
• We Need Better Benchmarks for Machine Learning in Drug Discoverypracticalcheminformatics.blogspot.com
• Gap Mapgap-map.org
• The ML drug discovery startup trying really, really hard to not cheat (Leash Bio)owlposting.com
• Practical Cheminformatics Index - Practical Cheminformaticspatwalters.github.io
• So where are we with deep learning for biochem?ladanuzhna.xyz
• TorchDrug: A Powerful and Flexible Machine Learning Platform for Drug Discoveryarxiv.org
• OCTO - Noetiknoetik.ai
• The coming age of data-driven medicine: translational bioinformatics' next frontierpmc.ncbi.nlm.nih.gov
• Models of Lifepress.asimov.com