Improved QSAR modeling of anti-HIV-1 acivities by means of the optimized correlation weights of local graph invariants

We report the results derived from the use of molecular descriptors calculated with the correlation weights (CWs) of local graph invariants for modeling of anti-HIV-1 potencies of two groups of reverse transcriptase inhibitors. The presence of different chemical elements in the molecular structure of the inhibitors and the Morgan extended connectivity values of zeroth-, first-, and second order have been examined as local graph invariants in the labeled hydrogen-filled graphs. We have computed via Monte Carlo optimization procedure the values of CWs which produce the largest possible correlation coefficient between the numerical data on the anti-HIV-1 potencies and those values of the descriptors on the training set. The model of the anti-HIV-1 activity obtained with compounds of training set by means of optimization of correlation weights of chemical elements present together with Morgan extended connectivity of first order makes up a sensible model for a satisfactory prediction of the endpoints of the compounds belonging to the test set.Facultad de Ciencias ExactasInstituto de Investigaciones Fisicoquímicas Teóricas y AplicadasCentro de Investigaciones del Medio Ambient

Improved QSAR modeling of anti-HIV-1 acivities by means of the optimized correlation weights of local graph invariants

We report the results derived from the use of molecular descriptors calculated with the correlation weights (CWs) of local graph invariants for modeling of anti-HIV-1 potencies of two groups of reverse transcriptase inhibitors. The presence of different chemical elements in the molecular structure of the inhibitors and the Morgan extended connectivity values of zeroth-, first-, and second order have been examined as local graph invariants in the labeled hydrogen-filled graphs. We have computed via Monte Carlo optimization procedure the values of CWs which produce the largest possible correlation coefficient between the numerical data on the anti-HIV-1 potencies and those values of the descriptors on the training set. The model of the anti-HIV-1 activity obtained with compounds of training set by means of optimization of correlation weights of chemical elements present together with Morgan extended connectivity of first order makes up a sensible model for a satisfactory prediction of the endpoints of the compounds belonging to the test set.Facultad de Ciencias ExactasInstituto de Investigaciones Fisicoquímicas Teóricas y AplicadasCentro de Investigaciones del Medio Ambient

Improved QSAR modeling of anti-HIV-1 acivities by means of the optimized correlation weights of local graph invariants

We report the results derived from the use of molecular descriptors calculated with the correlation weights (CWs) of local graph invariants for modeling of anti-HIV-1 potencies of two groups of reverse transcriptase inhibitors. The presence of different chemical elements in the molecular structure of the inhibitors and the Morgan extended connectivity values of zeroth-, first-, and second order have been examined as local graph invariants in the labeled hydrogen-filled graphs. We have computed via Monte Carlo optimization procedure the values of CWs which produce the largest possible correlation coefficient between the numerical data on the anti-HIV-1 potencies and those values of the descriptors on the training set. The model of the anti-HIV-1 activity obtained with compounds of training set by means of optimization of correlation weights of chemical elements present together with Morgan extended connectivity of first order makes up a sensible model for a satisfactory prediction of the endpoints of the compounds belonging to the test set.Facultad de Ciencias ExactasInstituto de Investigaciones Fisicoquímicas Teóricas y AplicadasCentro de Investigaciones del Medio Ambient

Pattern recognition system based on support vector machines: HIV-1 integrase inhibitors application

Support Vector Machines (SVM) represent one of the most promising Machine Learning (ML) tools that can be applied to develop a predictive Quantitative Structure-Activity Relationship (QSAR) models using molecular descriptors. The performance and predictive power of support vector machines (SVM) for regression problems in quantitative structure-activity relationship were investigated. The SVM results are superior to those obtained by artificial neural network and multiple linear regression. These results indicate that the SVM model with the kernel radial basis function can be used as an alternative tool for regression problems in quantitative structure-activity relationship. Keywords: Support Vector Machines; Artificial Neural Network; Quantitative Structure-Activity Relationship

3D-QSAR Studies on Thiazolidin-4-one S1P1 Receptor Agonists by CoMFA and CoMSIA

Selective S1P1 receptor agonists have therapeutic potential to treat a variety of immune-mediated diseases. A series of 2-imino-thiazolidin-4-one derivatives displaying potent S1P1 receptor agonistic activity were selected to establish 3D-QSAR models using CoMFA and CoMSIA methods. Internal and external cross-validation techniques were investigated as well as some measures including region focusing, progressive scrambling, bootstraping and leave-group-out. The satisfactory CoMFA model predicted a q2 value of 0.751 and an r2 value of 0.973, indicating that electrostatic and steric properties play a significant role in potency. The best CoMSIA model, based on a combination of steric, electrostatic, hydrophobic and H-bond donor descriptors, predicted a q2 value of 0.739 and an r2 value of 0.923. The models were graphically interpreted using contour plots which gave more insight into the structural requirements for increasing the activity of a compound, providing a solid basis for future rational design of more active S1P1 receptor agonists

Evolutionary Computation and QSAR Research

[Abstract] The successful high throughput screening of molecule libraries for a specific biological property is one of the main improvements in drug discovery. The virtual molecular filtering and screening relies greatly on quantitative structure-activity relationship (QSAR) analysis, a mathematical model that correlates the activity of a molecule with molecular descriptors. QSAR models have the potential to reduce the costly failure of drug candidates in advanced (clinical) stages by filtering combinatorial libraries, eliminating candidates with a predicted toxic effect and poor pharmacokinetic profiles, and reducing the number of experiments. To obtain a predictive and reliable QSAR model, scientists use methods from various fields such as molecular modeling, pattern recognition, machine learning or artificial intelligence. QSAR modeling relies on three main steps: molecular structure codification into molecular descriptors, selection of relevant variables in the context of the analyzed activity, and search of the optimal mathematical model that correlates the molecular descriptors with a specific activity. Since a variety of techniques from statistics and artificial intelligence can aid variable selection and model building steps, this review focuses on the evolutionary computation methods supporting these tasks. Thus, this review explains the basic of the genetic algorithms and genetic programming as evolutionary computation approaches, the selection methods for high-dimensional data in QSAR, the methods to build QSAR models, the current evolutionary feature selection methods and applications in QSAR and the future trend on the joint or multi-task feature selection methods.Instituto de Salud Carlos III, PIO52048Instituto de Salud Carlos III, RD07/0067/0005Ministerio de Industria, Comercio y Turismo; TSI-020110-2009-53)Galicia. Consellería de Economía e Industria; 10SIN105004P

Sektorowy formalizm porównawczej analizy powierzchni cząsteczkowej (s-CoMSA) - zastosowanie do modelowania zależności struktura-aktywność

Celem pracy jest: Opracowanie nowej metody obliczania deskryptorów cząsteczkowych s-CoMSA (ang. sector-comparative molecular surface analysis); w metodzie tej przestrzeń cząsteczkowa jest dzielona za zbiór sześciennych sektorów, ● Szeroko rozumiana optymalizacja metody s-CoMSA, ● Analiza QSAR oraz SAR wybranych szeregów związków aktywnych biologicznie z wykorzystaniem metody s-CoMSA oraz innych metod 3D-QSAR. W zakres pracy wchodzi: Opracowanie formalizmu metody s-CoMSA, ● Zaprogramowanie procedur analizy s-CoMSA, ● Badanie modeli s-CoMSA aktywności biologicznej wybranych szeregów związków organicznych, w tym: • szeregu steroidów o powinowactwie do globuliny wiążącej kortykosteroidy (ang. corticosteroid-binding globulin – CBG), • inhibitorów wirusa HIV, • inhibitorów reduktazy kwasu dihydrofoliowego

(Q)SAR Modelling of Nanomaterial Toxicity - A Critical Review

There is an increasing recognition that nanomaterials pose a risk to human health, and that the novel engineered nanomaterials (ENMs) in the nanotechnology industry and their increasing industrial usage poses the most immediate problem for hazard assessment, as many of them remain untested. The large number of materials and their variants (different sizes and coatings for instance) that require testing and ethical pressure towards non-animal testing means that expensive animal bioassay is precluded, and the use of (quantitative) structure activity relationships ((Q)SAR) models as an alternative source of hazard information should be explored. (Q)SAR modelling can be applied to fill the critical knowledge gaps by making the best use of existing data, prioritize physicochemical parameters driving toxicity, and provide practical solutions to the risk assessment problems caused by the diversity of ENMs. This paper covers the core components required for successful application of (Q)SAR technologies to ENMs toxicity prediction, and summarizes the published nano-(Q)SAR studies and outlines the challenges ahead for nano-(Q)SAR modelling. It provides a critical review of (1) the present status of the availability of ENMs characterization/toxicity data, (2) the characterization of nanostructures that meets the need of (Q)SAR analysis, (3) the summary of published nano-(Q)SAR studies and their limitations, (4) the in silico tools for (Q)SAR screening of nanotoxicity and (5) the prospective directions for the development of nano-(Q)SAR models

Machine learning for the prediction of phenols cytotoxicity

Quantitative structure-activity relationships (QSAR) are relevant techniques that assist biologists and chemists in accelerating the drug design process and help understanding many biological and chemical mechanisms. Using classical statistical methods may affect the accuracy and the reliability of the developed QSAR models. This work aims to use a machine learning approach to establish a QSAR model for phenols cytotoxicity prediction. This issue concern many chemists and biologists. In this investigation, the dataset is diverse, and the cytotoxicity data are sparse. Multi-component description of the compounds has then been considered. A set of molecular descriptors fed the deep neural network (DNN) and served to train the DNN. The established DNN model was able to predict the cytotoxicity of the phenols at high precision. The correlation coefficient at the fitting stage was higher than other statistical methods reported in the literature or developed in the present work, specifically multiple linear regression (MLR) and shallow artificial neural networks (ANN), and was equal to 0.943. The predictive capability of the model, as estimated by the coefficient of determination on an external predictive dataset, was significantly high and was about 0.739. This finding could help implement many molecular descriptors relevant to describing the compounds, representing the effects governing the phenols' cytotoxicity toward Tetrahymena pyriformis, avoiding overfitting and outlier exclusion