<p dir="ltr">Chemical science can be imagined as a universe of information in which individual galaxies, solar systems, stars, and planets are compounds, reactions, biomolecules, etc. which need to be discovered, researched, and documented. The problem with this is that the universe of chemical science is potentially vaster than the one in which we live, and we are exploring it in a relatively inefficient manner. There is a scene in one of my favorite television shows, Futurama, which paints a picture of traditional chemical exploration. Taking place in the 30^th century, the main character Fry loses his robot friend Bender in outer space and resorts to using a giant telescope in the Himalayan mountains to randomly search through points in space to try to find him. After days of searching nonstop, he gives up noting that it is an impossible task because space is so vast in size, and he is searching so inefficiently. While human exploration of chemistry may not be as inefficient, there are a lot of steps which are driven by trial and error and educated guesswork which ultimately introduce major inefficiencies into scientific discovery. While we don’t live in the 30^th century yet, we do have access to 21^stcentury technology which can assist in exploring chemistry in a more directed manner. This mainly involves using machine learning, search algorithms, and generative powered exploratory AI to serve as a force multiplier which can serve to assist human chemists in chemical exploration. To shamelessly compare this with another space-based sci-fi reference, this would be akin to deploying hundreds or thousands of automated space probes to search unexplored planets, akin to how the empire found the rebellion on Hoth in the Empire Strikes Back.</p><p dir="ltr">The journey to integrate AI with chemical exploration starts with the important concept of standardization and how to apply it to chemically relevant data. To easily organize, store, and access relevant aspects of small molecules, macromolecules, chemical reactions, biological assays, etc. it is imperative that data be represented in a standard format which accurately portrays necessary chemical information. This becomes especially relevant as humans aggregate more and more chemical data. In this thesis, we tackle a subset of standardization in Chapter 2 involving benchmarking sets for comparative evaluation of docking software. One major reason why standardization is so important is that standardization promotes ease of access to relevant data, regardless of if this access is attempted by human or computational means. While improving data access for humans is beneficial, computationally it is a game changer when datamining training data for machine learning (ML) applications. Having standardized data readily available for computational access allows for software to rapidly access and preprocess relevant data boosts efficiency in ML model training. In Chapter 4 of this thesis, the central database of the CIPHER close-loop system is standardized and integrated with a REST API, allowing for rapid data acquisition via a structured URL call. Having database standardization and a mechanism for easy data mining makes a database “ML ready” and promotes the database for ML applications.</p><p dir="ltr">Build upon data standardization and training ML models for chemical applications, the next step of this journey revolves around a concept known as a “chemical space” and how chemists can approximate and sample chemical spaces in a directed manner. In the context of this thesis, a chemical space can be visualized in the following manner. Start by envisioning any chemical relationship between some inputs and outputs as an unknown mathematical function. For example, if one is measuring the assay response of a specific drug at a certain concentration, the input would be the concentration, and the output would be the assay response. Then the bounds of this space are set by determining the range of input values and this forms a chemical space which corresponds to the chemical problem. Chemists sample these spaces every day when they go into the lab, run experiments, and analyze their data. While the example described above is relatively simple in scope, even if the relationship is very complex techniques such as ML can be used to approximate the relationship. An example of this approximation is shown in Chapter 3 of this thesis, where normalizing flow architecture is used to bias a vector space representation of molecules with chemical properties, creating a space which correlates compound and property and can be sampled to provided compounds with specific values of trained chemical properties. Training individual models is important, but to truly emulate certain chemical processes multiple models may need to be combined with physical instrumentation to efficiently sample and validate a chemical space. Chapter 4 of this thesis expands upon this concept by integrating a variety of ML modules with high-throughput (HT) bioassay instrumentation to create a “close loop” system designed around discovering, synthesizing, and validating non-addictive analgesics.</p><p dir="ltr">The final step of this journey is to integrate these systems which sample chemical spaces with AI, allowing for automated exploration of these spaces in a directed manner. There are several AI frameworks which can be used separately or combined to accomplish this task, but the framework that is the focus of this thesis is AI agents. AI agents are entities which use some form of AI to serve as a logical processing center which drives their exploration through a problem space. This can be a simple algorithm, some type of heuristic model, or an advance form of generative AI such as an LLM. Additionally, these agents generally have access to certain tools which serve as a medium for interaction with physical or computational environments, such as controlling a robotic arm or searching a database. Finally, these agents generally have a notion of past actions and observations, commonly referred to as memory, which allows agents to recall important information as they explore. Chapter 5 of this thesis details a custom agentic framework which is tailored towards complex scientific applications. This framework builds agents from source documentation around a specific user defined scope, provides them with access to literature and documentation in the form of embeddings, has custom memory for highly targeted retention, and allows form agents to communicate with one another to promote collaborative problem solving. Chapter 6 of this thesis showcase an application of a simpler agentic framework to an automated lipidomic workflow which performs comparative analysis on 5xFAD vs. WT mice brain tissue. The group of AI agents involved in this system generate mass spectrometry worklists, filter data into categories for analysis, perform comparative analysis, and allow for the user to dynamically create plots which can be used to answer specific statistical questions. In addition to performing all these operational and statistical analysis functions, the system includes an agent which uses document embeddings trained on curated technical manuals and protocols to answer user questions via a chatbot style interface. Overall, the system showcases how AI can effectivity be applied to relevant chemical problems to enhance speed, bolster accuracy, and improve usability.</p>

This dissertation details the use of computational methods to understand the effect that cancer-related mutations have on proteins that complex with nucleic acids. Firstly, we perform molecular dynamics (MD) simulations of various mutations in DNA polymerase κ (pol κ). Through an experimental collaboration, we classify the mutations as more or less active than the wild type complex, depending upon the incoming nucleotide triphosphate. From these classifications we use quantum mechanics/molecular mechanics (QM/MM) to explore the reaction mechanism. Preliminary analysis points to a novel method for nucleotide addition in pol κ. Secondly, we study the ten-eleven translocation 2 (TET2) enzyme in various contexts. We find that the identities of both the substrate and complementary strands (or lack thereof) are crucial for maintaining the complex structure. Separately, we find that point mutations within the protein can affect structural features throughout the complex, only at distal sites, or only within the active site. The mutation's position within the complex alone is not indicative of its impact. Thirdly, we share a new method that combines direct coupling analysis and MD to predict potential rescue mutations using poly(ADP-ribose) polymerase 1 as a model enzyme. Fourthly, we perform MD simulations of mutations in the protection of telomeres 1 (POT1) enzyme. The investigated variants modify the POT1-ssDNA complex dynamics and protein—DNA interactions. Fifthly, we investigate the incorporation of remdesivir and other nucleotide analogue prodrugs into the protein-RNA complex of severe acute respiratory syndrome-coronavirus 2 RNA-dependent RNA polymerase. We find evidence for destabilization throughout the complex and differences in inter-subunit communication for most of the incorporation patterns studied. Finally, we share a method for determining a minimum active region for QM/MM simulations. The method is validated using 4-oxalocrotonate, TET2, and DNA polymerase λ as test cases.

<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 self-assembly of structured particles into monodisperse clusters is a challenge on the nano-, micro- and even macro-scale. While biological systems are able to self-assemble with comparative ease, many aspects of this self-assembly are not fully understood. In this thesis, we look at the strategies and rules that can be applied to encourage the formation of monodisperse clusters. Though much of the inspiration is biological in nature, the simulations use a simple minimal patchy particle model and are thus applicable to a wide range of systems. The topics that this thesis addresses include: Encapsulation: We show how clusters can be used to encapsulate objects and demonstrate that such `templates' can be used to control the assembly mechanisms and enhance the formation of more complex objects. Hierarchical self-assembly: We investigate the use of hierarchical mechanisms in enhancing the formation of clusters. We find that, while we are able to extend the ranges where we see successful assembly by using a hierarchical assembly pathway, it does not straightforwardly provide a route to enhance the complexity of structures that can be formed. Pore formation: We use our simple model to investigate a particular biological example, namely the self-assembly and formation of heptameric alpha-haemolysin pores, and show that pore insertion is key to rationalising experimental results on this system. Phase re-entrance: We look at the computation of equilibrium phase diagrams for self-assembling systems, particularly focusing on the possible presence of an unusual liquid-vapour phase re-entrance that has been suggested by dynamical simulations, using a variety of techniques.

This thesis addresses simulation of electrochemical experiments, with an emphasis on processes of diffusional mass transport to electrode surface. Following system has been studied: &bull; Applying theoretical modeling and experimentation is shown that even significant surface roughness produced by deliberate polishing or scratching is not sufficient to be distinguished in cyclic voltammetry experiments conducted under the usual conditions. In stripping voltammetry experiment the shape of the voltammograms strongly depends on the model of the electron transfer but is not always sensitive to the precise model of the electrode surface; the conditions under which this is the case are identified, and generic roughness effects on stripping voltammetry are quantified. Electrode roughness can have a significant effect on the stripping of the metals from the solid electrode especially in respect of the voltammetric waveshape. &bull; We first consider two different models of electrodes covered with electroinactive layers: the electrode is covered with a uniform layer and the layer contains pinholes. Both models are simulated and then compared to identify conditions under which they can be distinguished. Next we propose generic model to predict the influence of electroactive layer on the cyclic voltammetric. The conditions under which deviation from the behavior of a planar electrode are predicted. &bull; We first consider one electron, one proton and next two electron, two proton reduction of surface bound species. Two mechanisms of reaction are considered: stepwise and concerted. Voltammetry studied under the three regimes of protons mass transport: infinitely fast (fully buffered solution), infinitely slow (infinitely high surface coverage of electrode) and intermediate case of finite rate of diffusional mass transport to electrode surface. Types of voltammograms observed in each case are presented and discussed. &bull; Theory of chronoamperometry on disc and ring-recessed microelectrodes and their arrays is reported. Three and four different regimes of transient current versus time can be observed at microelectrode arrays of disc and ring electrodes, accordingly. A generic, accurate and easy to use method of experimental chronoamperometric data analysis is proposed. It is shown that the method can be applied to the simultaneous measurement of D and nC in solution. &bull; The fabrication, characterization, and use of arrays of ring-recessed disk generator-colector microelectrodes are reported. Experiments and simulations relating to time- of-flight experiments in which material electrogenerated at a disk is diffusionally transported to the ring are reported. We further study voltammetry of electrochemically active species which undergoes first and second order chemical reactions. Current transients are found to be sensitive to the diffusion coefficient of both the reduced and oxidised species as well as to the rate of the chemical reaction and its mechanism.

Kinks are functionally important structural features found in the alpha-helices of many proteins, particularly membrane proteins. Structurally, they are points at which a helix abruptly changes direction. Previous kink definition and identification methods often disagree with one another. Here I describe three novel methods to characterise kinks, which improve on existing approaches. First, Kink Finder, a computational method that consistently locates kinks and estimates the error in the kink angle. Second the B statistic, a statistically robust method for identifying kinks. Third, Alpha Helices Assessed by Humans, a crowdsourcing approach that provided a gold-standard data set on which to train and compare existing kink identification methods. In this thesis, I show that kinks are a feature of long -helices in both soluble and membrane proteins, rather than just transmembrane -helices. Characteristics of kinks in the two types of proteins are similar, with Proline being the dominant feature in both types of protein. In soluble proteins, kinked helices also have a clear structural preference in that they typically point into the solvent. I also explored the conservation of kinks in homologous proteins. I found examples of conserved and non-conserved kinks in both the helix pairs and the helix families. Helix pairs with non-conserved kinks generally have less similar sequences than helix pairs with conserved kinks. I identified helix families that show highly conserved kinks, and families that contain non-conserved kinks, suggesting that some kinks may be flexible points in protein structures.

Molecular biophysics is a rapidly evolving field aimed at the physics-based investigation of the biomolecular processes that enable life. In this thesis, we explore two such processes: the thermodynamics of DNA bending, and the mechanism of the RecQ DNA helicase. A computational approach using a coarse-grained model of DNA is employed for the former; an experimental approach relying heavily on single-molecule fluorescence for the latter. There is much interest in understanding the physics of DNA bending, due to both its biological role in genome regulation and its relevance to nanotechnology. Small DNA bending fluctuations are well described by existing models; however, there is less consensus on what happens at larger bending fluctuations. A coarse-grained simulation is used to fully characterize the thermodynamics and mechanics of duplex DNA bending. We then use this newfound insight to harmonize experimental results between four distinct experimental systems: a 'molecular vise', DNA cyclization, DNA minicircles and a 'strained duplex'. We find that a specific structural defect present at large bending fluctuations, a 'kink', is responsible for the deviation from existing theory at lengths below about 80 base pairs. The RecQ DNA helicase is also of much biological and clinical interest, owing to its essential role in genome integrity via replication, recombination and repair. In humans, heritable defects in the RecQ helicases manifest clinically as premature aging and a greatly elevated cancer risk, in disorders such as Werner and Bloom syndromes. Unfortunately, the mechanism by which the RecQ helicase processes DNA remains poorly understood. Although several models have been proposed to describe the mechanics of helicases based on biochemical and structural data, ensemble experiments have been unable to address some of the more nuanced questions of helicase function. We prepare novel substrates to probe the mechanism of the RecQ helicase via single-molecule fluorescence, exploring DNA binding, translocation and unwinding. Using this insight, we propose a model for RecQ helicase activity.

Catalysis is a key area of chemistry. Through catalysis it is possible to achieve better synthetic routes, exploit molecules normally considered to be inactive and also attain novel chemical transformations. The development of new catalysts is crucial to furthering chemistry as a field. Computational chemistry, arising from applying the equations of quantum and classical mechanics to solving chemical problems, offers an essential route to investigating the underlying atomistic detail of catalysis. In this thesis calculations have been applied towards studying a number of different catalytic processes. The processing of renewable chemical sources via homogeneous reactions, specifically cardanol from cashew nuts, is discussed. All routes examined for monoreduction of a diene model by [Ru(H)(iPrOH)(Cl)(C₆H₆)] and [Ru(H)(iPrOH)(C₆H₆)]⁺ are energetically costly and would allow for total reduction of the diene if they were operating. While this accounts for the need of high temperatures, further work is required to elucidate the true mechanism of this small but surprisingly complex system. Gold-mediated protodecarboxylation was examined in tandem with experiment to find the subtle steric and electronic effects that dictate CO₂ extrusion from gold N-heterocyclic carbene activated benzene-derived carboxylic acids. The origin of a switch in the rate limiting step from decarboxylation to protodeauration with less activated substrates was also clearly demonstrated. Studies of gold systems are closed with examinations of 1,2-difluorobenzene C–H activation and CO₂ insertion by [Au(IPr)(OH)]. Calculations highlight that the proposed mechanism for oxazole-derived substrates cannot be extended to 1,2-difluorobenzene and instead a digold complex offers more congruent predicted kinetics. The lens of quantum chemistry was turned upon palladium-mediated methoxycarbonylation reactions. An extensive study was undertaken to attempt to understand the bidentate diphosphine ligand dependency on forming either methylpropanoate (MePro) or copolymers. Mechanisms currently suggested in literature are shown to be incongruous with the formation of MePro by Pd(OAc)₂ and bulky diphosphines. A possible alternative route is proposed in this thesis. Four mechanisms for methoxycarbonylation with Pd(2-PyPPh₂)ₙ are detailed. The most accessible route is found to be congruent with experimental reports of selectivity, acid dependency and slight steric modifications. A modification of 2-PyPPh₂ to 2-(4-NMe₂-6-Me)PyPPh₂ is shown to improve both selectivity and turnover, the latter by four orders of magnitude (highest transition state from 22.9 kcal/mol to 16.7 kcal/mol ∆G), and this new second generation in silico designed ligand is studied for its applicability to wider substrate scope and different solvents. The final chapter of this thesis is a mixed quantum mechanics and molecular mechanics (QM/MM) examination of an enzymatic reaction, discussing the need for certain conditions and the role of particular amino acid residues in an S[sub]N2 hydrolysis reaction.

Made available in DSpace on 2017-07-24T19:37:53Z (GMT). No. of bitstreams: 1 Luis Lacerda.pdf: 6157407 bytes, checksum: 67f47ee9ce5d908521ba3d0455add580 (MD5) Previous issue date: 2015-03-09 / Coordenação de Aperfeiçoamento de Pessoal de Nível Superior / Semiconductors materials are largely employed on development of innumerous optical and electronic due to their electronic, optical, ferroelectric and structural properties. Among the semiconductors materials stand out the zinc oxide (ZnO) and the barium titanate (BaTiO3) once shows excellent properties allied to low cost to obtaining. The ZnO is a simple oxide used in technology and largely investigated as an alternative to replace high cost material on development of electronic devices. Similarly, the BaTiO3 has perovskite crystalline structure whose properties present great technological interest. This work evaluated the effect of Ba presence on wurtzite structure and the influence of Zn atoms on tetragonal BaTiO3 properties. The obtained results indicates that the Ba atoms changes drastically the band structure of ZnO, resulting in the decrease of band gap for low quantities and the semiconductor type modification for doping above 25 %. The insertion of such atoms in wurtzite also causes the improvement of ferroelectric properties and the increase of unit cell lattice parameters. In case of Zn-doped BaTiO3, the doping process reduces radically de band gap and the ferroelectric properties regarding to pure material. Likewise, the semiconductor type is also modified by the Zn atoms presence. Based on obtained results for both crystalline systems, was proposed their employed in formation of p-n heterojunction. The heterostructure was evaluated through of four models. The obtained results for each one of these models were used to describe the interface region of ZnO/BaTiO3 heterojunction, proving that the atoms intercalation occurs and is responsible for heterostructure properties. Such properties present this heterostructure as a potential alternative for development of electronic devices, mainly the development of memory devices. The obtained heterostructure requires a low amount energy to electronic conduction process and shows high compatibility between the structure of heterojunction and the SiO2 substrate which is used in development of such devices. / Materiais semicondutores são amplamente empregados no desenvolvimento de vários dispositivos ópticos e eletrônicos variados devido às suas propriedades eletrônicas, ópticas, ferroelétricas e estruturais. Dentre os materiais semicondutores, destacam-se o óxido de zinco (ZnO) e o Titanato de Bário (BaTiO3) uma vez que apresentam excelentes propriedades aliadas ao baixo custo de síntese. O ZnO é um óxido simples amplamente empregado na tecnologia e largamente investigado como uma alternativa para substituição de materiais de custo elevado no desenvolvimento de dispositivos eletrônicos. Por sua vez, o BaTiO3 é um material de estrutura cristalina perovskita cujas propriedades são de grande interesse tecnológico. No presente trabalho avaliou-se o efeito da presença de átomos de Ba na estrutura wurtzita do ZnO e a influência dos átomos de Zn sobre as propriedades do BaTiO3 tetragonal. Os resultados indicaram que os átomos de bário alteram drasticamente a estrutura de bandas do ZnO, resultando na diminuição do band gap para pequenas quantidades e a modificação do tipo de semicondutor para dopagens superiores a 25%. A inserção de tais átomos na estrutura wurtzita também é responsável pelo aprimoramento das propriedades ferroelétricas do material, bem como pelo aumento dos parâmetros de rede da célula unitária. No caso da estrutura do BaTiO3 dopada com Zn observou-se a redução drástica do band gap para o material e a modificação do caráter semicondutor do material; entretanto, ocorreu a redução das propriedades ferroelétricas em relação ao BaTiO3 puro. Com base nos resultados obtidos para ambos os sistemas cristalinos, propôs-se a sua utilização para formação de uma heterojunção do tipo p-n. A heteroestrutura foi avaliada por meio de quatro modelos diferentes. Os resultados obtidos para cada um destes modelos foram utilizados para descrição da estrutura eletrônica da região de interface da heterojunção, comprovando que a intercalação de átomos na interface é observada e mostra-se responsável pelas propriedades observadas para a heteroestrutura. Tais propriedades apontam a heterojunção ZnO/BaTiO3 como uma alternativa em potencial para aplicação no desenvolvimento de dispositivos eletrônicos e, principalmente, no desenvolvimento de dispositivos de armazenamento de dados, devido a diminuição de energia necessária para condução eletrônica.

Solubility prediction usually refers to prediction of the intrinsic aqueous solubility, which is the concentration of an unionised molecule in a saturated aqueous solution at thermodynamic equilibrium at a given temperature. Solubility is determined by structural and energetic components emanating from solid-phase structure and packing interactions, solute–solvent interactions, and structural reorganisation in solution. An overview of the most commonly used methods for solubility prediction is given in Chapter 1. In this thesis, we investigate various approaches to solubility prediction and solvation model development, based on informatics and incorporation of empirical and experimental data. These are of a knowledge-based nature, and specifically incorporate information from the Cambridge Structural Database (CSD). A common problem for solubility prediction is the computational cost associated with accurate models. This issue is usually addressed by use of machine learning and regression models, such as the General Solubility Equation (GSE). These types of models are investigated and discussed in Chapter 3, where we evaluate the reliability of the GSE for a set of structures covering a large area of chemical space. We find that molecular descriptors relating to specific atom or functional group counts in the solute molecule almost always appear in improved regression models. In accordance with the findings of Chapter 3, in Chapter 4 we investigate whether radial distribution functions (RDFs) calculated for atoms (defined according to their immediate chemical environment) with water from organic hydrate crystal structures may give a good indication of interactions applicable to the solution phase, and justify this by comparison of our own RDFs to neutron diffraction data for water and ice. We then apply our RDFs to the theory of the Reference Interaction Site Model (RISM) in Chapter 5, and produce novel models for the calculation of Hydration Free Energies (HFEs).