Project Details
Projekt
Reliable scoring and synthesis of bioactive molecules beyond combinatorial chemical space
Subject Area
Theoretical Chemistry: Electronic Structure, Dynamics, Simulation
Term
since 2022
Project identifier
Deutsche Forschungsgemeinschaft (DFG) - Project number 497135079
Computer-aided drug design faces three intertwined computational challenges: the ligand must be functional in its interaction with the designated target, it must be synthetically accessible, and it must be sufficiently different from known compounds to avoid e.g. competing patents. In the first funding phase we have demonstrated that these goals can be achieved in principle by means of an evolutionary search algorithm. In the second phase we will further strengthen the search abilities in vast chemical spaces going beyond the limits of simple combinatorial make-on-demand libraries. To address the need for a more reliable scoring regime for molecules, we propose a novel machine learning model based on electron density predictions with which we intend to divide protein-ligand complexes into distinctive interactions. These interactions should form the basis of a robust scoring function rooted in physicochemical rules. In addition, we found that target specific chemical space increases docking scores tremendously. This allows reduced sampling and therefore faster runtimes without lower hit rates. However, it does reintroduce the need for chemical synthesis. The search process itself will therefore be restricted to enforce synthesizability by modifying the cut-and-join crossover developed in the first phase to respect the transformation patterns of chemical reactions, invoking graph transformation grammars. Search trees leading to good candidates, moreover, will be systematically simplified using a data-driven approach to further reduce synthetic pathways to reduce the cost and complexity of synthesis.
DFG Programme
Priority Programmes
