Design and synthesis of potent and subtype-selective PPARalpha agonists

Exploring fingerprints for antidiabetic therapeutics related to peroxisome proliferator-activated receptor gamma (PPARγ) modulators: A chemometric modeling approach

This study demonstrated the correlation of molecular structures of Peroxisome proliferator-activated receptor gamma (PPARγ) modulators and their biological activities. Bayesian classification, and recursive partitioning (RP) studies have been applied to a dataset of 323 PPARγ modulators with diverse scaffolds. The results provide a deep insight into the important sub-structural features modulating PPARγ. The molecular docking analysis again confirmed the significance of the identified sub-structural features in the modulation of PPARγ activity. Molecular dynamics simulations further underscored the stability of the complexes formed by investigated modulators with PPARγ. Overall, the integration of many computational approaches unveiled key structural motifs essential for PPARγ modulatory activity that will shed light on the development of effective modulators in the future.

Synthesis of 4-trifluoromethyl-2H-chromenes via the reaction of 2-(trifluoroacetyl)phenols with vinyltriphenylphosphonium chloride

Substituted on the benzene ring 4-CF₃-2H-chromenes have been prepared from substituted 2-(trifluoroacetyl)phenols and vinyltriphenylphosphonium chloride according to the Schweizer protocol in moderate to excellent yields. The influence of the type and the position of aromatic ring substituents on yields of 4-CF₃-2H-chromenes have been investigated. It has been shown that 4-CF₃-2H-chromenes are convenient precursors to 4-CF₃-coumarins.

Realizing private and practical pharmacological collaboration

Although combining data from multiple entities could power life-saving breakthroughs, open sharing of pharmacological data is generally not viable because of data privacy and intellectual property concerns. To this end, we leverage modern cryptographic tools to introduce a computational protocol for securely training a predictive model of drug-target interactions (DTIs) on a pooled dataset that overcomes barriers to data sharing by provably ensuring the confidentiality of all underlying drugs, targets, and observed interactions. Our protocol runs within days on a real dataset of more than 1 million interactions and is more accurate than state-of-the-art DTI prediction methods. Using our protocol, we discover previously unidentified DTIs that we experimentally validated via targeted assays. Our work lays a foundation for more effective and cooperative biomedical research.

Copper-catalyzed nucleophilic trifluoromethylation of benzylic chlorides

The reactions of primary and secondary benzylic chlorides with trifluoromethyltrimethylsilane in the presence of a catalytic amount of copper(i) thiophene-2-carboxylate (CuTC) have been found to give the corresponding benzylic trifluoromethylated products in good to high yields.

Pharmacophore modeling and parallel screening for PPAR ligands

We describe the generation and validation of pharmacophore models for PPARs, as well as a large scale validation of the parallel screening approach by screening PPAR ligands against a large database of structure-based models. A large test set of 357 PPAR ligands was screened against 48 PPAR models to determine the best models for agonists of PPAR-alpha, PPAR-delta, and PPAR-gamma. Afterwards, a parallel screen was performed using the 357 PPAR ligands and 47 structure-based models for PPARs, which were integrated into a 1537 models comprising in-house pharmacophore database, to assess the enrichment of PPAR ligands within the PPAR hypotheses. For these purposes, we categorized the 1537 database models into 181 protein targets and developed a score that ranks the retrieved targets for each ligand. Thus, we tried to find out if the concept of parallel screening is able to predict the correct pharmacological target for a set of compounds. The PPAR target was ranked first more often than any other target. This confirms the ability of parallel screening to forecast the pharmacological active target for a set of compounds.