The Structural Bioinformatics and Network Biology laboratory, led by ICREA Researcher Dr. Patrick Aloy, at IRB Barcelona has completed the bioactivity information for a million molecules using deep machine-learning computational models. It has also disclosed a tool to predict the biological activity of any molecule, even when no experimental data are available. The work has been published in the journal Nature Communications.

This new methodology is based on the Chemical Checker, the largest database of bioactivity profiles for pseudo pharmaceuticals to date, developed by the same laboratory and published in 2020. The Chemical Checker collects information from 25 spaces of bioactivity for each molecule. These spaces are linked to the chemical structure of the molecule, the targets with which it interacts or the changes it induces at the clinical or cellular level. However, this highly detailed information about the mechanism of action is incomplete for most molecules, implying that for a particular one there may be information for one or two spaces of bioactivity but not for all 25.

With this new development, researchers integrate all the experimental information available with deep machine learning methods, so that all the activity profiles, from chemistry to clinical level, for all molecules can be completed.

“The new tool also allows us to forecast the bioactivity spaces of new molecules, and this is crucial in the drug discovery process as we can select the most suitable candidates and discard those that, for one reason or another, would not work,” explains Dr. Patrick Aloy.

The software library is freely accessible to the scientific community at bioactivitysignatures.org and it will be regularly updated by the researchers as more biological activity data become available. With each update of experimental data in the Chemical Checker, artificial neural networks will also be revised to refine the estimates.

» More information: IRB Barcelona website [+]