Navigating the chemical space of dipeptidyl peptidase-4 inhibitors.

This study represents the first large-scale study on the chemical space of inhibitors of dipeptidyl peptidase-4 (DPP4), which is a potential therapeutic protein target for the treatment of diabetes mellitus. Herein, a large set of 2,937 compounds evaluated for their ability to inhibit DPP4 was compiled from the literature. Molecular descriptors were generated from the geometrically optimized low-energy conformers of these compounds at the semiempirical AM1 level.

Probing the origins of 17β-hydroxysteroid dehydrogenase type 1 inhibitory activity via QSAR and molecular docking.

It is generally known that proliferation of human breast cancer cells is stimulated by excess estrogen namely 17β-estradiol. Therefore, reduction of 17β-estradiol production by inhibiting 17β-hydroxysteroid dehydrogenase type 1 (17β-HSD1) is an interesting route for breast cancer treatment particularly during adjuvant therapy. This study investigated the structure-activity relationship of 17β-HSD1 inhibitors as to gain insights and understanding on the origins of 17β-HSD1 inhibitory activities.

Predictive QSAR modeling of aldose reductase inhibitors using Monte Carlo feature selection.

This study explores the chemical space and quantitative structure-activity relationship (QSAR) of a set of 60 sulfonylpyridazinones with aldose reductase inhibitory activity. The physicochemical properties of the investigated compounds were described by a total of 3230 descriptors comprising of 6 quantum chemical descriptors and 3224 molecular descriptors. A subset of 5 descriptors was selected from the aforementioned pool by means of Monte Carlo (MC) feature selection coupled to multiple linear regression (MLR).

Insights into antioxidant activity of 1-adamantylthiopyridine analogs using multiple linear regression.

A data set of 1-adamantylthiopyridine analogs (1-19) with antioxidant activity, comprising of 2,2-diphenyl-1-picrylhydrazyl (DPPH) and superoxide dismutase (SOD) activities, was used for constructing quantitative structure-activity relationship (QSAR) models. Molecular structures were geometrically optimized at B3LYP/6-31g(d) level and subjected for further molecular descriptor calculation using Dragon software. Multiple linear regression (MLR) was employed for the development of QSAR models using 3 significant descriptors (i.

Exploring the chemical space of aromatase inhibitors.

Aromatase, a rate-limiting enzyme catalyzing the conversion of androgen to estrogen, is overexpressed in human breast cancer tissue. Aromatase inhibitors (AIs) have been used for the treatment of estrogen-dependent breast cancer in post-menopausal women by blocking the biosynthesis of estrogen. The undesirable side effects in current AIs have called for continued pursuit for novel candidates with aromatase inhibitory properties.

Machine learning approaches for discerning intercorrelation of hematological parameters and glucose level for identification of diabetes mellitus.

Background: The aim of this study is to explore the relationship between hematological parameters and glycemic status in the establishment of quantitative population-health relationship (QPHR) model for identifying individuals with or without diabetes mellitus (DM).

Methods: A cross-sectional investigation of 190 participants residing in Nakhon Pathom, Thailand in January-March, 2013 was used in this study. Individuals were classified into 3 groups based on their blood glucose levels (normal, Pre-DM and DM).