LASSO—ligand activity by surface similarity order: a new tool for ligand based virtual screening

Abstract

Virtual Ligand Screening (VLS) has become an integral part of the drug discovery process for many pharmaceutical companies. Ligand similarity searches provide a very powerful method of screening large databases of ligands to identify possible hits. If these hits belong to new chemotypes the method is deemed even more successful. eHiTS LASSO uses a new interacting surface point types (ISPT) molecular descriptor that is generated from the 3D structure of the ligand, but unlike most 3D descriptors it is conformation independent. Combined with a neural network machine learning technique, LASSO screens molecular databases at an ultra fast speed of 1 million structures in under 1 min on a standard PC. The results obtained from eHiTS LASSO trained on relatively small training sets of just 2, 4 or 8 actives are presented using the diverse directory of useful decoys (DUD) dataset. It is shown that over a wide range of receptor families, eHiTS LASSO is consistently able to enrich screened databases and provides scaffold hopping ability.

LASSO-ing Potential Nuclear Receptor Agonists and Antagonists: A New Computational Method for Database Screening

“…Using dataset 2 to predict dataset 1 gives similar results. These results suggest that the models could identify potential human PXR agonists in databases similar to other target proteins [59].…”

“…The molecular descriptors used in eHiTS LASSO are independent of ligand conformation and have been shown to successfully enrich screened databases across a wide range of target families [59]. Lying somewhere between a 2D and a 3D descriptor the ISPT descriptor does not contain any shape or 2D connectivity information.…”

“…The methodology of LASSO has already been previously described in detail, [59] and the method performance in terms of diversity of test set and % enrichment of a database has also already been evaluated for the DUD set in paper just mentioned and also for other targets published elsewhere (http://www .simbiosys.com/ehits lasso/ehits lasso table.html) to examine the performance of eHiTS LASSO, with this endocrine panel subset of target proteins of the total ∼48 nuclear receptors. We used the newly assembled directory of useful decoys (DUD) [60] dataset to augment both the KIERBL and NCTR AR datasets.…”

“…There is a continuous search for new methods that might offer advantages for computational modeling to overcome some of these limitations and specifically for NRs [58]. A ligand-based software called LASSO (Ligand Activity by Surface Similarity Order) has been described that is focused on similarities in biomolecular activity rather than structural similarity [59]. The key components describing LASSO are the 23 kinds of Interacting Surface Point Type (ISPT) molecular descriptors (see Supplemental Table 1 available online at http://dx.doi.org/10.1155/2013/513537), which capture the essence of the surface point information in a feature vector containing the counts of each surface point type and create the feature vector for that ligand.…”

“…A key property of the LASSO descriptor is its conformation independence which is due to the fact that it is defined by the number and type of Interacting Surface Points and not by their relative spacial distribution. LASSO has been shown to be able to readily screen over 1 million structures/ minute, identify active molecules by enriching screened databases, and provide a means for scaffold hopping [59]. The current study applies LASSO to various NR datasets to generate models, validate them, and make the models available on a public website to illustrate how the method can be used.…”