Analysis of Multitarget Activities and Assay Interference Characteristics of Pharmaceutically Relevant Compounds

The availability of large amounts of data in public repositories provide a useful source of knowledge in the field of drug discovery. Given the increasing sizes of compound databases and volumes of activity data, computational data mining can be used to study different characteristics and properties of compounds on a large scale. One of the major source of identification of new compounds in early phase of drug discovery is high-throughput screening where millions of compounds are tested against many targets. The screening data provides opportunities to assess activity profiles of compounds. This thesis aims at systematically mining activity data from publicly available sources in order to study the nature of growth of bioactive compounds, analyze multitarget activities and assay interference characteristics of pharmaceutically relevant compounds in context of polypharmacology. In the first study, growth of bioactive compounds against five major target families is monitored over time and compound-scaffold-CSK (cyclic skeleton) hierarchy is applied to investigate structural diversity of active compounds and topological diversity of their scaffolds. The next part of the thesis is based on the analysis of screening data. Initially, extensively assayed compounds are mined from the PubChem database and promiscuity of these compounds is assessed by taking assay frequencies into account. Next, DCM (dark chemical matter) or consistently inactive compounds that have been extensively tested are systematically extracted and their analog relationships with bioactive compounds are determined in order to derive target hypotheses for DCM. Further, PAINS (pan-assay interference compounds) are identified in the extensively tested set of compounds using substructure filters and their assay interference characteristics are studied. Finally, the limitations of PAINS filters are addressed using machine learning models that can distinguish between promiscuous and DCM PAINS. Structural context dependence of PAINS activities is studied by assessing predictions through feature weighting and mapping

Chemoinformatics-Driven Approaches for Kinase Drug Discovery

Given their importance for the majority of cell physiology processes, protein kinases are among the most extensively studied protein targets in drug discovery. Inappropriate regulation of their basal levels results in pathophysiological disorders. In this regard, small-molecule inhibitors of human kinome have been developed to treat these conditions effectively and improve the survival rates and life quality of patients. In recent years, kinase-related data has become increasingly available in the public domain. These large amounts of data provide a rich knowledge source for the computational studies of kinase drug discovery concepts. This thesis aims to systematically explore properties of kinase inhibitors on the basis of publicly available data. Hence, an established "selectivity versus promiscuity" conundrum of kinase inhibitors is evaluated, close structural analogs with diverging promiscuity levels are analyzed, and machine learning is employed to classify different kinase inhibitor binding modes. In the first study, kinase inhibitor selectivity trends are explored on the kinase pair level where kinase structural features and phylogenetic relationships are used to explain the obtained selectivity information. Next, selectivity of clinical kinase inhibitors is inspected on the basis of cell-based profiling campaign results to consolidate the previous findings. Further, clinical candidates are mapped to medicinal chemistry sources and promiscuity levels of different inhibitor subsets are estimated, including designated chemical probes. Additionally, chemical probe analysis is extended to expert-curated representatives to correlate the views established by scientific community and evaluate their potential for chemical biology applications. Then, large-scale promiscuity analysis of kinase inhibitor data combining several public repositories is performed to subsequently explore promiscuity cliffs (PCs) and PC pathways and study structure-promiscuity relationships. Furthermore, an automated extraction protocol prioritizing the most informative pathways is proposed with focus on those containing promiscuity hubs. In addition, the generated promiscuity data structures including cliffs, pathways, and hubs are discussed for their potential in experimental and computational follow-ups and subsequently made publicly available. Finally, machine learning methods are used to develop classification models of kinase inhibitors with distinct experimental binding modes and their potential for the development of novel therapeutics is assessed

Índice semi-empírico topológico: desenvolvimento e aplicação de um novo descritor molecular em estudos de correlação quantitativa estrutura-propriedade (QSPR)

Tese (doutorado) - Universidade Federal de Santa Catarina, Centro de Ciências Físicas e Matemáticas. Programa de Pós-Graduação em Química.Neste estudo um novo descritor molecular - Índice Semi-Empírico Topológico (IET) - foi desenvolvido, a fim de estabelecer correlações quantitativas entre estrutura e propriedade (QSPR), para diferentes classes de compostos. Este Índice foi desenvolvido e otimizado para prever a retenção cromatográfica de alcenos ramificados, alcanos metil ramificados produzidos por insetos e álcoois saturados, em fases estacionárias de baixa polaridade. Foi avaliada, também, a habilidade de previsão do IET para a retenção cromatográfica de álcoois, aldeídos e cetonas em fases estacionárias mais polares. Os estudos preliminares aplicando o IET a diferentes propriedades/atividades apresentaram resultados promissores para a aplicação futura deste novo método. Para alcenos e álcoois foram obtidas correlações entre o IET e as propriedades (ponto de ebulição normal, refração molar, volume molar, calor de combustão, calor de vaporização molar e coeficiente de partição octanol/água), com valores de r > 0,94. As correlações quantitativas estrutura-atividade (QSAR) foram testadas para álcoois saturados, onde as atividades biológicas investigadas foram: atividade narcótica sobre larvas das cracas, toxicidade em aranhas e tomates e odor (r > 0,88). A qualidade dos resultados obtidos neste trabalho para a previsão de diferentes propriedades/atividades, empregando o IET como descritor molecular, pode ser considerada como uma importante etapa na direção de estudos futuros em QSAR/QSPR/QSRR

Estrategias computacionales en el desarrollo de neurofármacos: una tecnología de éxito

La química médica es una de las consideradas ciencias farmacéuticas, cuyo objetivo fundamental es la identificación y desarrollo de nuevos fármacos, seguros y efectivos, para el tratamiento de diversas patologías. La química médica se engloba en un ámbito multidisciplinar, que aúna química, biología y farmacia, y multitud de técnicas derivadas de éstas.El proceso de descubrimiento y desarrollo de un fármaco es un proceso largo, que dura en torno a unos 15 años, con un alto coste (cercano a los 1000 millones de euros), y con una baja tasa de éxito (1/5000). Todas las complicaciones asociadas a este proceso hacen de él un camino largo y difícil de abordar, por lo que el uso de nuevas herramientas, que faciliten este camino, ha sido una objetivo clave en la investigación básica. Una de las diferentes técnicas que han tenido un mayor crecimiento en las últimas décadas son las técnicas computacionales. El desarrollo, desde los años 1950, de las ciencias informáticas, en conjunto con la mejora de los recursos computacionales, y su aplicación a la química médica mediante las técnicas de modelado molecular, ha supuesto un punto de inflexión en el desarrollo de proyectos racionales de obtención de nuevos fármacos..