QPHAR: quantitative pharmacophore activity relationship: method and validation

Abstract

QSAR methods are widely applied in the drug discovery process, both in the hit‐to‐lead and lead optimization phase, as well as in the drug-approval process. Most QSAR algorithms are limited to using molecules as input and disregard pharmacophores or pharmacophoric features entirely. However, due to the high level of abstraction, pharmacophore representations provide some advantageous properties for building quantitative SAR models. The abstract depiction of molecular interactions avoids a bias towards overrepresented functional groups in small datasets. Furthermore, a well‐crafted quantitative pharmacophore model can generalise to underrepresented or even missing molecular features in the training set by using pharmacophoric interaction patterns only. This paper presents a novel method to construct quantitative pharmacophore models and demonstrates its applicability and robustness on more than 250 diverse datasets. fivefold cross-validation on these datasets with default settings yielded an average RMSE of 0.62, with an average standard deviation of 0.18. Additional cross-validation studies on datasets with 15–20 training samples showed that robust quantitative pharmacophore models could be obtained. These low requirements for dataset sizes render quantitative pharmacophores a viable go-tomethod for medicinal chemists, especially in the lead-optimisation stage of drug discovery projects.

Shaping the Future of Obesity Treatment: In Silico Multi-Modeling of IP6K1 Inhibitors for Obesity and Metabolic Dysfunction

“…To develop the structure-based pharmacophore models, we employed the recently introduced open-access tool, Quantitative Pharmacophore Activity Relationship (QPHAR) [ 42 ]. For each compound in the dataset, 50 conformations were generated using the genetic algorithm and Confab techniques separately, facilitated by the Open Babel software.…”

A new set of KNIME nodes implementing the QPhAR algorithm

“…We have previously published a novel method called QPhAR [3] that perceives and models quantitative relationships between biological activities and pharmacophores. It builds on the concept of merged-pharmacophores and machine-learning, whereas first, all the samples from the training set are aligned, their pharmacophore features put into a single container and clustered.…”

Applications of the Novel Quantitative Pharmacophore Activity Relationship Method QPhAR in Virtual Screening and Lead-optimization

“…In this paper we present a novel method for automated pharmacophore modelling given a previously trained and validated QPhAR [13] model. We show that it outperforms the commonly applied heuristics for pharmacophore model refinement and can reliably generate a set of three-dimensional (3D) pharmacophores that show high discriminatory power in the virtual screening process.…”

“…Feature contribution information derived from the QPhAR pharmacophore model: As explained in the QPhAR publication [13], the QPhAR algorithm associates each newly generated pharmacophore feature with a list of activities. These activities will not only be used to determine the relevance of the feature -whether it is actual information or just adds noise to the model -but also to determine the contribution of a pharmacophore feature to the models' predictions.…”