Extending the small-molecule similarity principle to all levels of biology with the Chemical Checker
By Miquel Duran-Frigola, E. Pauls, Oriol Guitart-Pla, Martino Bertoni, Víctor Alcalde, David Amat, Teresa Juan-Blanco, Patrick Aloy. 2020
114 citations recorded by the source corpus.
Small molecules are usually compared by their chemical structure, but there is no unified analytic framework for representing and comparing their biological activity. We present the Chemical Checker (CC), which provides processed, harmonized and integrated bioactivity data on ~800,000 small molecules. The CC divides data into five levels of increasing complexity, from the chemical properties of compounds to their clinical outcomes. In between, it includes targets, off-targets, networks and cell-level information, such as omics data, growth inhibition and morphology. Bioactivity data are expressed in a vector format, extending the concept of chemical similarity to similarity between bioactivity signatures. We show how CC signatures can aid drug discovery tasks, including target identification and library characterization. We also demonstrate the discovery of compounds that reverse and mimic biological signatures of disease models and genetic perturbations in cases that could not be addressed using chemical information alone. Overall, the CC signatures facilitate the conversion of bioactivity data to a format that is readily amenable to machine learning methods. The biological activities of >800,000 small molecules are represented within a uniform framework.
Subfields: Computational Theory and Mathematics, Molecular Biology, Pharmacology
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Citations in the corpus
- HERB: a high-throughput experiment- and reference-guided database of traditional Chinese medicine (2020, background): “Having a complete understanding of the molecular effects of chemical compounds facilitates strategic selection of candidates for advancement in modern drug discovery (1).”
- Dynamic-Backbone Protein-Ligand Structure Prediction with Multiscale Generative Diffusion Models (2022, background): “…based on the Frame Aligned Point Error (FAPE) [9]: where L CC − regression is a mean squared loss for fitting the "level 1" chemical checker (CC) [61] embeddings which represents harmonized and integrated bioactivity data, and L CC − ismask is an auxiliary binary cross entropy loss for…”