Lately, Quantitative Structure-Activity Relationship (QSAR) studies have been afar used to predict anticancer activity taking into account different molecular descriptors, statistical techniques, cell lines and data set of congeneric and non-congeneric compounds. Herein we report a QSAR study based on a TOPological Sub-structural Molecular Design (TOPS-MODE) approach, aiming at predicting the anticancer leukemia activity of a diverse data set of indolocarbazoles derivatives. Finally, several aspects of the structural activity relationships are discussed in terms of the contribution of different bonds to the anticancer activity, thereby making the relationship between structure and biological activity more transparent.
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http://dx.doi.org/10.1016/j.bmc.2008.11.084 | DOI Listing |
Sci Rep
July 2023
Zachry Department of Civil Engineering, Texas A&M University, College Station, TX, USA.
In studying resilience in temporal human networks, relying solely on global network measures would be inadequate; latent sub-structural network mechanisms need to be examined to determine the extent of impact and recovery of these networks during perturbations, such as urban flooding. In this study, we utilize high-resolution aggregated location-based data to construct temporal human mobility networks in Houston in the context of the 2017 Hurricane Harvey. We examine motif distribution, motif persistence, temporal stability, and motif attributes to reveal latent sub-structural mechanisms related to the resilience of human mobility networks during disaster-induced perturbations.
View Article and Find Full Text PDFBrief Bioinform
May 2023
Computer, Electrical and Mathematical Sciences & Engineering Division, King Abdullah University of Science and Technology, 4700 KAUST, Thuwal 23955, Saudi Arabia.
Identification of active candidate compounds for target proteins, also called drug-protein interaction (DPI) prediction, is an essential but time-consuming and expensive step, which leads to fostering the development of drug discovery. In recent years, deep network-based learning methods were frequently proposed in DPIs due to their powerful capability of feature representation. However, the performance of existing DPI methods is still limited by insufficiently labeled pharmacological data and neglected intermolecular information.
View Article and Find Full Text PDFSAR QSAR Environ Res
July 2020
Department of Occupational and Environmental Health, College of Public Health, Zhengzhou University, Zhengzhou, China.
Quantitative structure-property relationship (QSPR) models were developed for predicting the pungency of a set of capsaicinoids. Multiple linear regression (MLR) coupled with topological substructural molecular descriptor (TOPS-MODE) approach was used. The best MLR model based on only five orthogonalized TOPS-MODE variables allowed us to obtain a coefficient of determination of 0.
View Article and Find Full Text PDFFoods
November 2019
Facultad de Farmacia, Campus vida, Universidad de Santiago de Compostela, 15782 Santiago de Compostela, Spain.
Increasing interest in constituents and dietary supplements has created the need for more efficient use of this information in nutrition-related fields. The present work aims to obtain optimal models to predict the total antioxidant properties of food matrices, using available information on the amount and class of flavonoids present in vegetables. A new dataset using databases that collect the flavonoid content of selected foods has been created.
View Article and Find Full Text PDFMol Divers
August 2018
Department of Chemistry, School of Advanced Sciences, VIT University, Vellore, Tamil Nadu, 632014, India.
A quantitative structure-activity (QSAR) model has been developed for enriched tubulin inhibitors, which were retrieved from sequence similarity searches and applicability domain analysis. Using partial least square (PLS) method and leave-one-out (LOO) validation approach, the model was generated with the correlation statistics of [Formula: see text] and [Formula: see text] of 0.68 and 0.
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