Cette thèse est consacrée à la cartographie de l’espace chimique ; son but est d’établir les bases d’un outil donnant une vision d’ensemble d’un jeu de données, comprenant prédiction d’activité, visualisation, et comparaison de grandes librairies. Dans cet ouvrage, nous introduisons des modèles prédictifs QSAR (relations quantitatives structure à activité) avec de nouvelles définitions de domaines d’applicabilité, basés sur la méthode GTM (generative topographic mapping), introduite par C. Bishop et al. Une partie de cette thèse concerne l’étude de grandes librairies de composés chimiques grâce à la méthode GTM incrémentale. Nous introduisons également une nouvelle méthode « Stargate GTM », ou S-GTM, permettant de passer de l’espace des descripteurs chimiques à celui des activités et vice versa, appliquée à la prédiction de profils d’activité ou aux QSAR inverses. / This thesis is dedicated to the cartography of chemical space; our goal is to establish the foundations of a tool offering a complete overview of a chemical dataset, including visualization, activity prediction, and comparison of very large datasets. In this work, we introduce new QSAR models (quantitative structure-activity relationship) based on the GTM method (generative topographic mapping), introduced by C. Bishop et al. A part of this thesis is dedicated to the visualization and analysis of large chemical libraries using the incremental version of GTM. We also introduce a new method coined “Stargate GTM” or S-GTM, which allows us to travel from the space of chemical descriptors to activity space and vice versa; this approach was applied to activity profile prediction and inverse QSAR.

Target-based screening projects for bioactive (orphan) compounds have been shown in many cases to be insufficiently predictive for in vivo efficacy, leading to attrition in clinical trials. Phenotypic screening has hence undergone a renaissance in both academia and in the pharmaceutical industry, partly due to this reason. One key shortcoming of this paradigm shift is that the protein targets modulated need to be elucidated subsequently, which is often a costly and time-consuming procedure. In this work, we have explored both improved methods and real-world case studies of how computational methods can help in target elucidation of phenotypic screens. One limitation of previous methods has been the ability to assess the applicability domain of the models, that is, when the assumptions made by a model are fulfilled and which input chemicals are reliably appropriate for the models. Hence, a major focus of this work was to explore methods for calibration of machine learning algorithms using Platt Scaling, Isotonic Regression Scaling and Venn-Abers Predictors, since the probabilities from well calibrated classifiers can be interpreted at a confidence level and predictions specified at an acceptable error rate. Additionally, many current protocols only offer probabilities for affinity, thus another key area for development was to expand the target prediction models with functional prediction (activation or inhibition). This extra level of annotation is important since the activation or inhibition of a target may positively or negatively impact the phenotypic response in a biological system. Furthermore, many existing methods do not utilize the wealth of bioactivity information held for orthologue species. We therefore also focused on an in-depth analysis of orthologue bioactivity data and its relevance and applicability towards expanding compound and target bioactivity space for predictive studies. The realized protocol was trained with 13,918,879 compound-target pairs and comprises 1,651 targets, which has been made available for public use at GitHub. Consequently, the methodology was applied to aid with the target deconvolution of AstraZeneca phenotypic readouts, in particular for the rationalization of cytotoxicity and cytostaticity in the High-Throughput Screening (HTS) collection. Results from this work highlighted which targets are frequently linked to the cytotoxicity and cytostaticity of chemical structures, and provided insight into which compounds to select or remove from the collection for future screening projects. Overall, this project has furthered the field of in silico target deconvolution, by improving the performance and applicability of current protocols and by rationalizing cytotoxicity, which has been shown to influence attrition in clinical trials.