Dissertations / Theses on the topic 'Computer-Aided Drug Discovery and Design (CADD)'

A situação da tuberculose (TB) foi alterada de forma significativa pela síndrome de imunodeficiência adquirida (SIDA ou AIDS) e pelo aparecimento de novas cepas do Mycobacterium tuberculosis resistentes ao tratamento quimioterápico, que justificariam a pesquisa de novos agentes antimicobacterianos. Alvos interessantes têm emergido para o planejamento racional de novos fármacos contra a TB, particularmente, considerando processos metabólicos específicos que ocorrem durante a biossíntese da parede celular micobacteriana e que envolvem a síntese de ácidos graxos (FAS-II, fatty acid synthase). O sistema FAS-II constitui diferença bioquímica importante entre mamíferos e micobatérias. A enzima enoil-acp (acyl carrier protein, proteína acil-carregadora) redutase (ENR) é alvo determinante no sistema FAS-II, responsável pela etapa de alongamento dos ácidos micólicos, que são os principais componentes da parede celular do M. tuberculosis. O presente projeto tem como objetivo a aplicação de metodologias do planejamento de fármacos auxiliado por computador, CADD (Computer-Aided Drug Design), em um conjunto de derivados pirrolidina carboxamidas descritos como inibidores potenciais da ENR do M. tuberculosis (InhA) com intuito de mapear o farmacóforo, investigar a orientação dos ligantes no sítio ativo e os tipos de interações que se estabelecem com os resíduos de aminoácidos do sítio de interação. Inicialmente, investigaram-se as relações quantitativas entre estrutura química e atividade biológica (QSAR, quantitative structure-activity relationships) com aplicação de abordagem multivariada. O melhor modelo QSAR indicou que propriedades estruturais, termodinâmicas e eletrônicas devem ser consideradas no processo de planejamento e proposição de novos protótipos potencialmente tuberculostáticos.<br>The incidence of tuberculosis (TB) disease has significantly changed considering the acquired immunodeficiency syndrome (AIDS) co-infection as well as the emergence of new Mycobacterium tuberculosis strains resistant to the currently chemotherapy. These facts support the search for novel antimycobacterial agents. Interesting targets have been elucidated and could be used for the rational design of new drugs against TB, primarily those related to specific biochemical metabolic pathways that occur during the cell wall biosynthesis, specially involved in the fatty acid synthase (FAS) system. The FAS-II system is an important biochemical difference between mammals and mycobacteria. The enoyl-ACP reductase (ENR) is the key enzyme in the FAS-II system, responsible for the elongation step of mycolic acids, which are the major components in the M. tuberculosis cell wall. This research project aims the application of computer-aided drug design (CADD) methodologies to a set of pyrrolidine carboxamide derivatives, which were previously reported as potential M. tuberculosis ENR (InhA) inhibitors, for mapping the pharmacophore, investigating the ligands\' orientation at the active site and also the interaction types regarding the amino acid residues in the active site. Initially, the quantitative structure-activity relationships (QSAR) were performed applying a multivariate approach. The best QSAR model indicated the structural, thermodynamic, and electronic properties must be taken into account in the design of novel leads as potential antituberculosis agents.

The potential of natural products in general, and marine natural products in particular, as pharmacological entities has been widely demonstrated in recent years. Marine benthic ecosystems contain an extraordinary range of diverse organisms that possess bioactive natural compounds, which are commonly used as defensive or protective chemical mechanisms. These effective defensive strategies are based on secondary metabolites that are crucial for the species survival. The pharmacological properties of these unique chemical compounds constitute an interesting and emerging hot research line, based upon exploiting them for the development of new drugs. The evolution, biodiversity, and specific environmental conditions found in marine ecosystems, such as Antarctica and the Mediterranean Sea, make them an amazing source of potential therapeutic agents. Interestingly, some of these natural products are capable to modulate protein functions in pathogenesis-related pathways. The process of discovery and development of new drugs, for instance small molecules, with the aforementioned capacity to modulate protein functions, is a tedious procedure that requires economic resources and time. To reduce these drawbacks, computer-aided drug design (CADD) has emerged as one of the most effective methods. A rapid exploration of the chemical space can be done with computational methods, and they are very interesting and useful complementary approaches to experimental methods. CADD techniques can be applied in different steps of the drug discovery pipeline, and also, can cover several phases of this pipeline. To that end, several objectives have been set and reached in this thesis: 1. To find possible therapeutic activities and to establish the capability to modulate protein functions in pathogenesis-related pathways from marine molecules by using different CADD tools and techniques: I. Improve the drug discovery pipeline by the elucidation of the possible therapeutic potential of a set of marine molecules against a list of targets related to different pathologies. II. Elucidation of different pharmacophoric features of marine compounds and a precise in silico binding study, highlighting the power of CADD techniques, and reporting the inhibitory activity of different natural products and indole scaffold derivatives as GSK3β, CK1δ, DYRK1A, and CLK1 inhibitors. III. Computational study and an experimental validation of meridianins and lignarenones as possible ATP and/or substrate inhibitors of GSK3β. The main conclusions of this thesis are that marine molecules can be used as therapeutic agents against protein kinases related to the AD, and the exemplification of CADD potential applied to marine drug discovery.<br>El potencial dels productes naturals en general, i els productes naturals marins en particular, com a entitats farmacològiques ha quedat demostrat al llarg dels últims anys. Els ecosistemes bentònics marins contenen una extraordinària diversitat d'organismes que posseeixen compostos naturals bioactius, que utilitzen com mecanismes químics defensius i de protecció. Aquestes efectives estratègies defensives es basen en metabòlits secundaris, crucials per a la supervivència de les espècies. Tenint en compte les propietats farmacològiques d'aquests compostos químics únics, utilitzar-los per al desenvolupament de nous fàrmacs constitueix una línia interessant de recerca emergent. L'evolució, la biodiversitat i les condicions específiques que es troben en els ecosistemes marins, com ara l'Antàrtida i el mar Mediterrani, els converteixen en una font increïble de possibles agents terapèutics, capaços de modular funcions de proteïnes involucrades en determinades patologies. El procés de descobriment i desenvolupament de nous fàrmacs, per exemple, molècules petites, és un procediment tediós que requereix de recursos econòmics i de temps. Per reduir aquests inconvenients, el disseny de fàrmacs assistit per ordinador (DFAO) ha sorgit com un dels mètodes principals i més eficaços. Es pot fer una exploració ràpida de l'espai químic amb mètodes computacionals i a més, són aproximacions complementàries als mètodes experimentals molt interessants i útils. Les tècniques de DFAO es poden aplicar en diferents passos del procés de descobriment de fàrmacs, i també, poden cobrir diverses fases d'aquest pipeline. Amb aquesta finalitat, es varen establir diversos objectius en aquesta tesi: 1. Dilucidar la possible activitat terapèutica i la capacitat per modular les funcions de proteïnes que estan relacionades amb una determinada patologia de les molècules marines mitjançant l'ús de diferents eines i tècniques de DFAO: I. millorar el pipeline de descobriment de fàrmacs mitjançant l'elucidació del possible potencial terapèutic d'un conjunt de molècules marines enfront d'una llista de dianes relacionades amb diferents patologies. II. Dilucidació de les diferents característiques farmacofóriques dels compostos marins i en un precís estudi d’unió in silico, destacant el poder de les tècniques de DFAO, i avaluar l'activitat inhibidora de diferents productes naturals i derivats d’esquelets indòlics com inhibidors de GSK3β, CK1δ, DYRK1A i CLK1. III. Estudi computacional i validació experimental de meridianines i lignarenones com a possibles inhibidors de GSK3β mitjançant la unió a la cavitat de l'ATP i/o del substrat. En relació amb aquests objectius, les conclusions principals d'aquesta tesi són, que les molècules marines poden ser utilitzades com a agents terapèutics contra proteïnes quinases relacionades amb la malaltia d’Alzheimer, i l'exemplificació del potencial de les tècniques de DFAO aplicat al descobriment de fàrmacs marins.

<strong>Chapter 1</strong> Provides an overview of drug discovery with particular emphasis on library selection and hit identification methods using virtual based approaches. <strong>Chapter 2</strong> Gives an outline of the bone morphogenetic protein (BMP) signalling pathway and literature BMP pathway modulators. The association between the regulation of BMP pathway and cardiomyogenesis is also described. <strong>Chapter 3</strong> Describes the use of ligand based virtual screening to discover small molecule activators of the BMP signalling pathway. A robust cell based BMP responsive gene activity reporter assay was developed to test the libraries of small molecules selected. Hit molecules from the screen were synthesised to validate activity. It was found that a group of known histone deacetylase (HDAC) inhibitors displayed most promising activity. These were evaluated in a secondary assay measuring the expression of two BMP pathway regulated genes, hepcidin and Id1, using reverse transcription polymerase chain reaction (RT-PCR). 188 was discovered to increase expression of both BMP-responsive genes. <strong>Chapter 4</strong> Provides an overview of existing cannabinoid receptor (CBR) modulating molecules and their connection to progression of atherosclerosis. <strong>Chapter 5</strong> Outlines the identification and optimisation of selective small molecule agonists acting at the cannabinoid 2 receptor (CB₂R). Ligand based virtual screen was undertaken and promising hits were synthesised to allow structure activity relationship (SAR) to be developed around the hit molecule providing further information of the functional groups tolerated at the active site. Subsequent studies led to the investigation and optimisation of physicochemical properties around 236 leading to the development of a suitable compound for in vivo testing. Finally, a CB₂R selective compound with favourable physicochemical properties was evaluated in vivo in a murine inflammation model and displayed reduced recruitment of monocytes to the site of inflammation.

The work of this thesis was focused on the development of high-performance algorithms for a new generation of molecular descriptors, with many advantages with respect to its predecessors, suitable for diverse applications in the field of drug design, as well as its implementation in commercial grade scientific software (Pentacle). As a first step, we developed a new algorithm (AMANDA) for discretizing molecular interaction fields which allows extracting from them the most interesting regions in an efficient way. This algorithm was incorporated into a new generation of alignmentindependent molecular descriptors, named GRIND-2. The computing speed and efficiency of the new algorithm allow the application of these descriptors in virtual screening. In addition, we developed a new alignment-independent encoding algorithm (CLACC) producing quantitative structure-activity relationship models which have better predictive ability and are easier to interpret than those obtained with other methods.<br>El trabajo que se presenta en esta tesis se ha centrado en el desarrollo de algoritmos de altas prestaciones para la obtención de una nueva generación de descriptores moleculares, con numerosas ventajas con respecto a sus predecesores, adecuados para diversas aplicaciones en el área del diseño de fármacos, y en su implementación en un programa científico de calidad comercial (Pentacle). Inicialmente se desarrolló un nuevo algoritmo de discretización de campos de interacción molecular (AMANDA) que permite extraer eficientemente las regiones de máximo interés. Este algoritmo fue incorporado en una nueva generación de descriptores moleculares independientes del alineamiento, denominados GRIND-2. La rapidez y eficiencia del nuevo algoritmo permitieron aplicar estos descriptores en cribados virtuales. Por último, se puso a punto un nuevo algoritmo de codificación independiente de alineamiento (CLACC) que permite obtener modelos cuantitativos de relación estructura-actividad con mejor capacidad predictiva y mucho más fáciles de interpretar que los obtenidos con otros métodos.

In recent years, the potential benefits of high-throughput virtual screening to the drug discovery community have been recognized, bringing an increase in the number of tools developed for this purpose. These programs have to process large quantities of data, searching for an optimal solution in a vast combinatorial range. This is particularly the case for protein-ligand docking, since proteins are sophisticated structures with complicated interactions for which either molecule might reshape itself. Even the very limited flexibility model to be considered here, using ligand conformation ensembles, requires six dimensions of exploration - three translations and three rotations - per rigid conformation. The functions for evaluating pose suitability can also be complex to calculate. Consequently, the programs being written for these biochemical simulations are extremely resource-intensive. This work introduces a pure computer science approach to the field, developing techniques to improve the effectiveness of such tools. Their architecture is generalized to an abstract pattern of nested layers for discussion, covering scoring functions, search methods, and screening overall. Based on this, new stratagems for molecular docking software design are described, including lazy or partial evaluation, geometric analysis, and parallel processing implementation. In addition, a range of novel algorithms are presented for applications such as active site detection with linear complexity (PIES) and small molecule shape description (PASTRY) for pre-alignment of ligands. The various stratagems are assessed individually and in combination, using several modified versions of an existing docking program, to demonstrate their benefit to virtual screening in practical contexts. In particular, the importance of appropriate precision in calculations is highlighted.

Developing a new drug costs up to US$2.6B and 13.5 years. To save money and time, we have developed a toolset for computer-aided drug discovery, and utilized our toolset to discover drugs for the treatment of cancers and influenza.<br>We first implemented a fast protein-ligand docking tool called idock, and obtained a substantial speedup over a popular counterpart. To facilitate the large-scale use of idock, we designed a heterogeneous web platform called istar, and collected a huge database of more than 23 million small molecules. To elucidate molecular interactions in web, we developed an interactive visualizer called iview. To synthesize novel compounds, we developed a fragment-based drug design tool called iSyn. To improve the predictive accuracy of binding affinity, we exploited the machine learning technique random forest to re-score both crystal and docked poses. To identify structurally similar compounds, we ported the ultrafast shape recognition algorithms to istar. All these tools are free and open source.<br>We applied our novel toolset to real world drug discovery. We repurposed anti-acne drug adapalene for the treatment of human colon cancer, and identified potential inhibitors of influenza viral proteins. Such new findings could hopefully save human lives.<br>開發一種新藥需要多至26億美元和13年半的時間。為節省金錢和時間，我們開發了一套計算機輔助藥物研發工具集，並運用該工具集尋找藥物治療癌症和流感。<br>我們首先實現了一個快速的蛋白與配體對接工具idock，相比一個同類流行軟件獲得了顯著的速度提升。為輔助idock 的大規模使用，我們設計了一個異構網站平台istar，收集了多達兩千三百萬個小分子的大型數據庫。為在網頁展示分子間相互作用，我們開發了一個交互式可視化軟件iview。為生成全新的化合物，我們開發了一個基於分子片段的藥物設計工具iSyn。為改進結合強度預測的精度，我們利用了機器學習技術隨機森林去重新打分晶體及預測構象。為尋找結構相似的化合物，我們移植了超快形狀識別算法至istar。所有這些工俱全是免費和開源。<br>我們應用了此創新工具集至現實世界藥物尋找中。我們發現抗痤瘡藥阿達帕林可用於治療人類結腸癌，亦篩選出流感病毒蛋白的潛在抑制物。這些新發現可望拯救人類生命。<br>Li, Hongjian.<br>Thesis (Ph.D.)--Chinese University of Hong Kong, 2015.<br>Includes bibliographical references (leaves 340-394).<br>Abstracts also in Chinese.<br>Title from PDF title page (viewed on 15, September, 2016).<br>Detailed summary in vernacular field only.<br>Detailed summary in vernacular field only.<br>Detailed summary in vernacular field only.

Ligand-protein inverse docking has recently been introduced as a computer method for identification of potential protein targets of a drug. A protein structure database is searched to find proteins to which a drug can bind or weakly bind. Examples of potential applications of this method in facilitating drug discovery include: (1) identification of unknown and secondary therapeutic targets of a drug, (2) prediction of potential toxicity and side effect of an investigative drug, and (3) probing molecular mechanism of bioactive herbal compounds such as those extracted from plants used in traditional medicines. This method and recent results on its applications in solving various drug discovery problems are reviewed.<br>Singapore-MIT Alliance (SMA)

Deciphering the activity of chemical molecules against a pathogenic organism is an essential task in drug discovery process. Virtual screening, in which few plausible molecules are selected from a large set for further processing using computational methods, has become an integral part and complements the expensive and time-consuming in vivo and in vitro experiments. To this end, it is essential to extract certain features from molecules which in the one hand are relevant to the biological activity under consideration, and on the other are suitable for designing fast and robust algorithms. The features/representations are derived either from physicochemical properties or their structures in numerical form and are known as descriptors. In this work we develop two new molecular-fragment descriptors based on the critical analysis of existing descriptors. This development is primarily guided by the notion of coding degeneracy, and the ordering induced by the descriptor on the fragments. One of these descriptors is derived based on the simple graph representation of the molecule, and attempts to encode topological feature or the connectivity pattern in a hierarchical way without discriminating atom or bond types. Second descriptor extends the first one by weighing the atoms (vertices) in consideration with the bonding pattern, valence state and type of the atom. Further, the usefulness of these indices is tested by ranking and classifying molecules in two previously studied large heterogeneous data sets with regard to their anti-tubercular and other bacterial activity. This is achieved by developing a scoring function based on clustering using these new descriptors. Clusters are obtained by ordering the descriptors of training set molecules, and identifying the regions which are (almost) exclusively coming from active/inactive molecules. To test the activity of a new molecule, overlap of its descriptors in those cluster (interpolation) is weighted. Our results are found to be superior compared to previous studies: we obtained better classification performance by using only structural information while previous studies used both structural features and some physicochemical parameters. This makes our model simple, more interpretable and less vulnerable to statistical problems like chance correlation and over fitting. With focus on predictive modeling, we have carried out rigorous statistical validation. New descriptors utilize primarily the topological information in a hierarchical way. This can have significant implications in the design of new bioactive molecules (inverse QSAR, combinatorial library design) which is plagued by combinatorial explosion due to use of large number of descriptors. While the combinatorial generation of molecules with desirable properties is still a problem to be satisfactorily solved, our model has potential to reduce the number of degrees of freedom, thereby reducing the complexity.

Deciphering the activity of chemical molecules against a pathogenic organism is an essential task in drug discovery process. Virtual screening, in which few plausible molecules are selected from a large set for further processing using computational methods, has become an integral part and complements the expensive and time-consuming in vivo and in vitro experiments. To this end, it is essential to extract certain features from molecules which in the one hand are relevant to the biological activity under consideration, and on the other are suitable for designing fast and robust algorithms. The features/representations are derived either from physicochemical properties or their structures in numerical form and are known as descriptors. In this work we develop two new molecular-fragment descriptors based on the critical analysis of existing descriptors. This development is primarily guided by the notion of coding degeneracy, and the ordering induced by the descriptor on the fragments. One of these descriptors is derived based on the simple graph representation of the molecule, and attempts to encode topological feature or the connectivity pattern in a hierarchical way without discriminating atom or bond types. Second descriptor extends the first one by weighing the atoms (vertices) in consideration with the bonding pattern, valence state and type of the atom. Further, the usefulness of these indices is tested by ranking and classifying molecules in two previously studied large heterogeneous data sets with regard to their anti-tubercular and other bacterial activity. This is achieved by developing a scoring function based on clustering using these new descriptors. Clusters are obtained by ordering the descriptors of training set molecules, and identifying the regions which are (almost) exclusively coming from active/inactive molecules. To test the activity of a new molecule, overlap of its descriptors in those cluster (interpolation) is weighted. Our results are found to be superior compared to previous studies: we obtained better classification performance by using only structural information while previous studies used both structural features and some physicochemical parameters. This makes our model simple, more interpretable and less vulnerable to statistical problems like chance correlation and over fitting. With focus on predictive modeling, we have carried out rigorous statistical validation. New descriptors utilize primarily the topological information in a hierarchical way. This can have significant implications in the design of new bioactive molecules (inverse QSAR, combinatorial library design) which is plagued by combinatorial explosion due to use of large number of descriptors. While the combinatorial generation of molecules with desirable properties is still a problem to be satisfactorily solved, our model has potential to reduce the number of degrees of freedom, thereby reducing the complexity.