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Why computational predictive toxicology is hard
3.4k words, 16 minutes reading time
The existence of easily recognizable toxicophores spurned interest in establishing mappings between facets of a chemical structure and the physiological impact it had on organisms, leading to a field of study called ‘Quantitative Structure-Activity Relationship’, or QSAR. As with every other field, the explosion of deep learning led to a pivot — i nstead of working with derived features understandable to a chemist, neural networks were instead given the raw molecule as input, represented in either 2D or 3D space, building their own conception of what is/isn’t important for the problem of toxicity. The dramatic impact of microbial metabolism on the toxicity of metabolites derived from drugs was clearly manifested in the death of fifteen patients, who were orally administered with sorivudine (SRV, 1-b-d-arabinofuranosyl-(E)-5-(2-bromovinyl) uracil) within forty days.
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