August 11, 2021 A team of researchers in Barcelona have gathered bioactivity information for a million molecules using deep machine-learning computational models and a database of experimental results. Both the experimental results and the machine learning tool are available to the community at bioactivitysignatures.org. The work originated with the Structural Bioinformatics and Network Biology laboratory at the Institute for Research in Biomedicine (IRB) in Barcelona, Spain. In May 2020, the team published in Nature Biotechnology an integration of the major chemogenomics and drug databases including ChEMBL and DrugBank (DOI: 10.1038/s41587-020-0502-7). The result is Chemical Checker (CC), a database that includes processed, harmonized, and integrated bioactivity data on more than 800,000 small molecules.

In the COVID era, computational biology is having a heyday – and machine learning is playing a massive role. With billions upon billions of compounds to search through for any given therapeutic application, strictly brute-force simulations are wildly unfeasible, necessitating more artificially intelligent methods of whittling down the options. Now, researchers from IRB Barcelona's Structural Bioinformatics and Network Biology lab have developed a deep learning method that predicts the biological activity of any given molecule – even in the absence of experimental data. The researchers, led by Patrick Aloy, are applying deep machine learning to a massive dataset: the Chemical Checker, which provides processed, harmonized, and integrated bioactivity data on 800,000 small molecules and is also produced by the Structural Bioinformatics and Network Biology lab. In total, any given molecule has 25 bioactivity "spaces," but for most molecules, data on only a few are known – if that.