  Ultra-large make-on-demand compound libraries now contain billions of readily
available compounds. This represents a golden opportunity for in-silico drug
discovery. One challenge, however, is the time and computational cost of an
exhaustive screen of such large libraries when receptor flexibility is taken
into account. We propose an evolutionary algorithm to search combinatorial
make-on-demand chemical space efficiently without enumerating all molecules. We
exploit the feature of make-on-demand compound libraries, namely that they are
constructed from lists of substrates and chemical reactions. Our novel
algorithm RosettaEvolutionaryLigand (REvoLd) explores the vast search space of
combinatorial libraries for protein-ligand docking with full ligand and
receptor flexibility through RosettaLigand. A benchmark of REvoLd on five drug
targets showed improvements in hit rates by factors between 869 and 1,622
compared to random selections. REvoLd is available as an application within the
Rosetta software suite.
