Shape-Based Descriptors for Efficient Structure-Based Fragment Growing

Structure-based
fragment growing is one of the key techniques in
fragment-based drug design. Fragment growing is commonly practiced
based on structural and biophysical data. Computational workflows
are employed to predict which fragment elaborations could lead to
high-affinity binders. Several such workflows exist but many are designed
to be long running noninteractive systems. Shape-based descriptors
have been proven to be fast and perform well at virtual-screening
tasks. They could, therefore, be applied to the fragment-growing problem
to enable an interactive fragment-growing workflow. In this work,
we describe and analyze the use of specific shape-based directional
descriptors for the task of fragment growing. The performance of these
descriptors that we call ray volume matrices (RVMs) is evaluated on
two data sets containing protein–ligand complexes. While the
first set focuses on self-growing, the second measures practical performance
in a cross-growing scenario. The runtime of screenings using RVMs
as well as their robustness to three dimensional perturbations is
also investigated. Overall, it can be shown that RVMs are useful to
prefilter fragment candidates. For up to 84% of the 3299 generated
self-growing cases and for up to 66% of the 326 generated cross-growing
cases, RVMs could create poses with less than 2 Å root-mean-square
deviation to the crystal structure with average query speeds of around
30,000 conformations per second. This opens the door for fast explorative
screenings of fragment libraries.