Indexing Ultrafast Shape-Based Descriptors in MongoDB to Identify TLR4 Pathway Agonists

A method is presented for an ultrafast
shape-based search workflow
for the screening of large compound collections, i.e., those of vendors.
The three-dimensional shape of a molecule dictates its biological
activity by enabling the molecule to fit into binding pockets of proteins.
Quite often, distinctly different chemical compounds that have similar
shapes can bind in a similar way. OpenEye pioneered an algorithm for
comparing shapes of molecules by overlaying them in a computer and
measuring differences between a query molecule and a target molecule.
Overlaying shapes is a computationally intensive process and represents
a bottleneck in searching for similar molecules. More recent publications
describe alternative methods of overlaying molecules, which are accomplished
by comparing shape-based descriptors. These methods were implemented
in the Open Drug Discovery Toolkit (ODDT) package. We utilized a combination
of open-source software packages like ODDT and RDkit to implement
a workflow for ultrafast conformer generation and matching that does
not require storing precomputed conformers on the file system or in
memory. Moreover, the generated descriptors could be optionally stored
in MongoDB for performing searches in the future. To speed up the
search, we created a set of indexes from the transformed shape-based
descriptors. We are in the process of calculating descriptors for
multiple vendors, including Enamine’s “REAL”
collection of 1.2 billion compounds. Currently, the shape similarity
search on more than 70 million compounds takes less than 8 s! We exemplified
our methodology with the screen of compounds that can act as putative
TLR4 agonists. The search was based on a literature-known small-molecule
TLR4 agonist series. In due course, we identified compounds with novel
structural motifs that were active in mouse and human TLR4 reporter
cell lines.