Protein-protein interactions are central to most biological processes. Although in the field of drug discovery there is a great interest in targeting protein-protein interactions, the discovery and development of small-molecules, which effect these interactions has been challenging. The purpose of this project is to determine if in situ click chemistry is a practical approach towards testing whether Bcl-XL is capable of assembling it's own inhibitory compounds. Abbott laboratories developed compound ABT-737, which binds with high affinity (Ki < 1 nM) to the binding sites of Bcl-XL.³⁶ Based on ABT-737, two acetylene anchor molecules AM3 and AM4 have been synthesized. These anchor molecules are distinguished by the reactivity of the their carbon-carbon triple bond. Compound AM3 contains an electron withdrawing carbonyl in the alpha-position to the acetylene resulting in an activating effect towards the [1,3]-dipolar cycloaddition compared to compound AM4. To determine the reactivity of the activated system, ¹ H-NMR kinetic studies were performed to compare the relative rates of these two systems by reacting model alkynes 1,2,3, and 4 with azide AZ7. It was shown that the activated systems, 1 and 3, produce triazoles in an accelerated rate compared to the unactivated systems 2 and 3. To test for the self-assembly of inhibitory triazoles, the acetylenes AM3 and AM4 were incubated with Bcl-XL and 14 azide building blocks (AZ1-AZ12) for 12 hours at 37 degrees C. Subjecting these mixtures to LC/MS-SIM led to the discovery of two hit compounds, 35 and 36, of which 35 has been chemically synthesized confirming the hit. Future work includes the synthesis of all hit compounds. Since hit triazoles can be syn or anti, both need to be synthesized for each hit to investigate which regioisomer Bcl-XL generates. Tests to confirm if hit compounds are actually modulating Bcl-XL activity will be done using conventional bio-assays. This will validate that Bcl-XL is capable of assembling its own inhibitor via the in situ click chemistry approach to drug discovery.

<p>Program synthesis aims to generate programs automatically from user-provided specifications and has the potential to aid users in real-world programming tasks from different domains. Although there have been great achievements of synthesis techniques in specific domains such as spreadsheet programming, computer-aided education and software engineering, there still exist huge barriers that keep us from achieving scalable and practical synthesis tools.</p> <p><br></p> <p>This dissertation presents several techniques towards more scalable and practical program synthesis from three perspectives: 1) intention: Writing formal specification for synthesis is a major barrier for average programmers. In particular, in some quantitative synthesis scenarios (such as network design), the first challenge faced by users is expressing their optimization targets. To address this problem, we present comparative synthesis, an interactive synthesis framework that learns near optimal programs through comparative queries, without explicitly specified optimization targets. 2) invention: Synthesis algorithms are key to pushing the performance limit of program synthesis. Aiming to solve syntax-guided synthesis problems efficiently, we introduce a cooperative synthesis technique that combines the merits of enumerative and deductive synthesis. 3) adaptation: Besides functional correctness, quality of generated code is another important aspect. Towards automated provably-correct optimization over tree traversals, we propose a stack-based representation for iterations in tree traversals and an encoding to Monadic Second-Order logic over trees, which enables reasoning about tree traversal transformations which were not possible before.</p>

La synthèse contrôlée par la cible sous contrôle cinétique (Kinetic Target-Guided Synthesis, KTGS) est une approche relativement peu explorée, alternative à la chimie combinatoire traditionnelle,dans laquelle la protéine cible participe à la synthèse du ou de ses propres ligands. Ainsi, les travaux présentés dans la première partie de cette thèse ont pour principal objectif d'élargir l'éventail des réactions actuellement disponibles en KTGS grâce à la réaction d'aldolisation voire d'amidation, et ce en utilisant la β-sécrétase (BACE-1) comme cible biologique, qui est une enzyme étroitement impliquée dans la maladie d'Alzheimer. La seconde partie de cette thèse a été consacrée à la synthèse de marqueurs de masse fluorescents bioconjugables basés sur l'association d'un noyau coumarinique et d'une fonction phosphonium. Les deux générations présentées dans ce manuscrit ont entre autre permis de synthétiser une sonde FRET permettant de détecter l'activité enzymatique de la BACE-1, qui pourrait par ailleurs être un outil intéressant pour l'analyse des bruts réactionnels des réactions de click in situ,et diminuer les quantités d'enzyme engagées dans ces expériences. Enfin dans la dernière partie de cette thèse nous décrivons la mise au point de nouvelles réactions de conjugaison bio-orthogonale pour le marquage de molécules comportant une fonction aldéhyde. Nous avons ainsi développé d'une part une réaction trois composants via une séquence de condensation/Mannich/lactamisation et d'autre part une réaction d'oléfination de Wittig. / The kinetic target-guided synthesis (KTGS), is an underexplored alternative approach to combinatorial chemistry, in which the biological target is able to assemble its own inhibitors from a pool of fragments. Thus, the first part of this thesis aimed at extending the scope of the reactions available for the KTGS, by investigating the aldolisation and amidation reaction, using the β-secretase (BACE-1) as biological target, which is an enzyme narrowly involved in the Alzheimer's disease. The second part of this thesis was dedicated to the synthesis of bioconjagatable fluorophores containing a phosphonium group as mass tag associated to a coumarin core. Both generations presented in this manuscript allowed us, among other things, to synthesize a FRET probe that proved suitable for the determination of BACE-1 enzymatic activity. The utility of such a fluorogenic tool could be leveraged to facilitate the analysis of crude mixtures obtained during KTGS experiments, and lessen the amount of enzyme required in these experiments. Finally, in the last part of this thesis, we describe the development of two new bioorthogonal reactions allowing the selective labeling of molecules containing an aldehyde moiety : 1) a three component reaction involving a condensation/Mannich/lactamisation procedure, between an amine, an aldehyde and an enol partner; 2) a Wittig ligation between an aldehyde and a phosphonium bearing an active methylene.

In recent years, texture synthesis has been a heated topic in computer graphics, and the development of advanced algorithms for generating high-quality 3D textures is an area of active research. A recently proposed model, Neural Cellular Automata, can synthesize realistic 2D texture images or videos. However, due to the complexity and non-differentiable nature of 3D rendering and the lack of definition of the neighborhood on 3D mesh objects, no one has extended the 2D Neural Cellular Automata to the 3D scenario. In this master’s thesis, we propose a novel method for modeling the neighborhood relationship on 3D mesh objects, drawing inspiration from a graph variant of the Neural Cellular Automata. We also design an end-to-end 3D texture synthesis pipeline, leveraging a differentiable renderer to enable the Graph Neural Cellular Automata to learn to synthesize desired 3D textures. Our method allows users to either give the text description of the target textures or present the target texture images as the objectives. We evaluate the effectiveness of our proposed method both qualitatively and quantitatively, comparing it with the state-of-the-art method to demonstrate that it achieves comparable or better results. Furthermore, we explore the homology between the graph variant of Neural Cellular Automata and the 2D model, examining whether our proposed model preserves critical properties of the 2D model such as zero-shot generalization and self-regeneration. Finally, we analyze the limitations and potential drawbacks of our proposed method and suggest directions for future research. In summary, this thesis proposes a novel approach to synthesizing high-quality 3D textures using the Graph Neural Cellular Automata model and a differentiable renderer. Our work provides a foundation for future research in this area, and we believe that our findings will contribute to the development of advanced algorithms for 3D texture synthesis. / Under de senaste åren har textursyntes varit ett hett ämne inom datorgrafik, och utvecklingen av avancerade algoritmer för att generera högkvalitativa 3D-texturer är ett aktivt forskningsområde. En nyligen föreslagen modell, Neural Cellular Automata, kan syntetisera realistiska 2D-texturbilder eller videor. Dock, på grund av komplexiteten och den icke-differentierbara naturen av 3D-rendering och bristen på definition av grannskapet på 3D-meshobjekt, har ingen utvidgat 2D Neural Cellular Automata till 3D-scenariot. I den här masteruppsatsen föreslår vi en ny metod för att modellera grannskapsrelationen på 3D-meshobjekt, inspirerade av en grafvariant av Neural Cellular Automata. Vi utformar också en ände-till-ände 3D-textursyntes pipeline, genom att utnyttja en differentierbar renderer för att möjliggöra för Graph Neural Cellular Automata att lära sig syntetisera önskade 3D-texturer. Vår metod tillåter användare att antingen ge textbeskrivningen av måltexturerna eller presentera måltexturbilderna som målen. Vi utvärderar effektiviteten av vår föreslagna metod både kvalitativt och kvantitativt, jämför den med den mest avancerade metoden för att visa att den uppnår jämförbara eller bättre resultat. Dessutom utforskar vi homologin mellan grafvarianten av Neural Cellular Automata och 2D-modellen, undersöker om vår föreslagna modell bevarar kritiska egenskaper hos 2D-modellen som zero-shot generalisering och självregenerering. Slutligen analyserar vi begränsningarna och eventuella nackdelar med vår föreslagna metod och föreslår riktningar för framtida forskning. Sammanfattningsvis föreslår denna avhandling en ny metod för att syntetisera högkvalitativa 3D-texturer med hjälp av Graph Neural Cellular Automata-modellen och en differentierbar renderer. Vårt arbete ger en grund för framtida forskning inom detta område, och vi tror att våra fynd kommer att bidra till utvecklingen av avancerade algoritmer för 3D-textursyntes.