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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 free-text. It's a dizzying rate of progress.

But there's a few problems in biology that, interestingly enough, have evaded a similar level of ML progress, despite there seemingly being all the necessary conditions to achieve it.

Toxicology is one of those problems.

This isn’t a new insight, it was called out in one of Derek Lowe's posts, where he said: There are no existing AI/ML systems that mitigate clinical failure risks due to target choice or toxicology. He also repeats it in a more recent post: ‘…the most badly needed improvements in drug discovery are in the exact areas that are most resistant to AI and machine learning techniques. By which I mean target selection and predictive toxicology.’ [...]

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Outline:

(00:18) Introduction

(02:09) Some background

(06:21) The hard stuff

(06:24) The relevance of toxicity datasets to the clinical problem

(11:38) Methodological problems in toxicity datasets

(12:39) Intraspecies toxicity variability

(18:14) Toxicity synergism

(22:00) Conclusion

The original text contained 3 images which were described by AI.

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“A primer on why computational predictive toxicology is hard ” by Abhishaike Mahajan

LessWrong (30+ Karma)