Tyrosinase Enzyme: 1. An Overview on a Pharmacological Target
The tyrosinase enzyme (EC 1.14.18.1) is an oxidoreductase inside the general enzyme classification and is involved in the oxidation and reduction process in the epidermis. These chemical reactions that the enzyme catalyzes are of principal importance in the melanogenesis process. This process of melanogenesis is related to the melanin formation, a heteropolymer of indolic nature that provides the different tonalities in the skin and helps to the protection from the ultraviolet radiation. However, a pigment overproduction, come up by the action of the tyrosinase, can cause different disorders in the skin related to the hyperpigmentation. Several studies mainly focused on the characteristics …
Comparative study to predict toxic modes of action of phenols from molecular structures.
Quantitative structure-activity relationship models for the prediction of mode of toxic action (MOA) of 221 phenols to the ciliated protozoan Tetrahymena pyriformis using atom-based quadratic indices are reported. The phenols represent a variety of MOAs including polar narcotics, weak acid respiratory uncouplers, pro-electrophiles and soft electrophiles. Linear discriminant analysis (LDA), and four machine learning techniques (ML), namely k-nearest neighbours (k-NN), support vector machine (SVM), classification trees (CTs) and artificial neural networks (ANNs), have been used to develop several models with higher accuracies and predictive capabilities for distinguishing between four MOAs. M…
Retrained Classification of Tyrosinase Inhibitors and “In Silico” Potency Estimation by Using Atom-Type Linear Indices
In this paper, the authors present an effort to increase the applicability domain (AD) by means of retraining models using a database of 701 great dissimilar molecules presenting anti-tyrosinase activity and 728 drugs with other uses. Atom-based linear indices and best subset linear discriminant analysis (LDA) were used to develop individual classification models. Eighteen individual classification-based QSAR models for the tyrosinase inhibitory activity were obtained with global accuracy varying from 88.15-91.60% in the training set and values of Matthews correlation coefficients (C) varying from 0.76-0.82. The external validation set shows globally classifications above 85.99% and 0.72 fo…
QSAR models for tyrosinase inhibitory activity description applying modern statistical classification techniques: A comparative study
Abstract Cluster analysis (CA), Linear and Quadratic Discriminant Analysis (L(Q)DA), Binary Logistic Regression (BLR) and Classification Tree (CT) are applied on two datasets for description of tyrosinase inhibitory activity from molecular structures. The first set included 701 tyrosinase inhibitors (TI) that are used for performance of inhibitory and non-inhibitory activity and the second one is for potency estimation of active compounds. 2D TOMOCOMD-CARDD atom-based quadratic indices are computed as molecular descriptors. CA is used to “rational” design of training (TS) and prediction set (PS) but it shows of not being adequate as classification technique. On the first data, the overall a…
Multi-output Model with Box-Jenkins Operators of Quadratic Indices for Prediction of Malaria and Cancer Inhibitors Targeting Ubiquitin- Proteasome Pathway (UPP) Proteins.
The ubiquitin-proteasome pathway (UPP) is the primary degradation system of short-lived regulatory proteins. Cellular processes such as the cell cycle, signal transduction, gene expression, DNA repair and apoptosis are regulated by this UPP and dysfunctions in this system have important implications in the development of cancer, neurodegenerative, cardiac and other human pathologies. UPP seems also to be very important in the function of eukaryote cells of the human parasites like Plasmodium falciparum, the causal agent of the neglected disease Malaria. Hence, the UPP could be considered as an attractive target for the development of compounds with Anti-Malarial or Anti-cancer properties. R…
The Use of Rule-Based and QSPR Approaches in ADME Profiling: A Case Study on Caco-2 Permeability.
During the early ADME profiling the development of simple, interpretable and reliable in silico tools is very important. In this study, rule-based and QSPR approaches were investigated using a large Caco-2 permeability database. Three permeability classes were determined: high (H), moderate (M) and low (L). The main physicochemical properties related with permeability were ranked as follows: Polar Surface Area (PSA)>Lipophilicity (logP/logD)>Molecular Weight (MW)>number of Hydrogen Bond donors and acceptors>Ionization State>number of Rotatable Bonds>number of Rings. The best rule, based on the combination of PSA-MW-logD (3PRule), was able to identify the H, M and L classes with accuracy of …
Dry selection and wet evaluation for the rational discovery of new anthelmintics
Helminths infections remain a major problem in medical and public health. In this report, atom-based 2D bilinear indices, a TOMOCOMD-CARDD (QuBiLs-MAS module) molecular descriptor family and linear discriminant analysis (LDA) were used to find models that differentiate among anthelmintic and non-anthelmintic compounds. Two classification models obtained by using non-stochastic and stochastic 2D bilinear indices, classified correctly 86.64% and 84.66%, respectively, in the training set. Equation 1(2) correctly classified 141(135) out of 165 [85.45%(81.82%)] compounds in external validation set. Another LDA models were performed in order to get the most likely mechanism of action of anthelmin…
<strong>Machine Learning and Atom-Based Quadratic Indices for Proteasome Inhibition Prediction </strong>
The atom-based quadratic indices are used in this work together with some machine learning techniques that includes: support vector machine, artificial neural network, random forest and k-nearest neighbor. This methodology is used for the development of two quantitative structure-activity relationship (QSAR) studies for the prediction of proteasome inhibition. A first set consisting of active and non-active classes was predicted with model performances above 85% and 80% in training and validation series, respectively. These results provided new approaches on proteasome inhibitor identification encouraged by virtual screenings procedures. .
Generalized Molecular Descriptors Derived From Event-Based Discrete Derivative.
In the present study, a generalized approach for molecular structure characterization is introduced, based on the relation frequency matrix (F) representation of the molecular graph and the subsequent calculation of the corresponding discrete derivative (finite difference) over a pair of elements (atoms). In earlier publications (22- 24), an unique event, named connected subgraphs, (based on the Kier-Hall's subgraphs) was systematically employed for the computation of the matrix F. The present report is a generalization of this notion, in which eleven additional events are introduced, classified in three categories, namely, topological (terminal paths, vertex path incidence, quantum subgrap…
A Simple Method to Predict Blood-Brain Barrier Permeability of Drug- Like Compounds Using Classification Trees
Background: To know the ability of a compound to penetrate the blood-brain barrier (BBB) is a challenging task; despite the numerous efforts realized to predict/measure BBB passage, they still have several drawbacks. Methods: The prediction of the permeability through the BBB is carried out using classification trees. A large data set of 497 compounds (recently published) is selected to develop the tree model. Results: The best model shows an accuracy higher than 87.6% for training set; the model was also validated using 10-fold cross-validation procedure and through a test set achieving accuracy values of 86.1% and 87.9%, correspondingly. We give a brief explanation, in structural terms, o…
A Comparative Study of Nonlinear Machine Learning for the "In Silico" Depiction of Tyrosinase Inhibitory Activity from Molecular Structure.
In the preset report, for the first time, support vector machine (SVM), artificial neural network (ANN), Baye- sian networks (BNs), k-nearest neighbor (k-NN) are applied and compared on two "in-house" datasets to describe the tyrosinase inhibitory activity from the molecular structure. The data set Data I is used for the identification of tyrosi- nase inhibitors (TIs) including 701 active and 728 inactive compounds. Data II consists of active chemicals for potency estimation of TIs. The 2D TOMOCOMD-CARDD atom-based quadratic indices are used as molecular descriptors. The de- rived models show rather encouraging results with the areas under the Receiver Operating Characteristic (AURC) curve …
Prediction of acute toxicity of phenol derivatives using multiple linear regression approach for Tetrahymena pyriformis contaminant identification in a median-size database
In this article, the modeling of inhibitory grown activity against Tetrahymena pyriformis is described. The 0-2D Dragon descriptors based on structural aspects to gain some knowledge of factors influencing aquatic toxicity are mainly used. Besides, it is done by some enlarged data of phenol derivatives described for the first time and composed of 358 chemicals. It overcomes the previous datasets with about one hundred compounds. Moreover, the results of the model evaluation by the parameters in the training, prediction and validation give adequate results comparable with those of the previous works. The more influential descriptors included in the model are: X3A, MWC02, MWC10 and piPC03 wit…