Kernel learning for ligand-based virtual screening: discovery of a new PPARgamma agonist

Poster presentation at 5th German Conference on Cheminformatics: 23. CIC-Workshop Goslar, Germany. 8-10 November 2009 We demonstrate the theoretical and practical application of modern kernel-based machine learning methods to ligand-based virtual screening by successful prospective screening for novel agonists of the peroxisome proliferator-activated receptor gamma (PPARgamma) [1]. PPARgamma is a nuclear receptor involved in lipid and glucose metabolism, and related to type-2 diabetes and dyslipidemia. Applied methods included a graph kernel designed for molecular similarity analysis [2], kernel principle component analysis [3], multiple kernel learning [4], and, Gaussian process regression [5]. In the machine learning approach to ligand-based virtual screening, one uses the similarity principle [6] to identify potentially active compounds based on their similarity to known reference ligands. Kernel-based machine learning [7] uses the "kernel trick", a systematic approach to the derivation of non-linear versions of linear algorithms like separating hyperplanes and regression. Prerequisites for kernel learning are similarity measures with the mathematical property of positive semidefiniteness (kernels). The iterative similarity optimal assignment graph kernel (ISOAK) [2] is defined directly on the annotated structure graph, and was designed specifically for the comparison of small molecules. In our virtual screening study, its use improved results, e.g., in principle component analysis-based visualization and Gaussian process regression. Following a thorough retrospective validation using a data set of 176 published PPARgamma agonists [8], we screened a vendor library for novel agonists. Subsequent testing of 15 compounds in a cell-based transactivation assay [9] yielded four active compounds. The most interesting hit, a natural product derivative with cyclobutane scaffold, is a full selective PPARgamma agonist (EC50 = 10 ± 0.2 microM, inactive on PPARalpha and PPARbeta/delta at 10 microM). We demonstrate how the interplay of several modern kernel-based machine learning approaches can successfully improve ligand-based virtual screening results

Molecular determinants for improved activity at PPARα: structure-activity relationship of pirinixic acid derivatives, docking study and site-directed mutagenesis of PPARα

Peroxisome proliferator-activated receptors (PPARs) are attractive targets for the treatment of the metabolic syndrome. Especially a combination of PPARα and PPARγ agonistic activity seems worthwhile to be pursued. Herein we present the design and synthesis of a series of pirinixic acid derivatives as potent PPARα particularly dual PPARα/γ agonists with 2-((4-chloro-6-((4-(phenylamino)phenyl)amino)pyrimidin-2-yl)thio)octanoicacid having the highest potential. Our investigations based on molecular docking and structure-activity relationship (SAR) studies elucidated structural determinants affecting the potency at PPARα. A diphenylamine-scaffold seems to play a key role. Careful in silico analysis revealed an essential role for a hydrogen bond between the diphenylamine and a water cluster. We confirmed this hypothesis using a mutated PPARα LBD in our transactivation assay to disrupt the water cluster and to validate the proposed interaction

SAR-studies of γ-secretase modulators with PPARγ-agonistic and 5-lipoxygenase-inhibitory activity for Alzheimer's disease

We present the design, synthesis and biological evaluation of compounds containing a 2-(benzylidene)hexanoic acid scaffold as multi-target directed γ-secretase-modulators. Broad structural variations were undertaken to elucidate the structure-activity-relationships at the 5-position of the aromatic core. Compound 13 showed the most potent activity profile with IC50 values of 0.79μM (Aβ42), 0.3μM (5-lipoxygenase) and an EC50 value of 4.64μM for PPARγ-activation. This derivative is the first compound exhibiting low micromolar to nanomolar activities for these three targets. Combining γ-secretase-modulation, PPARγ-agonism and inhibition of 5-lipoxygenase in one compound could be a novel disease-modifying multi-target-strategy for Alzheimer's disease to concurrently address the causative amyloid pathology and secondary pathologies like chronic brain inflammation