New methods of multiscale chemical space analysis : visualization of structure-activity relationships and structural pattern extraction
Identifieur interne : 000108 ( Hal/Corpus ); précédent : 000107; suivant : 000109New methods of multiscale chemical space analysis : visualization of structure-activity relationships and structural pattern extraction
Auteurs : Shilva KayasthaSource :
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Abstract
This thesis presents studies devoted to aid in systematic analysis of chemical spaces, focusing on mining and visualization of structure-activity relationships (SARs). It reports some new analysis protocols, combining both existing and on-purpose developed novel methodology to address both large-scale and local SAR analysis. Large-scale analysis featured both generative topographic mapping (GTM)-based extraction of privileged structural motifs and scaffold analysis. GTM was combined with chemical space network (CSN) to develop a visualization tool providing global-local views of SAR in large data sets. We also introduce star coordinates (STC) to visualize multi-property space and prioritize drug-like subspaces. Local SAR monitoring includes new strategies to predict activity cliffs using support vector machine models and a study of structural modifications on ionization state of compounds. The SAR matrix methodology was applied to objectively evaluate SAR progression during lead optimization.
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<front><div type="abstract" xml:lang="en"> <p>This thesis presents studies devoted to aid in systematic analysis of chemical spaces, focusing on mining and visualization of structure-activity relationships (SARs). It reports some new analysis protocols, combining both existing and on-purpose developed novel methodology to address both large-scale and local SAR analysis. Large-scale analysis featured both generative topographic mapping (GTM)-based extraction of privileged structural motifs and scaffold analysis. GTM was combined with chemical space network (CSN) to develop a visualization tool providing global-local views of SAR in large data sets. We also introduce star coordinates (STC) to visualize multi-property space and prioritize drug-like subspaces. Local SAR monitoring includes new strategies to predict activity cliffs using support vector machine models and a study of structural modifications on ionization state of compounds. The SAR matrix methodology was applied to objectively evaluate SAR progression during lead optimization.</p>
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<abstract xml:lang="en"> <p>This thesis presents studies devoted to aid in systematic analysis of chemical spaces, focusing on mining and visualization of structure-activity relationships (SARs). It reports some new analysis protocols, combining both existing and on-purpose developed novel methodology to address both large-scale and local SAR analysis. Large-scale analysis featured both generative topographic mapping (GTM)-based extraction of privileged structural motifs and scaffold analysis. GTM was combined with chemical space network (CSN) to develop a visualization tool providing global-local views of SAR in large data sets. We also introduce star coordinates (STC) to visualize multi-property space and prioritize drug-like subspaces. Local SAR monitoring includes new strategies to predict activity cliffs using support vector machine models and a study of structural modifications on ionization state of compounds. The SAR matrix methodology was applied to objectively evaluate SAR progression during lead optimization.</p>
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<abstract xml:lang="fr"> <p>Cette thèse est dédiée à l’analyse systématique de l’espace chimique, et des relations structure-activité (SAR) en particulier. L’ouvrage présente des nouveaux protocoles d’analyse combinant des méthodes classiques et originales, dans le but d’analyser les SAR à l’échelle globale ainsi que locale. L’analyse globale des espaces chimiques repose sur la recherche des motifs structuraux privilégiés par cartographie topographique générative (GTM), ainsi que par analyse classique des « châssis » moléculaires. La cartographie a été ensuite couplée avec l’analyse de réseaux chimiques (CSN), permettant une transition de la vue globale vers l’analyse locale de SAR. L’optimisation mutiobjectif des propriétés de potentiels médicaments a été adressé par la méthode « star coordinates ». L’analyse locale des SAR inclut des nouvelles stratégies pour prédire les discontinuités dans le paysage structure-activité biologique, et une étude de l’impact de la structure sur l’ionisation des molécules. Des matrices SAR ont servi pour monitorer le progrès dans l’optimisation de nouveaux principes actifs.</p>
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