Shape-Based Descriptors for Efficient Structure-Based Fragment Growing

Abstract

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.

Frag4Lead: growing crystallographic fragment hits by catalog using fragment-guided template docking

“…However, for large virtual chemical spaces with billions of compounds, the computational cost will increase. This might require more efficient prefiltering to remove sterically incompatible follow-up candidates prior to docking, for example by employing the recently described shape-based descriptors (Penner et al, 2020).…”

FastGrow: on-the-fly growing and its application to DYRK1A

“…FastGrow is primarily based on the Ray Volume Matrix (RVM) shape descriptor [ 18 ]. RVM shape screening is a fast way to generate accurate poses for thousands of fragments in a few seconds.…”

“…5 Performance comparison of DOCK in a "Cross Docking" and an "Anchored Docking" to FastGrow with or without subsequent restrained JAMDA optimization. The error bars are 95% confidence intervals would have rejected [18]. Restrained JAMDA optimization then resolved these clashes with minor geometry adjustments.…”

“…In this work we will describe our fragment growing workflow FastGrow based on the Ray Volume Matrix (RVM) shape descriptor [ 18 ], a pharmacophoric interaction description, and JAMDA geometry optimization [ 19 ]. Its features will be statistically evaluated on a previously reported data set of fragment growing steps extracted from crystallographic data [ 18 ] and compared to DOCK, a well-known, open source docking suite [ 20 ].…”

Report on an NIH Workshop on Ultralarge Chemistry Databases

“…The classical paradigm of virtual screening (Table 1) now fails due to the sheer number of compounds contained in today's huge make-on-demand compound libraries. To address this problem, SpaceLight 90 uses classic extended connectivity fingerprints (ECFP) 35 and connected subgraph fingerprints (CSFP) 91 in a new search approach for similarity searching. There are three versions of CSFP: fCSFP for fine-grained similarity measurement, iCSFP, an MCS-like descriptor, and tCSFP with scaffold-hopping potential.…”