Kohlbacher, S.M.; Schmid, M.; Seidel, T.; Langer, T. Applications of the Novel Quantitative Pharmacophore Activity Relationship Method QPhAR in Virtual Screening and Lead-Optimisation. Pharmaceuticals2022, 15, 1122.
Kohlbacher, S.M.; Schmid, M.; Seidel, T.; Langer, T. Applications of the Novel Quantitative Pharmacophore Activity Relationship Method QPhAR in Virtual Screening and Lead-Optimisation. Pharmaceuticals 2022, 15, 1122.
Cite as:
Kohlbacher, S.M.; Schmid, M.; Seidel, T.; Langer, T. Applications of the Novel Quantitative Pharmacophore Activity Relationship Method QPhAR in Virtual Screening and Lead-Optimisation. Pharmaceuticals2022, 15, 1122.
Kohlbacher, S.M.; Schmid, M.; Seidel, T.; Langer, T. Applications of the Novel Quantitative Pharmacophore Activity Relationship Method QPhAR in Virtual Screening and Lead-Optimisation. Pharmaceuticals 2022, 15, 1122.
Abstract
Pharmacophores are an established concept for the modelling of ligand-receptor interactions based on the abstract representations of stereoelectronic molecular features. They became widely popular as filters for the fast virtual screening of large compound libraries. Until today a lot of effort has been put into the development of sophisticated algorithms and strategies to increase the computational efficiency of the screening process. However, hardly any focus was put on the development of automated procedures that optimise pharmacophores towards higher discriminatory power, which until today still has to be done manually by a human expert. In the age of machine learning, the researcher has become the decision-maker at the top level, outsourcing analysis tasks and recurrent work to advanced algorithms and automation workflows. Here we propose an algorithm for the automated selection of features driving pharmacophore model quality using SAR information extracted from validated QPhAR models. By integrating the developed method into an end-to-end workflow, we present a fully automated method that is able to derive best-quality pharmacophores from a given input dataset. Finally, we show how the QPhAR-generated models can be used to guide the researcher with insights regarding (un-)favourable interactions for compounds of interest.
Computer Science and Mathematics, Mathematical and Computational Biology
Copyright:
This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
