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...

This chapter critically reviews some of the important methods being used for building quantitative structure-activity relationship (QSAR) models using the artificial neural networks (ANNs). It attends predominantly to the use of multilayer ANNs in the regression analysis of structure-activity data. The highlighted topics cover the approximating ability of ANNs, the interpretability of the resul...

Quantitative Structure-Activity Relationships (QSAR) is a method to create models that can predict certain properties of compounds. Because of the importance of QSAR in designing new drugs, ability to accelerate this process becomes crucial. One way to achieve that is to be able to quickly explore the QSAR model space in the search for the best models. The cloud computing paradigm very well fit...

Q uantitative structure-activity relationships (QSAR) correlate, within congeneric series of compounds, affinities of ligands to their binding sites, inhibition constants, rate constants, and other biological activities, either with certain structural features (Free Wilson analysis) or with atomic, group or molecular properties, such as lipophilicity, polarizability, electronic and steric prope...