Ligand Strain Energy in Large Library Docking

While
small molecule internal strain is crucial to molecular docking,
using it in evaluating ligand scores has remained elusive. Here, we
investigate a technique that calculates strain using relative torsional
populations in the Cambridge Structural Database, enabling fast precalculation
of these energies. In retrospective studies of large docking screens
of the dopamine D4 receptor and of AmpC β-lactamase, where close
to 600 docking hits were tested experimentally, including such strain
energies improved hit rates by preferentially reducing the ranks of
strained high-scoring decoy molecules. In a 40-target subset of the
DUD-E benchmark, we found two thresholds that usefully distinguished
between ligands and decoys: one based on the total strain energy of
the small molecules and another based on the maximum strain allowed
for any given torsion within them. Using these criteria, about 75%
of the benchmark targets had improved enrichment after strain filtering.
Relying on precalculated population distributions, this approach is
rapid, taking less than 0.04 s to evaluate a conformation on a standard
core, making it pragmatic for precalculating strain in even ultralarge
libraries. Since it is scoring function agnostic, it may be useful
to multiple docking approaches; it is openly available at http://tldr.docking.org.