0000000000172646
AUTHOR
Arjumand Ather
TOMOCOMD-CARDD descriptors-based virtual screening of tyrosinase inhibitors: evaluation of different classification model combinations using bond-based linear indices.
Abstract A new set of bond-level molecular descriptors (bond-based linear indices) are used here in QSAR (quantitative structure–activity relationship) studies of tyrosinase inhibitors, for finding functions that discriminate between the tyrosinase inhibitor compounds and inactive ones. A database of 246 compounds was collected for this study; all organic chemicals were reported as tyrosinase inhibitors; they had great structural diversity. This dataset can be considered as a helpful tool, not only for theoretical chemists but also for other researchers in this area. The set used as inactive has 412 drugs with other clinical uses. Twelve LDA-based QSAR models were obtained, the first six us…
Prediction of tyrosinase inhibition activity using atom-based bilinear indices.
A set of novel atom-based molecular fingerprints is proposed based on a bilinear map similar to that defined in linear algebra. These molecular descriptors (MDs) are proposed as a new means of molecular parametrization easily calculated from 2D molecular information. The nonstochastic and stochastic molecular indices match molecular structure provided by molecular topology by using the kth nonstochastic and stochastic graph-theoretical electronic-density matrices, M(k) and S(k), respectively. Thus, the kth nonstochastic and stochastic bilinear indices are calculated using M(k) and S(k) as matrix operators of bilinear transformations. Chemical information is coded by using different pair com…
New tyrosinase inhibitors selected by atomic linear indices-based classification models.
In the present report, the use of the atom-based linear indices for finding functions that discriminate between the tyrosinase inhibitor compounds and inactive ones is presented. In this sense, discriminant models were applied and globally good classifications of 93.51% and 92.46% were observed for non-stochastic and stochastic linear indices best models, respectively, in the training set. The external prediction sets had accuracies of 91.67% and 89.44%. In addition, these fitted models were used in the screening of new cycloartane compounds isolated from herbal plants. A good behavior is shown between the theoretical and experimental results. These results provide a tool that can be used i…
Atom-Based 2D Quadratic Indices in Drug Discovery of Novel Tyrosinase Inhibitors: Results ofIn Silico Studies Supported by Experimental Results
Herein we present results of QSAR studies of tyrosinase inhibitors employing one of the atom-based TOMOCOMD-CARDD (acronym of TOpological MOlecular COMputer Design-Computer Aided “Rational” Drug Design) descriptors, molecular quadratic indices, and Linear Discriminant Analysis (LDA) as pattern recognition method. In this way, a database of 246 organic chemicals, reported as tyrosinase inhibitors having great structural variability, was analyzed and presented as a helpful tool, not only for theoretical chemists but also for other researchers in this area. In total, 12 LDA-based QSAR models were obtained, the first six with the non-stochastic total and local quadratic indices and the six rema…
Dragon method for finding novel tyrosinase inhibitors: Biosilico identification and experimental in vitro assays
QSAR (quantitative structure-activity relationship) studies of tyrosinase inhibitors employing Dragon descriptors and linear discriminant analysis (LDA) are presented here. A data set of 653 compounds, 245 with tyrosinase inhibitory activity and 408 having other clinical uses were used. The active data set was processed by k-means cluster analysis in order to design training and prediction series. Seven LDA-based QSAR models were obtained. The discriminant functions applied showed a globally good classification of 99.79% for the best model Class=-96.067+1.988 x 10(2)X0Av +9 1.907 BIC3 + 6.853 CIC1 in the training set. External validation processes to assess the robustness and predictive pow…