In continuing with the research program initiated by Llored and Harré of exploring the part/whole (mereological) discourses of chemistry, we analyse Quantitative Structure-Activity Relationships (QSAR) studies, which are widespread approaches for modeling substances’ properties. The study is carried out by analyzing a particular QSAR model, and it is found that different mereologies are needed:...

Quantitative structure activity relationship (QSAR) studies of twenty four 5-cyano, N1, 6disubstituted, 2-thiouracil derivatives were performed for their central nervous system (CNS) depressant (locomotor) activity using VlifeMDS3.5 software. Partial least square (PLS) linear regression analysis coupled with stepwise variable selection method was applied to derive QSAR models which were further...

In this contribution, the advantages and limitations of two computational techniques that can be used for the investigation of nanoparticles activity and toxicity: classic nano-QSAR (Quantitative Structure-Activity Relationships employed for nanomaterials) and 3D nano-QSAR (three-dimensional Quantitative Structure-Activity Relationships, such us Comparative Molecular Field Analysis, CoMFA/Compa...

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 differen...

A series of new 3-amino-6-chloro-7-(azol-2 or 5-yl)-1,1-dioxo-1,4,2-benzodithiazine derivatives 5a-j have been synthesized and evaluated in vitro for their antiproliferative activity at the U.S. National Cancer Institute. The most active compound 5h showed significant cytotoxic effects against ovarian (OVCAR-3) and breast (MDA-MB-468) cancer (10% and 47% cancer cell death, respectively) as well...

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 Quantitative Structure Activity Relationship (QSAR) study has been an attempted on a series of 88 N-aryl derivatives which display varied inhibitory activity towards both acetylcholinesterase (AChE) and butyrylcholinesterase (BChE), targets in Alzheimer's drug discovery. QSAR models were derived for 53 and 61 compounds for each target, respectively, with the aid of genetic function approximat...

This article provides an overview of methods for reliability assessment of quantitative structure-activity relationship (QSAR) models in the context of regulatory acceptance of human health and environmental QSARs. Useful diagnostic tools and data analytical approaches are highlighted and exemplified. Particular emphasis is given to the question of how to define the applicability borders of a Q...

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...