no / Predictive toxicology is concerned with the development of models that are able to predict the toxicity of chemicals. A reliable prediction of toxic effects of chemicals in living systems is highly desirable in cosmetics, drug design or food protection to speed up the process of chemical compound discovery while reducing the need for lab tests. There is an extensive literature associated with the best practice of model generation and data integration but management and automated identification of relevant models from available collections of models is still an open problem. Currently, the decision on which model should be used for a new chemical compound is left to users. This paper intends to initiate the discussion on automated model identification. We present an algorithm, based on Pareto optimality, which mines model collections and identifies a model that offers a reliable prediction for a new chemical compound. The performance of this new approach is verified for two endpoints: IGC50 and LogP. The results show a great potential for automated model identification methods in predictive toxicology.

Submitted by PPG Engenharia e Tecnologia de Materiais (engenharia.pg.materiais@pucrs.br) on 2018-08-27T20:28:46Z No. of bitstreams: 1 Uma perspectiva da modelagem QSPR para triagem-desenho de catalisadores para a s?ntese de carbona.pdf: 5038129 bytes, checksum: cd9bae4ba9eacd711c360bc304996732 (MD5) / Approved for entry into archive by Sheila Dias (sheila.dias@pucrs.br) on 2018-08-28T12:42:24Z (GMT) No. of bitstreams: 1 Uma perspectiva da modelagem QSPR para triagem-desenho de catalisadores para a s?ntese de carbona.pdf: 5038129 bytes, checksum: cd9bae4ba9eacd711c360bc304996732 (MD5) / Made available in DSpace on 2018-08-28T13:11:11Z (GMT). No. of bitstreams: 1 Uma perspectiva da modelagem QSPR para triagem-desenho de catalisadores para a s?ntese de carbona.pdf: 5038129 bytes, checksum: cd9bae4ba9eacd711c360bc304996732 (MD5) Previous issue date: 2018-05-29 / To date, only a small number of organocatalysts have been applied to produce oleochemical carbonates, while the description of new catalysts system still limited. This work presents a preliminary perspective of Quantitative Structure-Property Relationship (QSPR) modeling to assist in the targeted choice/design of active organocatalysts to produce cyclic carbonates. The QSPR was developed by applying the molecular descriptors (2D) to model the structure-property relationship between the organocatalysts features and its activity to produce oleochemical carbonates. From the virtual screening, a total of 122 catalysts have their activity predicted and the best molecular targets are proposed. The principal molecular features (organic structure, molecular arrangement, carbon chain size and substituent type) were identified through data mining, while the principal component analysis (PCA) proved to be suitable to perform the exploratory analysis of the molecules set. In addition, is presented the first report of the application of cetyltrimethylammonium bromide (CTAB) as a new catalyst to produce oleochemical carbonate derived from soy, canola and rice oils. The reactions were performed in a 50 cm3 stainless steel autoclave at 120?C, for 48 hours, without stirring, 5 MPa (p, CO2), 2 g of epoxidized oil, 4 mL of butanol and 5 mol% of CTAB. From the proposed method, all reactions showed more than 98% of epoxide conversion to cyclic carbonate for all the vegetable oil. In this way, the QSPR modelling can be applied to reduce the costs and time in the organocatalysts screening/design for the cyclic carbonates synthesis from CO2 and epoxides. / At? o momento, apenas um pequeno n?mero de organocatalisadores foram aplicados para produ??o de carbonatos oleoqu?micos, enquanto a descri??o de novos sistemas de catalisadores ainda ? limitada. O presente trabalho apresenta uma perspectiva preliminar da modelagem por Rela??o Quantitativa Estrutura-Propriedade (QSPR) para auxiliar na escolha/desenho de novos organocatalisadores para produ??o de carbonatos c?clicos. O QSPR foi desenvolvido aplicando os descritores moleculares (2D) para modelar a rela??o estrutura-propriedade entre as caracter?sticas dos organocatalisadores e sua atividade para produ??o de carbonatos oleoqu?micos. A partir da triagem virtual, um total de 122 catalisadores tiveram sua atividade prevista e os melhores alvos moleculares s?o propostos. As principais caracter?sticas moleculares (estrutura org?nica, arranjo molecular, tamanho da cadeia de carbono e tipo de substituinte) foram identificadas atrav?s da minera??o de dados, enquanto a an?lise de componentes principais (PCA) mostrou-se adequada para realizar a an?lise explorat?ria do conjunto de mol?culas. Al?m disso, ? apresentado o primeiro relato da aplica??o do brometo de cetiltrimetilam?nio (CTAB) como um catalisador para a produ??o de carbonato oleoqu?mico derivados dos ?leos de soja, canola e arroz. As rea??es foram realizadas em uma autoclave de a?o inoxid?vel de 50 cm3 a 120 ? C, durante 48 horas, sem agita??o, 5 MPa (p, CO2), 2 g de ?leo epoxidado, 4 mL de butanol e 5% molar de CTAB. A partir do m?todo proposto, todas as rea??es apresentaram mais de 98% de convers?o de ep?xido em carbonato c?clico para todos os ?leos vegetais. Desta forma, a modelagem QSPR pode ser aplicada para reduzir os custos e tempo na sele??o/desenho de organocatalisadores para a s?ntese de carbonatos c?clicos a partir de CO2 e ep?xidos.

<p>Aqueous solubility is a key parameter influencing the bioavailability of drugs and drug candidates. In this thesis computational models for the prediction of aqueous drug solubility were explored. High quality experimental solubility data for drugs were generated using a standardised protocol and models were developed using multivariate data analysis tools and calculated molecular descriptors. In addition, structural features associated with either solid-state limited or solvation limited solubility of drugs were identified.</p><p>Solvation, as represented by the octanol-water partition coefficient (log<i>P</i>), was found to be the dominant factor limiting the solubility of drugs, with solid-state properties being the second most important limiting factor.</p><p>The relationship between the chemical structure of drugs and the strength of their crystal lattice was studied for a dataset displaying log<i>P</i>-independent solubility. Large, rigid and flat molecules with an extended ring-structure and a large number of conjugated π-bonds were found to be more likely to have their solubility limited by a strong crystal lattice than were small, spherically shaped molecules with flexible side-chains.</p><p>Finally, the relationship between chemical structure and drug solvation was studied using computer simulated values of the free energy of hydration. Drugs exhibiting poor hydration were found to be large and flexible, to have low polarisability and few hydrogen bond acceptors and donors.</p><p>The relationship between the structural features of drugs and their aqueous solubility discussed in this thesis provide new rules-of-thumb that could guide decision-making in early drug discovery.</p>

Aqueous solubility is a key parameter influencing the bioavailability of drugs and drug candidates. In this thesis computational models for the prediction of aqueous drug solubility were explored. High quality experimental solubility data for drugs were generated using a standardised protocol and models were developed using multivariate data analysis tools and calculated molecular descriptors. In addition, structural features associated with either solid-state limited or solvation limited solubility of drugs were identified. Solvation, as represented by the octanol-water partition coefficient (logP), was found to be the dominant factor limiting the solubility of drugs, with solid-state properties being the second most important limiting factor. The relationship between the chemical structure of drugs and the strength of their crystal lattice was studied for a dataset displaying logP-independent solubility. Large, rigid and flat molecules with an extended ring-structure and a large number of conjugated π-bonds were found to be more likely to have their solubility limited by a strong crystal lattice than were small, spherically shaped molecules with flexible side-chains. Finally, the relationship between chemical structure and drug solvation was studied using computer simulated values of the free energy of hydration. Drugs exhibiting poor hydration were found to be large and flexible, to have low polarisability and few hydrogen bond acceptors and donors. The relationship between the structural features of drugs and their aqueous solubility discussed in this thesis provide new rules-of-thumb that could guide decision-making in early drug discovery.

Machine learning (ML) methods have been applied to the analysis of a range of biological systems. This thesis evaluates the application of these methods to the problem domain of skin permeability. ML methods offer great potential in both predictive ability and their ability to provide mechanistic insight to, in this case, the phenomena of skin permeation. Historically, refining mathematical models used to predict percutaneous drug absorption has been thought of as a key factor in this field. Quantitative Structure-Activity Relationships (QSARs) models are used extensively for this purpose. However, advanced ML methods successfully outperform the traditional linear QSAR models. In this thesis, the application of ML methods to percutaneous absorption are investigated and evaluated. The major approach used in this thesis is Gaussian process (GP) regression method. This research seeks to enhance the prediction performance by using local non-linear models obtained from applying clustering algorithms. In addition, to increase the model's quality, a kernel is generated based on both numerical chemical variables and categorical experimental descriptors. Monte Carlo algorithm is also employed to generate reliable models from variable data which is inevitable in biological experiments. The datasets used for this study are small and it may raise the over-fitting/under-fitting problem. In this research I attempt to find optimal values of skin permeability using GP optimisation algorithms within small datasets. Although these methods are applied here to the field of percutaneous absorption, it may be applied more broadly to any biological system.

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