Prediction of compound toxicity is essential because covering the vast chemical space requiring safety assessment using traditional experimentally-based, resource-intensive techniques is impossible. However, such prediction is nontrivial due to the complex causal relationship between compound structure and in vivo harm. Protein target annotations and in vitro experimental outcomes encode relevant bioactivity information complementary to chemicals’ structures. This presentation will describe general principles, and then move n to a concrete study (DOI 10.1039/C5TX00406C) which has shown that utilizing three complementary types of data will afford predictive models that outperform traditional models built using fewer data types.
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