Le nombre considérable d'interactions protéine-protéine (PPIs) existant au sein d'un organisme, ainsi que leur implication cruciale dans la vie cellulaire et dans de nombreuses pathologies, font des PPIs un immense réservoir de cibles potentielles pour la recherche de médicaments. Les PPIs sont aujourd'hui sur le devant de la scène grâce au développement de méthodologies innovantes et la validation récente de molécules chimiques modulant ces interactions dans des essais précliniques.L'étude des modulateurs d'interactions protéine-protéine (PPIM), a des implications tant dans la recherche fondamentale que thérapeutique. Les PPIMs peuvent aider à la compréhension des réseaux d'interactions. Elles permettront également de faire émerger de nouvelles familles d'agents thérapeutiques actifs dans diverses pathologies.Mon travail de thèse a principalement porté sur deux aspects de l'étude de l'inhibition des PPIs. D'une part, l'étude de l'implication des divers paramètres physicochimiques gouvernant une PPI dans sa capacité à être modulée (étude dite de la « druggabilité »), m'a amené à participer à la création d'une base de données structurale des interactions protéine-protéine : 2P2IDB (http://2p2idb.cnrs-mrs.fr/). D'autre part, j'ai contribué à l analyse de l'espace chimique des molécules présentes dans la base de données 2P2IDB. Nous avons défini la « Rule Of 4 » comme ligne de conduite pour caractériser ces molécules. Nous avons de plus utilisé le SVM afin de créer un protocole innovant (2P2IHUNTER) qui nous a permis de filtrer de grandes collections de composés afin de créer des chimiothèques dédiées aux PPIs. / The number of protein-protein interactions (PPIs) existing in an organism, and their crucial implication in cellular life and in many pathologies, demonstrates the importance of PPIs as a large reservoir of potential targets for medicinal research. Neglected for a long time by both pharmaceutical companies and academic laboratories because they were historically classified as difficult targets, PPIs are now getting into the groove due to the development of innovative methodologies and the growing number of small molecule compounds modulating these interactions.The study of PPI modulators has implications in both fundamental and therapeutics research. On the one hand, PPI modulators can be used in basic research to decipher the role of PPIs in biological networks. On the other hand, they represent a valuable source of new families of therapeutic agents in pathologic processes.In the first part of my PhD, I contributed to the development of a structural database dedicated to protein-protein interactions: 2P2IDB (http://2p2idb.cnrs-mrs.fr/). The interface descriptors of protein-protein interfaces which are typical of complexes present in 2P2IDB have been used to develop a qualitative scoring function to assess the ‘druggability' of PPI targets.In the second part of my PhD, I contributed to the analysis of the chemical space of PPI inhibitors present in the 2P2I database using chemoinformatics tools. We defined the ‘Rule-of-4' as a guideline to characterize these compounds. We have used support vector machine approaches to elaborate a protocol: 2P2IHUNTER, which allows filtering large collection of compounds to design chemical libraries dedicated to PPI targets.

Le traitement quantique de l'information est un domaine très actif dont les enjeux sont importants tant d'un point de vue du savoir scientifique fondamental que des applications technologiques. Dans ce contexte le concept de bus de spin consiste à employer en tandem des spins électroniques et nucléaires. Les électrons célibataires servent de tête de lecture et d'écriture sur le registre de bits quantiques constitué par les spins nucléaires. Les électrons sont délocalisés sur un ensemble de spins nucléaires dont les temps de décohérences doivent être longs. Dans ce travail nous avons étudié un ion titane (III) dans l'oxyde de gallium dont nous avons synthétisé et étudié des monocristaux. Une étude approfondie par RPE et ENDOR en onde continue a montré que l'électron porté par le titane était en interaction avec huit noyaux de gallium qui constituent le registre de qubits potentiel. L'étude a également révélé un effet isotopique sur les interactions noyau-noyau véhiculées par l'électron. Lorsque les deux noyaux de gallium entourant le titane sont identiques (mêmes isotopes), cette interaction est d'un ordre de grandeur plus grande que dans le cas d'isotopes différents, un effet qui peut être employé afin de réduire la durée des opérations logiques. Enfin, la dynamique de cet ensemble de spin a été caractérisée par RPE et ENDOR en impulsions. Il s'avère que la décohérence électronique est dominée par des phénomènes de diffusion instantanée et de diffusion spectrale. La dynamique des spins nucléaires les expériences menées permettent de déterminer l'ordre de grandeur des temps de relaxation longitudinaux et de décohérence des spins nucléaires.

New experimental strategies and algorithmic approaches were devised and tested to improve the analysis of pharmaceutically relevant materials. These new methods were developed to address key bottlenecks in the design of amorphous solid dispersions for the delivery of low-solubility active pharmaceutical ingredients in the final dosage forms exhibiting high bioavailability. <br>

This thesis studies, implements and experiments with specific application-oriented approaches for exploring and querying multi-dimensional datasets. The first part of the thesis scrutinizes indexing of the complex space of chemical compounds, and details a design of high-performance retrieval system for small molecules. The resulting system is then utilized within a wider context of federated search in heterogeneous data and metadata related to the chemical datasets. In the second part, the thesis focuses on fast visualization and exploration of many-dimensional data that originate from single- cell cytometry. Self-organizing maps are used to derive fast methods for analysis of the datasets, and used as a base for a novel data visualization algorithm. Finally, a similar approach is utilized for highly interactive exploration of multimedia datasets. The main contributions of the thesis comprise the advancement in optimization and methods for querying the chemical data implemented in the Sachem database cartridge, the federated, SPARQL-based interface to Sachem that provides the heterogeneous search support, dimensionality reduction algorithm EmbedSOM, design and implementation of the specific EmbedSOM-backed analysis tool for flow and mass cytometry, and design and implementation of the multimedia...

<div>Deep neural networks (DNNs) have gained tremendous attention over the recent years due to their outstanding performance in solving many problems in different fields of science and technology. Currently, this field is of interest to many researchers and growing rapidly. The ability of DNNs to learn new concepts with minimal instructions facilitates applying current DNN-based methods to new problems. Here in this dissertation, three methods based on DNNs are discussed, tackling different problems in the field of computer-aided drug design.</div><div><br></div><div>The first method described addresses the problem of prediction of hydration properties from 3D structures of proteins without requiring molecular dynamics simulations. Water plays a major role in protein-ligand interactions and identifying (de)solvation contributions of water molecules can assist drug design. Two different model architectures are presented for the prediction the hydration information of proteins. The performance of the methods are compared with other conventional methods and experimental data. In addition, their applications in ligand optimization and pose prediction is shown.</div><div><br></div><div>The design of de novo molecules has always been of interest in the field of drug discovery. The second method describes a generative model that learns to derive features from protein sequences to design de novo compounds. We show how the model can be used to generate molecules similar to the known for the targets the model have not seen before and compare with benchmark generative models.</div><div><br></div><div>Finally, it is demonstrated how DNNs can learn to predict secondary structure propensity values derived from NMR ensembles. Secondary structure propensities are important in identifying flexible regions in proteins. Protein flexibility has a major role in drug-protein binding, and identifying such regions can assist in development of methods for ligand binding prediction. The prediction performance of the method is shown for several proteins with two or more known secondary structure conformations.</div>

Molecular property prediction has the ability to improve many processes in molecular chemistry industry. One important application is the development of new drugs where molecular property prediction can decrease both the cost and time of finding new drugs. The current trend is to use graph neural networks or transformers which tend to need moderate and large amounts of data respectively to perform well. Because of the scarceness of molecular property data it is of great interest to find an effective method to transfer learning from other more data-abundant problems. In this thesis I present an approach to pre-train transformer encoders on reaction prediction in order to improve performance on downstream molecular property prediction tasks. I have built a model based on the full transformer architecture but modify it for the purpose of pre-training the encoder. Model performance and specifically the effect of pre-training is tested by predicting lipophilicity, HIV inhibition and hERG channel blocking using both pre-trained models and models without any pre-training. The results demonstrate a tendency for improvement of performance on all molecular property prediction tasks using the suggested pre-training but this tendency for improvement is not statistically significant. The major limitation with the conclusive evaluation stems from the limited simulations due to computational constraints

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Measurement science has seen fast growth of data in both volume and complexity in recent years, new algorithms and methodologies have been developed to aid the decision<br>making in measurement sciences, and this process is automated for the liberation of labor. In light of the adversarial approaches shown in digital image processing, Chapter 2 demonstrate how the same attack is possible with spectroscopic data. Chapter 3 takes the question presented in Chapter 2 and optimized the classifier through an iterative approach. The optimized LDA was cross-validated and compared with other standard chemometrics methods, the application was extended to bi-distribution mineral Raman data. Chapter 4 focused on a novel Artificial Neural Network structure design with diffusion measurements; the architecture was tested both with simulated dataset and experimental dataset. Chapter 5 presents the construction of a novel infrared hyperspectral microscope for complex chemical compound classification, with detailed discussion in the segmentation of the images and choice of a classifier to choose.<br>

The BioChemical Library (BCL) cheminformatics toolkit is an application-based academic open-source software package designed to integrate traditional small molecule cheminformatics tools with machine learning-based quantitative structure-activity/ property relationship (QSAR/QSPR) modeling. In this pedagogical article we provide a detailed introduction to core BCL cheminformatics functionality, showing how traditional tasks (e.g., computing chemical properties, estimating druglikeness) can be readily combined with machine learning. In addition, we have included multiple examples covering areas of advanced use, such as reaction-based library design. We anticipate that this manuscript will be a valuable resource for researchers in computer-aided drug discovery looking to integrate modular cheminformatics and machine learning tools into their pipelines.