New methods of multiscale chemical space analysis : visualization of structure-activity relationships and structural pattern extraction
Identifieur interne : 000D30 ( Main/Exploration ); précédent : 000D29; suivant : 000D31New methods of multiscale chemical space analysis : visualization of structure-activity relationships and structural pattern extraction
Auteurs : Shilva Kayastha [France]Source :
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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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