Discovery of diamide compounds as diacylglycerol acyltransferase 1 (DGAT1) inhibitors

Discovery of novel DGAT1 inhibitors by combination of machine learning methods, pharmacophore model and 3D-QSAR model

DGAT1 plays a crucial controlling role in triglyceride biosynthetic pathways, which makes it an attractive therapeutic target for obesity. Thus, development of DGAT1 inhibitors with novel chemical scaffolds is desired and important in the drug discovery. In this investigation, the multistep virtual screening methods, including machine learning methods and common feature pharmacophore model, were developed and used to identify novel DGAT1 inhibitors from BioDiversity database with 30,000 compounds. 531 compounds were predicted as DGAT1 inhibitors by combination of machine learning methods comprising of SVM, NB and RP models. Then, 12 agents were filtered from 531 compounds by using the common feature pharmacophore model. The 3D chemical structures of the 12 hits coordinated with surface charges and isosurface have been carefully analyzed by the established 3D-QSAR model. Finally, 8 compounds with desired properties were retained from the final hits and have been assigned to another research group to complete the follow-up compound synthesis and biologic evaluation.

In silico design of diacylglycerol acyltransferase-1 (DGAT1) inhibitors based on SMILES descriptors using Monte-Carlo method

Diabetes, obesity and other diseases related to metabolism are worldwide health problems. These syndromes can be well treated when a particular enzyme-based therapy is developed. Diacylglycerol acyltransferase (DGAT; EC 2.3.1.20) is a microsomal enzyme which is responsible for the synthesis of triglycerides from 1,2-diacylglycerol by catalyzing the acyl-CoA-dependent acylation. The obesity and type-II diabetes can be checked by the inhibition of DGAT1 enzyme. Quantitative structure-activity relationship (QSAR) modelling is an essential technique in drug design and development. To study the aspect of DGAT1 inhibitors, Monte-Carlo method-based QSAR was developed for 197 DGAT1 inhibitors. QSAR models were derived by using the optimal descriptor based on SMILES notation. Different statistical parameters including the novel index of ideality of correlation were applied to validate the generated QSAR models. Four random splits were prepared from the data set. The statistical criteria r² = 0.8129, CCC = 0.8979 and Q² = 0.7962 of the validation set of split 1 were the best; therefore, the developed QSAR model of split 1 was decided to be the leading model. The molecular fragments, which were promoter of endpoint increase or decrease were also determined. Thirteen new DGAT1 inhibitors were designed from the lead compound DGAT011.

Facile ring opening reaction of oxazolone enables efficient amidation for aminoisobutyric acid

4,4-Dimethyloxazolones derived from N-protected aminoisobutyric acid (AIB) are particularly known as poor electrophiles due to the steric hindrance around the carbonyl and not employed as useful intermediates for amidation whereas numerous examples have been reported to support the utility of other oxazolones in amidation. AIB is an important and strategical synthon in medicinal chemistry but the peptide bond formation of the N-protected urethane derivatives of AIB is known to be often unproductive due to the rapid formation of the stable 4,4-dimethyloxazolone via an intramolecular cyclization. We discovered that the 4,4-dimethyloxazolone of an AIB urethane is in fact an excellent electrophile that enables efficient amidation even with weakly reactive nucleophiles. The 4,4-dimethyloxazolone can be stored in a pure form and used as a reagent offering an efficient and convenient synthetic tool for generating AIB-peptide analogs.

Unusual Amino Acids in Medicinal Chemistry

Unusual amino acids are fundamental building blocks of modern medicinal chemistry. The combination of readily functionalized amine and carboxyl groups attached to a chiral central core along with one or two potentially diverse side chains provides a unique three-dimensional structure with a high degree of functionality. This makes them invaluable as starting materials for syntheses of complex molecules, highly diverse elements for SAR campaigns, integral components of peptidomimetic drugs, and potential drugs on their own. This Perspective highlights the diversity of unnatural amino acid structures found in hit-to-lead and lead optimization campaigns and clinical stage and approved drugs, reflecting their increasingly important role in medicinal chemistry.