Quantitative structure activity relationship (QSAR) models can be used to predict the activity of new drug candidates in early stages of drug discovery. In the present study, the information of the ninety two 2,5-diaminobenzophenone-containing farnesyltranaferase inhibitors (FTIs) were taken from the literature. Subsequently, the structures of the molecules were optimized using Hyperchem softwa...

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...

In this study, in silico approaches, including multiple QSAR modeling, structural similarity analysis, and molecular docking, were applied to develop QSAR classification models as a fast screening tool for identifying highly-potent ABCA1 up-regulators targeting LXRβ based on a series of new flavonoids. Initially, four modeling approaches, including linear discriminant analysis, support vector m...

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...

3D-QSAR approach has been widely applied and proven to be useful in the case where no reliable crystal structure of the complex between a biologically active molecule and the receptor is available. At the same time, however, it also has highlighted the sensitivity of this approach. The main requirement of the traditional 3D-QSAR method is that molecules should be correctly overlaid in what is a...

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 ...

Quantitative structure activity relationships (QSARs) are theoretical models that relate a quantitative measure of chemical structure to a physical property or a biological effect. QSAR predictions can be used for chemical risk assessment for protection of human and environmental health, which makes them interesting to regulators, especially in the absence of experimental data. For compatibilit...

A number of binary QSAR models have been developed using methods of artificial neural networks, k-nearest neighbors, linear discriminative analysis, and multiple linear regression and have been compared for their ability to recognize five types of chemical compounds that include conventional drugs, inactive druglikes, antimicrobial substituents, and bacterial and human metabolites. Thus, 20 bin...

BACKGROUND Machine learning has a vast range of applications. In particular, advanced machine learning methods are routinely and increasingly used in quantitative structure activity relationship (QSAR) modeling. QSAR data sets often encompass tens of thousands of compounds and the size of proprietary, as well as public data sets, is rapidly growing. Hence, there is a demand for computationally ...

The Discovery Bus, a multi-agent software system designed for automating aspects of Molecular Design, particularly expert decision making, is described. It extends approaches aimed at automating the processing of drug discovery information but where control remains with the human expert, to automating the " tacit knowledge " of the expert and best practice, which we model as a workflow, and exp...