Modeling of the Acute Toxicity of Benzene Derivatives by Complementary QSAR Methods

A data set containing acute toxicity values (96-h LC50) of 69 substituted benzenes for fathead minnow (Pimephales promelas) was investigated with two Quantitative Structure- Activity Relationship (QSAR) models, either using or not using molecular descriptors, respectively. Recursive Neural Networks (RNN) derive a QSAR by direct treatment of the molecular structure, described through an appropriate graphical tool (variable-size labeled rooted ordered trees) by defining suitable representation rules. The input trees are encoded by an adaptive process able to learn, by tuning its free parameters, from a given set of structureactivity training examples. Owing to the use of a flexible encoding approach, the model is target invariant and does not need a priori definition of molecular descriptors. The results obtained in this study were analyzed together with those of a model based on molecular descriptors, i.e. a Multiple Linear Regression (MLR) model using CROatian MultiRegression selection of descriptors (CROMRsel). The comparison revealed interesting similarities that could lead to the development of a combined approach, exploiting the complementary characteristics of the two approaches

Modeling of the acute toxicity of benzene derivatives by complementary QSAR methods

A data set containing acute toxicity values (96-h LC50) of 69 substituted benzenes for fathead minnow (Pimephales promelas) was investigated with two Quantitative Structure- Activity Relationship (QSAR) models, either using or not using molecular descriptors, respectively. Recursive Neural Networks (RNN) derive a QSAR by direct treatment of the molecular structure, described through an appropriate graphical tool (variable-size labeled rooted ordered trees) by defining suitable representation rules. The input trees are encoded by an adaptive process able to learn, by tuning its free parameters, from a given set of structureactivity training examples. Owing to the use of a flexible encoding approach, the model is target invariant and does not need a priori definition of molecular descriptors. The results obtained in this study were analyzed together with those of a model based on molecular descriptors, i.e. a Multiple Linear Regression (MLR) model using CROatian MultiRegression selection of descriptors (CROMRsel). The comparison revealed interesting similarities that could lead to the development of a combined approach, exploiting the complementary characteristics of the two approaches

定量的構造物性相関/定量的構造活性相関モデルの逆解析を利用した化学構造創出に関する研究

学位の種別: 課程博士審査委員会委員 : （主査）東京大学教授 船津 公人, 東京大学教授 酒井 康行, 東京大学准教授 杉山 弘和, 東京大学准教授 伊藤 大知, 京都大学特任教授 奧野 恭史, スイス連邦工科大学教授 Gisbert SchneiderUniversity of Tokyo(東京大学

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

Novel topological descriptors for analyzing biological networks

<p>Abstract</p> <p>Background</p> <p>Topological descriptors, other graph measures, and in a broader sense, graph-theoretical methods, have been proven as powerful tools to perform biological network analysis. However, the majority of the developed descriptors and graph-theoretical methods does not have the ability to take vertex- and edge-labels into account, e.g., atom- and bond-types when considering molecular graphs. Indeed, this feature is important to characterize biological networks more meaningfully instead of only considering pure topological information.</p> <p>Results</p> <p>In this paper, we put the emphasis on analyzing a special type of biological networks, namely bio-chemical structures. First, we derive entropic measures to calculate the information content of vertex- and edge-labeled graphs and investigate some useful properties thereof. Second, we apply the mentioned measures combined with other well-known descriptors to supervised machine learning methods for predicting Ames mutagenicity. Moreover, we investigate the influence of our topological descriptors - measures for only unlabeled vs. measures for labeled graphs - on the prediction performance of the underlying graph classification problem.</p> <p>Conclusions</p> <p>Our study demonstrates that the application of entropic measures to molecules representing graphs is useful to characterize such structures meaningfully. For instance, we have found that if one extends the measures for determining the structural information content of unlabeled graphs to labeled graphs, the uniqueness of the resulting indices is higher. Because measures to structurally characterize labeled graphs are clearly underrepresented so far, the further development of such methods might be valuable and fruitful for solving problems within biological network analysis.</p

Development of Computer-Aided Molecular Design Methods for Bioengineering Applications

Computer-aided molecular design (CAMD) offers a methodology for rational product design. The CAMD procedure consists of pre-design, design and post-design phases. CAMD was used to address two bioengineering problems: design of excipients for lyophilized protein formulations and design of ionic liquids for use in bioseparations. Protein stability remains a major concern during protein drug development. Lyophilization, or freeze-drying, is often sought to improve chemical stability. However, lyophilization can result in protein aggregation. Excipients, or additives, are included to stabilize proteins in lyophilized formulations. CAMD was used to rationally select or design excipients for lyophilized protein formulations. The use of solvents to aid separation is common in chemical processes. Ionic liquids offer a class of molecules with tunable properties that can be altered to find optimal solvents for a given application. CAMD was used to design ionic liquids for extractive distillation and in situ extractive fermentation processes. The pre-design phase involves experimental data gathering and problem formulation. When available, data was obtained from literature sources. For excipient design, data of percent protein monomer remaining post-lyophilization was measured for a variety of protein-excipient combinations. In problem formulation, the objective was to minimize the difference between the properties of the designed molecule and the target property values. Problem formulations resulted in either mixed-integer linear programs (MILPs) or mixed-integer non-linear programs (MINLPs). The design phase consists of the forward problem and the reverse problem. In the forward problem, linear quantitative structure-property relationships (QSPRs) were developed using connectivity indices. Chiral connectivity indices were used for excipient property models to improve fit and incorporate three-dimensional structural information. Descriptor selection methods were employed to find models that minimized Mallow's Cp statistic, obtaining models with good fit while avoiding overfitting. Cross-validation was performed to access predictive capabilities. Model development was also performed to develop group contribution models and non-linear QSPRs. A UNIFAC model was developed to predict the thermodynamic properties of ionic liquids. In the reverse problem of the design phase, molecules were proposed with optimal property values. Deterministic methods were used to design ionic liquids entrainers for azeotropic distillation. Tabu search, a stochastic optimization method, was applied to both ionic liquid and excipient design to provide novel molecular candidates. Tabu search was also compared to a genetic algorithm for CAMD applications. Tuning was performed using a test case to determine parameter values for both methods. After tuning, both stochastic methods were used with design cases to provide optimal excipient stabilizers for lyophilized protein formulations. Results suggested that the genetic algorithm provided a faster time to solution while the tabu search provides quality solutions more consistently. The post-design phase provides solution analysis and verification. Process simulation was used to evaluate the energy requirements of azeotropic separations using designed ionic liquids. Results demonstrated that less energy was required than processes using conventional entrainers or ionic liquids that were not optimally designed. Molecular simulation was used to guide protein formulation design and may prove to be a useful tool in post-design verification. Finally, prediction intervals were used for properties predicted from linear QSPRs to quantify the prediction error in the CAMD solutions. Overlapping prediction intervals indicate solutions with statistically similar property values. Prediction interval analysis showed that tabu search returns many results with statistically similar property values in the design of carbohydrate glass formers for lyophilized protein formulations. The best solutions from tabu search and the genetic algorithm were shown to be statistically similar for all design cases considered. Overall the CAMD method developed here provides a comprehensive framework for the design of novel molecules for bioengineering approaches