Ultrafast shape recognition for similarity search in molecular databases

Abstract

Molecular databases are routinely screened for compounds that most closely resemble a molecule of known biological activity to provide novel drug leads. It is widely believed that three-dimensional molecular shape is the most discriminating pattern for biological activity as it is directly related to the steep repulsive part of the interaction potential between the drug-like molecule and its macromolecular target. However, efficient comparison of molecular shape is currently a challenge. Here, we show that a new approach based on moments of distance distributions is able to recognize molecular shape at least three orders of magnitude faster than current methodologies. Such an ultrafast method permits the identification of similarly shaped compounds within the largest molecular databases. In addition, the problematic requirement of aligning molecules for comparison is circumvented, as the proposed distributions are independent of molecular orientation. Our methodology could be also adapted to tackle similar hard problems in other fields, such as designing content-based Internet search engines for three-dimensional geometrical objects or performing fast similarity comparisons between proteins. From a broader perspective, we anticipate that ultrafast pattern recognition will soon become not only useful, but also essential to address the data explosion currently experienced in most scientific disciplines.

PHoS: Persistent Homology for Virtual Screening

“…We tested both USR [7] and PHoS in the manner described by Shin et al [63], using the Enrichment Factor (EF). As in their case, we define EF α % as follows:…”

“…For reference, USR [7] (using an open source Python implementation [15]) was also run against the Cleves and Jain dataset. Summary results for both PHoS and USR are displayed in Table 1, and more detailed information for PHoS with partial charge and USR is included in the Supplemental Information.…”

How reliable are ligand-centric methods for Target Fishing?

“…This similarity can be in terms of 2D chemical structure (Nettles et al, 2006) using circular fingerprints (Rogers and Hahn, 2010), 3D molecular properties (Cortés-Cabrera et al, 2013) using Ultrafast Shape Recognition variants (Ballester and Richards, 2007;Armstrong et al, 2010;Ballester, 2011) or NCI-60 bioactivity spectra (Holbeck et al, 2010) using cellular fingerprints (Füllbeck et al, 2009). Note that there are similarity-based methods that are not ligand-centric.…”

Application of Kernel Functions for Accurate Similarity Search in Large Chemical Databases

“…There are two approaches for similarity search for 2D or 3D structure of biomolecues. Most 3D structure based approaches compare threedimensional shapes using a range of molecular descriptors [5] [6]. Such methods provide fast query processing in large chemical databases but relatively poor accuracy since such methods may lost much of the structure information during compressing the three-dimensional shapes.…”