Machine-Learning-guided chemical space exploration: automatic creation and navigation of ultra-large open-source molecular libraries

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The “chemical space” formed by all drug-like molecules contains an estimated 10 to the power of 60 compounds, a number too large to ever synthesise one of each. In this project we will tackle two challenges. First, how can we discover concretely which molecules are contained in chemical space or at least a therapeutically relevant portion thereof? Second, how can we search such large spaces with protein-structure-based in silico methods? Our strategy is based on our database of virtually synthesised compounds, SCUBIDOO, and we will develop algorithms to identify novel robust and broadly applicable chemical reactions as well as filters to increase synthesis success rates. This will substantially increase the size of publicly available easily accessible chemical space. For navigating this huge space, we will develop evolutionary algorithms that will help us identify promising ligands in an efficient way. Moreover, we will develop a deep-learning based method in order to store the opinion of an expert about the fit of each potential ligand in a protein binding pocket. In this way, we will be able to preserve knowledge and also apply it to molecule numbers that are out of reach for a single human being. Both arms of the project together will open the door for fast and comprehensive chemical space exploration.