Cinnamaldehyde, of the genius Cinnamomum, is a major constituent of the bark of the cinnamon tree and possesses broad-spectrum antimicrobial activity. In this study, we used best multiple linear regression (BMLR) to develop quantitative structure activity relationship (QSAR) models for cinnamaldehyde derivatives against wood-decaying fungi Trametes versicolor and Gloeophyllun trabeum. Based on ...

Quantitative structure activity relationship (QSAR) models are are a statisti-cal solution to the problem of directly calculating physical and biological properties ofmolecules from their physical structure. The direct prediction of properties is in generalnot feasible either owing to lack of computing resources or lack of knowledge about therelationship between structure and pr...

In this paper the Multi-linear regression analysis has been applied for QSAR study. The relationship has been worked out between the Log 1/C values of a series of compounds and certain quantum chemical and energy descriptors. The QSAR studies of Triazines inhibiting dihydrofolate reductase based on quantum chemical and energy descriptors shows that among all the 28 QSAR models PA51 to PA 78, th...

One of the physiological characteristics of carotenoids is their radical scavenging activity. In this study, the relationship between radical scavenging activities and quantum chemical descriptors of carotenoids was determined. Adaptive neuro-fuzzy inference system (ANFIS) applied quantitative structure-activity relationship models (QSAR) were also developed for predicting and comparing radical...

Objective: Thiazolidine-2,4-dione (TZD) are the well known anti-diabetic scaffold. Very recently, several TZD based anti-cancer agents have came into limelight for treating mutant cancer forms. In order to establish and understand the relationship of biological activity with that of physiochemical parameters associated with the structure, twodimensional (2D-QSAR), group-based (G-QSAR), and thre...

In this study, we compare the performance of semi-supervised and supervised machine learning methods applied to various problems of modeling Quantitative Structure Activity Relationship (QSAR) in sets of chemical compounds. Semi-supervised learning utilizes unlabeled data in addition to labeled data with the goal of building better predictive models than can be learned by using labeled data alo...

A three-dimensional quantitative structure activity relationship (3D-QSAR) study using comparative molecular field analysis (CoMFA) method was performed on 2,5-disubstituted1,3,4-thiadiazole derivatives as diuretic agents. This study was performed using 40 compounds, in which the CoMFA model was developed using a training set of 30 compounds. Ten compounds (selected at randomly served as a test...

1-[4-(2-alkylaminoethoxy)phenylcarbonyl]-3,5-bis(arylidene)-4-piperidones are a novel class of potent cytotoxic agents. these compounds demonstrate low micromolar to submicromolar ic50 values against human molt 4/c8 and cem t-lymphocytes and murine leukemia l1210 cells. in this study, a comparative qsar investigation was performed on a series of 3,5-bis(arylidene)-4-piperidones using different ...

Abstract Quantitative structure activity relationships (QSAR) modelling is a well-known computational tool, often used in wide variety of applications. Yet one the major drawbacks conventional QSAR that models are set up based on limited number experimental and/or theoretical conditions. To overcome this, so-called multitasking or multitarget (mt-QSAR) approaches have emerged as new tools able ...

BACKGROUND QSAR is an established and powerful method for cheap in silico assessment of physicochemical properties and biological activities of chemical compounds. However, QSAR models are rather complex mathematical constructs that cannot easily be interpreted. Medicinal chemists would benefit from practical guidance regarding which molecules to synthesize. Another possible approach is analysi...