Drug discovery is a complicated and expensive process in the billion dollar range. One way of making the drug development process more efficient is better information handling, modelling and visualisation. The majority of todays drugs are small molecules, which interact with drug targets to cause an effect. Since the 1980s large amounts of compounds have been systematically tested by robots in so called high-throughput screening. Ligand-based drug discovery is based on modelling drug molecules. In the field known as Quantitative Structure–Activity Relationship (QSAR) molecules are described by molecular descriptors which are used for building mathematical models. Based on these models molecular properties can be predicted and using the molecular descriptors molecules can be compared for, e.g., similarity. Bioclipse is a workbench for the life sciences which provides ligand-based tools through a point and click interface.  The aims of this thesis were to research, and develop new or improved ligand-based methods and open source software, and to work towards making these tools available for users through the Bioclipse workbench. To this end, a series of molecular signature studies was done and various Bioclipse plugins were developed. An introduction to the field is provided in the thesis summary which is followed by five research papers. Paper I describes the Bioclipse 2 software and the Bioclipse scripting language. In Paper II the laboratory information system Brunn for supporting work with dose-response studies on microtiter plates is described. In Paper III the creation of a molecular fingerprint based on the molecular signature descriptor is presented and the new fingerprints are evaluated for target prediction and found to perform on par with industrial standard commercial molecular fingerprints. In Paper IV the effect of different parameter choices when using the signature fingerprint together with support vector machines (SVM) using the radial basis function (RBF) kernel is explored and reasonable default values are found. In Paper V the performance of SVM based QSAR using large datasets with the molecular signature descriptor is studied, and a QSAR model based on 1.2 million substances is created and made available from the Bioclipse workbench.

Virtual Sreening (VS) is an in silico technique for drug discovery. An overview of VS methods is given and is seen to be approachable from two sides: structure based and ligand based. Structure based virtual screening uses explicit knowledge of the target receptor to suggest candidate receptor-ligand complexes. Ligand based virtual screening can infer required characteristics of binders from known ligands. A consideration for all virtual screening techniques is the amount of computing time required to arrive at a solution. For this reason, techniques of high performance computing have been applied to both the structural and ligand based approaches. A proven structure based virtual screening code LIDAEUS (Ligand Discovery At Edinburgh University) has been ported and parallelised to a massively parallel computing platform, the University of Edinburgh’s IBM Bluegene/l, consisting of 2,048 processor cores. A challenge in achieving scaling to such a large number of processors required implementation of a minimal communication parallel sort algorithm. Parallel efficiencies achieved within this parallelisation exceeded 99%, confirming that a near optimum strategy has been followed and capacity for running the code on a greater number of processors exists. This implementation of the program has been successfully used with a number of protein targets. The development of a new ligand based virtual screening code has been completed. The program UFSRAT (Ultra Fast Shape Recognition with Atom Types) takes the features of known binders and suggests molecules which will be able to make similar interactions. This similarity method is both fast (1 million molecules per hour per processor) and independent of input orientation. Along with UFSRAT, some other methods (VolRAT and UFSRGraph) based on UFSRAT have been developed, addressing different approaches to ligand based virtual screening. UFSRAT as an approach to discovering novel protein-ligand complexes has been validated with the discovery of a number of inhibitors for 11β-Hydroxysteroid Dehydrogenase type 1 and FK binding protein 12.

Virtual screening is a well-established part of computer-aided drug design, which heavily employs similarity search and similarity modeling methods. Most of the popular methods are target agnostic, leaving space for design of new methods that would take into account the specifics of the particular molecular target. Additionally, newly developed methods suffer from two related issues: benchmarking and availability. Benchmarking in the domain often suffers from the use of inappropriate reference methods, lack of reproducibility, and the use of nonstandard benchmark datasets. Although there have been several benchmarking studies in the domain that aim at addressing these issues, mainly by offering a standardized comparison, they often suffer from similar drawbacks. For these reasons, new methods fail to gain trust and therefore fail to become a part of the standard toolbox, which thus consists mostly of older methods. In this work, we address the above-described issues. First, we introduce new adaptive methods for virtual screening. Then, to make our and other newly developed methods readily available, we have designed and implemented a virtual screening tool. To address the benchmarking issue, we have compiled a publicly available collection of benchmarking datasets and proposed a platform offering a...

<p>Drug-drug interactions (DDIs) occur when multiple drugs are used concurrently. Caused by one drug inhibiting or inducing the metabolism of a second drug, DDIs often alter plasma concentrations and could seriously impact efficacy and safety of co-administered medications. Cytochrome P450 (CYP), a superfamily of enzymes, plays an important role in metabolizing a majority of FDA approved drugs currently on the market. 70% of predicable DDIs are associated with CYP enzymes inhibition. In-silico methods are increasingly adopted as a cost-effective complement to guide and prioritize efforts in drug discovery. Recent emerging applications of artificial intelligence algorithms have demonstrated promising results capable of prioritizing the selection of large chemical libraries, thereby outlining the future of in-silico methods assisting in drug discovery. Nevertheless, current methods rely on molecular descriptors that almost exclusively focus on chemical properties and atomic structures that fail to capture critical conformation and biological interaction related properties. There is also a lack of trainable molecular descriptors with feature specificity that reflect detailed protein-ligand binding energy and enable biological activity prediction. The overall objective of this dissertation is to understand molecular biological binding activity through electronic structure-based local descriptors derived from quantum based conceptual density functional theory (CDFT). This method will be used to assess the correlation of intermolecular interaction energy with ligand-protein binding with 2D feature maps reduced from the 4D molecular surfaces of the binding site and ligand (3D molecular surface with 1D electronic property). Additionally, it will be used to explore the possibility of predicting CYP related DDIs using descriptors generated using first principles including protein-ligand binding with specificity and strength and deep learning algorithms. Using quantum chemistry to interpret topological molecular information residing on 3D molecular surface permits the extraction of interacting features directly from the ligand structure. To achieve that, a set of curatable data containing consistent measurements was accessed through publicly accessible libraries. A series of novel Manifold Embedding of Molecular Surface (MEMS) descriptors were generated containing local electronic properties residing on the 3D molecule structure surface of each ligand using manifold learning. Major information were captured featuring electronic characteristics on the molecular 3D surface. Shape context was employed to derive transnational invariance feature vectors from MEMS with high granularity, thus preserving molecular information with specificity. DeepSet was utilized to perform permutation equivariance model training and validation. Powerful model learning is observed with an F-measure for all targets above 75% with the highest of 87% from external testing. Despite their promising prediction performance, molecular conformation changes and analytical featurization methods need to be implemented to expand model applicability and improve model reliability.</p>

>Magister Scientiae - MSc / New therapeutic strategies are needed for a diverse array of poorly understood neurological impairments. These include neurodegenerative disorders such as Parkinson’s disease and Alzheimer’s disease, and the psychiatric disorders such as depression, anxiety and drug dependence. Popular neuropharmacotherapies have focused on dopamine (DA), serotonin (5HT), γ-aminobutric acid (GABA) and glutamate systems (Jupp & Lawrence, 2010). However recent research points to the sigma receptor (σR) as a possible neuromodulatory system. Due to its multi-receptor action, the σR can trigger several significant biological pathways. This indicates its ideal potential as a drug target to effectively minimise drug dosage and potential side effects. Currently there are a limited number of σR ligands available and few possess the selectivity to significantly show σR’s role in neurological processes. Polycyclic amines have shown notable sigma activity and provide an advantageous scaffold for drug design that can improve pharmacodynamic and pharmacokinetic properties (Banister et al., 2010; Geldenhuys et al., 2005). Aryl-heterocycle amine groups were also shown to improve σR activity (Piergentili et al., 2009).

Computational drug design methods have great potential in drug discovery particularly in lead identification and lead optimization. 3-Phosphoinositide dependent kinase-1 (PDK1) is a protein kinase and a well validated anti-cancer target. Inhibitors of PDK1 have the potential to be developed as anti-cancer drugs. In this work, we have applied various novel computational drug design strategies to design and identify new PDK1 inhibitors with potential anti-cancer activity. We have pursued novel structure-based drug design strategies and identified a new binding mode for celecoxib and its derivatives binding with PDK1. This new binding mode provides a valuable basis for rational design of potent PDK1 inhibitors. In order to understand the structure-activity relationship of indolinone-based PDK1 inhibitors, we have carried out a combined molecular docking and three-dimensional quantitative structure-activity relationship (3D-QSAR) modeling study. The predictive ability of the developed 3D-QSAR models were validated using an external test set of compounds. An efficient strategy of the hierarchical virtual screening with increasing complexity was pursued to identify new hits against PDK1. Our approach uses a combination of ligand-based and structure-based virtual screening including shape-based filtering, rigid docking, and flexible docking. In addition, a more sophisticated molecular dynamics/molecular mechanics- Poisson-Boltzmann surface area (MD/MM-PBSA) analysis was used as the final filter in the virtual screening. Our screening strategy has led to the identification of a new PDK1 inhibitor. The anticancer activities of this compound have been confirmed by the anticancer activity assays of national cancer institute-developmental therapeutics program (NCI-DTP) using 60 cancer cell lines. The PDK1-inhibitor binding mode determined in this study may be valuable in future de novo drug design. The virtual screening approach tested and used in this study could also be applied to lead identification in other drug discovery efforts.

Philosophiae Doctor - PhD / Given the significant disease burden caused by human coronaviruses, the discovery of an effective antiviral strategy is paramount, however there is still no effective therapy to combat infection. This thesis details the in silica exploration of ligand libraries to identify candidate lead compounds that, based on multiple criteria, have a high probability of inhibiting the 3 chymotrypsin-like protease (3CUro) of human coronaviruses. Atomistic models of the 3CUro were obtained from the Protein Data Bank or theoretical models were successfully generated by homology modelling. These structures served the basis of both structure- and ligand-based drug design studies. Consensus molecular docking and pharmacophore modelling protocols were adapted to explore the ZINC Drugs-Now dataset in a high throughput virtual screening strategy to identify ligands which computationally bound to the active site of the 3CUro . Molecular dynamics was further utilized to confirm the binding mode and interactions observed in the static structure- and ligand-based techniques were correct via analysis of various parameters in a IOns simulation. Molecular docking and pharmacophore models identified a total of 19 ligands which displayed the potential to computationally bind to all 3CUro included in the study. Strategies employed to identify these lead compounds also indicated that a known inhibitor of the SARS-Co V 3CUro also has potential as a broad spectrum lead compound. Further analysis by molecular dynamic simulations largely confirmed the binding mode and ligand orientations identified by the former techniques. The comprehensive approach used in this study improves the probability of identifying experimental actives and represents a cost effective pipeline for the often expensive and time consuming process of lead discovery. These identified lead compounds represent an ideal starting point for assays to confirm in vitro activity, where experimentally confirmed actives will be proceeded to subsequent studies on lead optimization.

Submitted by Maike Costa (maiksebas@gmail.com) on 2017-09-13T11:59:49Z No. of bitstreams: 1 arquivototal.pdf: 7758959 bytes, checksum: db745d41b196978192ebc789e25f442b (MD5) / Made available in DSpace on 2017-09-13T11:59:49Z (GMT). No. of bitstreams: 1 arquivototal.pdf: 7758959 bytes, checksum: db745d41b196978192ebc789e25f442b (MD5) Previous issue date: 2016-02-26 / The use of plants for medicinal purposes is one of the oldest forms of medical practice of mankind, emphasizing the alkaloids because they present rich structural and pharmacological properties extensive variety. The drug design is aided by computer based strategies based on linkers or target. When developing new compounds, the structure-based techniques, such as docking, can be applied to study of certain receptor and its corresponding ligand, evaluating bindingprotein interactions. Whereas in the ligand-based methods, a database of known ligands is used, looking for ways to evaluate parameters (molecular descriptors) that can assist in the development of compounds with higher power. This study aimed to perform in silico studies to investigate drug-target interactions with alkaloids derived from natural products and their analogues with relevant pharmacological activity. Different molecular descriptors and methodologies were used in the studies developed. In chapter 2, the interaction of alkaloid bisindolic caulerpine (CLP) was evaluated with the enzyme involved in Alzheimer's disease (AD) monoamine oxidase B (MAO-B), and a database with 109 analogs. It was possible to observe a chemical parameter of inhibition of PLC analogues where the replacement of the radicals must be asymmetric with different polarity. The studies based on the linker and the structure associated with the classification drug-like chemical skeleton suggest that the PLC has potential use in the treatment of AD. In chapter 3, 8 alkaloids isolated Cissampelos sympodialis and 101 derivatives, had their inhibitory potential against enzyme (BACE, GSK-3β and MAO-A) involved in degenerative diseases assessed by in silico methods. consensual analysis showed affinity alkaloids bisbenzilisoquinolinics by BACE, incluindos the roraimine natural alkaloids and simpodialine-β-N-oxide, supporting interest in investigating this skeleton as an antagonist of this enzyme. In Chapter 4 we evaluated the multi-target potential of 148 aphorphinics alkaloids Annonaceae against Leishmania donovani. Six were selected enzymes of this neglected disease for theoretical study, which was associated with experimental four alkaloids available data and integrating the bank, which had pIC50 value inferior to 5.26. The xyloguyelline alkaloid was named as a potential multi-agent target, demonstrating activity against 5 of 6 enzymes evaluated, likely to activity of over 60%. fragment descriptors were used to create model-based binder in a parallel approach with molecular docking to predict the cytotoxic and against topoisomerase II activity azaphenantrene alkaloids in chapter 5. The cytotoxic activity of this skeleton alkaloids are well described in the literature, molecules having activity against several tumor cell lines. The IMB 6 analog and 23 IMB showed interesting activity and selectivity, with MolDock energy similar to liriodenine composed characterized by potent anti-tumor action, but with high toxicity. Important structural information is provided by spectroscopy nuclear magnetic resonance (NMR), and Chapter 6 aimed to discuss the importance of this technique for generating molecular descriptors. Studies that applied successfully in drug design NMR descriptors assisted by computer are described and several QSAR and QSPR having as support data chemical shifts. / A utilização de plantas com fins medicinais é uma das mais antigas formas de prática medicinal da humanidade, enfatizando os alcaloides, por apresentarem rica variedade estrutural e extensa propriedade farmacológica. O desenho de drogas auxiliado pelo computador é fundamentado em estratégias baseadas nos ligantes ou no alvo. No desenvolvimento de novos compostos, técnicas baseadas na estrutura, como o docking, podem ser aplicadas no estudo de um determinado receptor e seu respectivo ligante, avaliando as interações ligante-proteína. Ao passo que nos métodos baseados no ligante, um banco de ligantes conhecidos é utilizado, buscando modos de avaliar parâmetros (descritores moleculares) que possam auxiliar no desenvolvimento de compostos com maior potência. Este estudo teve como objetivo realizar estudos in silico para investigar interações fármaco-alvo com alcaloides oriundos de produtos naturais, e respectivos análogos, com relevante atividade farmacológica. Diferentes descritores moleculares e metodologias foram utilizadas nos estudos desenvolvidos. No capítulo 2, foi avaliado a interação do alcaloide bisindolico caulerpina (CLP) com a enzima envolvida na doença de Alzheimer (DA) monoamina oxidase B (MAO-B), além de um banco com 109 análogos. Foi possível observar um parâmetro químico de inibição dos análogos da CLP, onde a substituição dos radicais deve ser assimétrica com polaridade distinta. Os estudos dos baseados no ligante e na estrutura, associado à classificação drug-like, sugerem que o esqueleto químico da CLP tem potencial uso no tratamento da DA. No capítulo 3, 8 alcaloides isolados de Cissampelos sympodialis e 101 derivados, tiveram seu potencial inibitório contra enzimas (BACE, GSK-3β e MAO-A) envolvidas em doenças degenerativas avaliado por metodologias in silico. Análise consensual demonstrou afinidade de alcaloides bisbenzilisoquinolínicos pela BACE, incluindos os alcaloides naturais roraimina e simpodialina- β-N-oxide, suportando interesse em investigar este esqueleto como antagonista desta enzima. No capítulo 4 foi avaliado o potencial multi-target de 148 alcaloides aporfinicos de Annonaceae contra Leishmania donovani. Foram utilizadas seis enzimas desta doença negligenciada para o estudo teórico, que foi associado com dados experimentais de quatro alcaloides disponíveis e que integram o banco, que apresentaram valor pIC50 inferior a 5.26. O alcaloide xyloguyellina foi apontado como potencial agente multitarget, demonstrando atividade contra 5 das 6 enzimas avaliadas, com probabilidade de atividade superior a 60%. Descritores de fragmento foram utilizados para criar modelo baseado no ligante em uma abordagem paralela com docking molecular, para predizer a atividade citotóxica e contra topoisomerase II de azafenantreno alcaloides, no capítulo 5. A atividade citotóxica deste esqueleto de alcaloides está bem descrita na literatura, com diversas moléculas apresentando atividade contra linhagens de células tumorais. Os análogos IMB 6 e IMB 23 apresentaram interessante atividade e com seletividade, apresentando energia MolDock similar à liriodenina, composto caracterizado por potente ação antitumoral, porém com elevada toxicidade. Importantes informações estruturais são fornecidas pela espectroscopia de ressonância magnética nuclear (RMN), sendo o capítulo 6 destinado a discorrer sobre a importância desta técnica para geração de descritores moleculares. Estudos que aplicaram com sucesso descritores RMN em design de drogas assistida pelo computador encontram-se descritos, além de diversos estudos de QSAR e QSPR tendo como amparo dados de deslocamentos químicos.

Viral proteases play crucial roles in the life cycle and maturation of many viruses by processing the viral polyprotein after translation and in some cases cleaving host proteins associated with the immune response. The essential role of viral proteases makes them attractive therapeutic targets. In this thesis, I provide an introductory summary of viral proteases, their structure, mechanism, and inhibition, while the breadth of this thesis focuses on the Hepatitis C virus (HCV) NS3/4A and Zika virus (ZIKV) NS2B/NS3 viral proteases. HCV NS3/4A protease inhibitors (PIs) have become a mainstay in combination therapies. However, drug resistance remains a major problem against these PIs. In this thesis, I applied insights from the HCV substrate envelope (SE) model to develop strategies for designing PIs that are less susceptible to resistance. Also, I used the HCV NS3/4A protease as a model system to decipher the molecular mechanism and role of fluorination in HCV PIs potency and drug resistance. The drug design strategies described in this thesis have broad applications in drug design. The ZIKV is an emerging global threat, and currently, with no treatment available. In this thesis, I described the discovery, biochemical and antiviral evaluation of novel noncompetitive quinoxaline-based inhibitors of the ZIKV NS2B/NS3 protease. The inhibitors are proposed to interfere with NS2 binding to NS3, thereby preventing the protease from adopting the closed and active conformation. The inhibitors from this work will serve as lead compounds for further inhibitor development toward the goal of developing antivirals.