Optimization Algorithms for Chemoinformatics and Material-informatics

Modeling complex phenomena in chemoinformatics and material-informatics can often be formulated as single-objective or multi-objective optimization problems (SOOPs or MOOPs). For example, the design of new drugs or new materials is inherently a MOOP since drugs/materials require the simultaneous optimization of multiple parameters

3D-QSPR Method of Computational Technique Applied on Red Reactive Dyes by Using CoMFA Strategy

Cellulose fiber is a tremendous natural resource that has broad application in various productions including the textile industry. The dyes, which are commonly used for cellulose printing, are “reactive dyes” because of their high wet fastness and brilliant colors. The interaction of various dyes with the cellulose fiber depends upon the physiochemical properties that are governed by specific features of the dye molecule. The binding pattern of the reactive dye with cellulose fiber is called the ligand-receptor concept. In the current study, the three dimensional quantitative structure property relationship (3D-QSPR) technique was applied to understand the red reactive dyes interactions with the cellulose by the Comparative Molecular Field Analysis (CoMFA) method. This method was successfully utilized to predict a reliable model. The predicted model gives satisfactory statistical results and in the light of these, it was further analyzed. Additionally, the graphical outcomes (contour maps) help us to understand the modification pattern and to correlate the structural changes with respect to the absorptivity. Furthermore, the final selected model has potential to assist in understanding the charachteristics of the external test set. The study could be helpful to design new reactive dyes with better affinity and selectivity for the cellulose fiber

2D Quantitative Structure-Property Relationship Study of Mycotoxins by Multiple Linear Regression and Support Vector Machine

In the present work, support vector machines (SVMs) and multiple linear regression (MLR) techniques were used for quantitative structure–property relationship (QSPR) studies of retention time (tR) in standardized liquid chromatography–UV–mass spectrometry of 67 mycotoxins (aflatoxins, trichothecenes, roquefortines and ochratoxins) based on molecular descriptors calculated from the optimized 3D structures. By applying missing value, zero and multicollinearity tests with a cutoff value of 0.95, and genetic algorithm method of variable selection, the most relevant descriptors were selected to build QSPR models. MLR and SVMs methods were employed to build QSPR models. The robustness of the QSPR models was characterized by the statistical validation and applicability domain (AD). The prediction results from the MLR and SVM models are in good agreement with the experimental values. The correlation and predictability measure by r2 and q2 are 0.931 and 0.932, repectively, for SVM and 0.923 and 0.915, respectively, for MLR. The applicability domain of the model was investigated using William’s plot. The effects of different descriptors on the retention times are described

Integration of Data Quality, Kinetics and Mechanistic Modelling into Toxicological Assessment of Cosmetic Ingredients

In our modern society we are exposed to many natural and synthetic chemicals. The assessment of chemicals with regard to human safety is difficult but nevertheless of high importance. Beside clinical studies, which are restricted to potential pharmaceuticals only, most toxicity data relevant for regulatory decision-making are based on in vivo data. Due to the ban on animal testing of cosmetic ingredients in the European Union, alternative approaches, such as in vitro and in silico tests, have become more prevalent. In this thesis existing non-testing approaches (i.e. studies without additional experiments) have been extended, e.g. QSAR models, and new non-testing approaches, e.g. in vitro data supported structural alert systems, have been created. The main aspect of the thesis depends on the determination of data quality, improving modelling performance and supporting Adverse Outcome Pathways (AOPs) with definitions of structural alerts and physico-chemical properties. Furthermore, there was a clear focus on the transparency of models, i.e. approaches using algorithmic feature selection, machine learning etc. have been avoided. Furthermore structural alert systems have been written in an understandable and transparent manner. Beside the methodological aspects of this work, cosmetically relevant examples of models have been chosen, e.g. skin penetration and hepatic steatosis. Interpretations of models, as well as the possibility of adjustments and extensions, have been discussed thoroughly. As models usually do not depict reality flawlessly, consensus approaches of various non-testing approaches and in vitro tests should be used to support decision-making in the regulatory context. For example within read-across, it is feasible to use supporting information from QSAR models, docking, in vitro tests etc. By applying a variety of models, results should lead to conclusions being more usable/acceptable within toxicology. Within this thesis (and associated publications) novel methodologies on how to assess and employ statistical data quality and how to screen for potential liver toxicants have been described. Furthermore computational tools, such as models for skin permeability and dermal absorption, have been created

QSAR models for the prediction of dietary biomagnification factor in fish

Xenobiotics released in the environment can be taken up by aquatic and terrestrial organisms and can accumulate at higher concentrations through the trophic chain. Bioaccumulation is therefore one of the PBT properties that authorities require to assess for the evaluation of the risks that chemicals may pose to humans and the environment. The use of an integrated testing strategy (ITS) and the use of multiple sources of information are strongly encouraged by authorities in order to maximize the information available and reduce testing costs. Moreover, considering the increasing demand for development and the application of new approaches and alternatives to animal testing, the development of in silico cost-effective tools such as QSAR models becomes increasingly important. In this study, a large and curated literature database of fish laboratory-based values of dietary biomagnification factor (BMF) was used to create externally validated QSARs. The quality categories (high, medium, low) available in the database were used to extract reliable data to train and validate the models, and to further address the uncertainty in low-quality data. This procedure was useful for highlighting problematic compounds for which additional experimental effort would be required, such as siloxanes, highly brominated and chlorinated compounds. Two models were suggested as final outputs in this study, one based on good-quality data and the other developed on a larger dataset of consistent Log BMFL values, which included lower-quality data. The models had similar predictive ability; however, the second model had a larger applicability domain. These QSARs were based on simple MLR equations that could easily be applied for the predictions of dietary BMFL in fish, and support bioaccumulation assessment procedures at the regulatory level. To ease the application and dissemination of these QSARs, they were included with technical documentation (as QMRF Reports) in the QSAR-ME Profiler software for QSAR predictions available online

Integration of handheld NIR and machine learning to “Measure & Monitor” chicken meat authenticity

By combining portable, handheld near-infrared (NIR) spectroscopy with state-of-the-art classification algorithms, we developed a powerful method to test chicken meat authenticity. The research presented shows that it is both possible to discriminate fresh from thawed meat, based on NIR spectra, as well as to correctly classify chicken fillets according to the growth conditions of the chickens with good accuracy. In all cases, the random subspace discriminant ensemble (RSDE) method significantly outperformed other common classification methods such as partial least squares-discriminant analysis (PLS-DA), artificial neural network (ANN) and support vector machine (SVM) with classification accuracy of >95%. This study shows that handheld NIR coupled with machine learning algorithms is a useful, fast, non-destructive tool to identify the authenticity of chicken meat. By comparing and combining different protocols to measure the NIR spectra (i.e., through packaging and directly on meat), we show the possibilities for both consumers and food inspection authorities to check the authenticity and origin of packaged chicken fillet.</p

A Quantitative Structure Activity Relationship for acute oral toxicity of pesticides on rats: Validation, Domain of Application and Prediction

International audienceQuantitative Structure Activity Relationship (QSAR) models are expected to play an important role in the risk assessment of chemicals on humans and the environment. In this study, we developed a validated QSAR model to predict acute oral toxicity of 329 pesticides to rats because a few QSAR models have been devoted to predict the Lethal Dose 50 (LD50) of pesticides on rats. This QSAR model is based on 17 molecular descriptors, and is robust, externally predictive and characterized by a good applicability domain. The best results were obtained with a 17/9/1 Artificial Neural Network model trained with the Quasi Newton back propagation (BFGS) algorithm. The prediction accuracy for the external validation set was estimated by the Q2ext and the Root Mean Square error (RMS) which are equal to 0.948 and 0.201, respectively. 98.6% of external validation set is correctly predicted and the present model proved to be superior to models previously published. Accordingly, the model developed in this study provides excellent predictions and can be used to predict the acute oral toxicity of pesticides, particularly for those that have not been tested as well as new pesticides

Nucleic acid quadratic indices of the "macromolecular graph's nucleotides adjacency matrix" : Modeling of footprints after the interaction of paromomycin with the HIV-1 Ψ-RNA packaging region

This report describes a new set of macromolecular descriptors of relevance to nucleic acid QSAR/QSPR studies, nucleic acids' quadratic indices. These descriptors are calculated from the macromolecular graph's nucleotide adjacency matrix. A study of the interaction of the antibiotic Paromomycin with the packaging region of the RNA present in type-1 HIV illustrates this approach. A linear discriminant function gave rise to excellent discrimination between 90.10% (91/101) and 81.82% (9/11) of interacting/noninteracting sites of nucleotides in training and test set, respectively. The LOO crossvalidation procedure was used to assess the stability and predictability of the model. Using this approach, the classification model has shown a LOO global good classification of 91.09%. In addition, the model's overall predictability oscillates from 89.11% until 87.13%, when n varies from 2 to 3 in leave-n-out jackknife method. This value stabilizes around 88.12% when n was > 3. On the other hand, a linear regression model predicted the local binding affinity constants [log K (10-4M-1)] between a specific nucleotide and the aforementioned antibiotic. The linear model explains almost 92% of the variance of the experimental log K (R = 0.96 and s = 0.07) and LOO press statistics evidenced its predictive ability (q2 = 0.85 and scv = 0.09). These models also permit the interpretation of the driving forces of the interaction process. In this sense, developed equations involve short-reaching (k ≤ 3), middle-reaching (4 k k = 10 or greater) nucleotide's quadratic indices. This situation points to electronic and topologic nucleotide's backbone interactions control of the stability profile of Paromomycin-RNA complexes. Consequently, the present approach represents a novel and rather promising way to chem & bioinformatics research.Facultad de Ciencias Exacta

Methods for reliability and uncertainty assessment and for applicability evaluations of classification- and regression-based QSARs

This article provides an overview of methods for reliability assessment of quantitative structure–activity relationship (QSAR) models in the context of regulatory acceptance of human health and environmental QSARs. Useful diagnostic tools and data analytical approaches are highlighted and exemplified. Particular emphasis is given to the question of how to define the applicability borders of a QSAR and how to estimate parameter and prediction uncertainty. The article ends with a discussion regarding QSAR acceptability criteria. This discussion contains a list of recommended acceptability criteria, and we give reference values for important QSAR performance statistics. Finally, we emphasize that rigorous and independent validation of QSARs is an essential step toward their regulatory acceptance and implementation. Key words: QSAR acceptability criteria, QSAR applicability domain, QSAR reliability, QSAR uncertainty estimation, QSAR validation