Please use this identifier to cite or link to this item: http://scholarbank.nus.edu.sg/handle/10635/36328
Title: Multi-target selection and high throughput quantitative structure-activity relationship model development
Authors: LIU XIN
Keywords: pathway cross-talk, multi-target, QSAR, virtual screening, support vector regression, machine learning
Issue Date: 6-Aug-2012
Source: LIU XIN (2012-08-06). Multi-target selection and high throughput quantitative structure-activity relationship model development. ScholarBank@NUS Repository.
Abstract: Drugs designed to act against individual molecular targets cannot usually combat multigenic diseases such as cancers in which alternative or compensatory pathways are often activated. Thus selection of proper multi-target combinations and prediction of new molecules against these selected multiple targets are highly useful for discovering drugs with improved therapeutic efficacies by collective regulations of primary therapeutic targets, compensatory signaling and drug resistance mechanisms. Cross-talk between pathways plays important regulatory roles in biological processes, disease processes, and therapeutic responses. Knowledge of these cross-talks is highly useful for facilitating systems level analysis of diseases, biological processes and the mechanisms of multi-targeting drugs and drug combinations. However, to our best knowledge, currently no such database exists providing this kind of information. In this work, a Pathway Cross-talk Database (PCD) is developed providing information about experimentally discovered cross-talks between pathways and their relevance to diseases and biological processes thus facilitating multi-target selection. Based on some entries stored in PCD, four combinations of anticancer kinase targets, EGFR-VEGFR, EGFR-Src, EGFR-PDGFR and EGFR-FGFR were selected as illustration and for further study. In silico methods have been extensively explored for the discovery of multi-target drugs. Apart from drug lead optimization, predictive quantitative structure-activity relationship (QSAR) models with well-defined applicability domains (ADs) have shown promising capability in virtual screening (VS) large chemical databases for novel drug hits. Despite the good hit rates and activity assessment these QSAR models can achieve, however, these models cannot find highly novel actives outside similarity-based ADs. One possible reason is that ADs may only contain limited spectrum of active compounds. Another possible reason lies in the limited scaffold hopping ability of the molecular descriptors, i.e. the chosen molecular descriptors may not be able to fully represent and identify molecules with similar properties yet different or novel scaffolds. Thus, an extended QSAR approach is needed aimed at finding highly novel inhibitors without compromising hit rates within similarity-based ADs. In this work, new MLR QSAR models are constructed via chemspace-wide activity regression and tested on DHFR, ACE and Cox2 inhibitors, and further applied for searching for dual inhibitors of the four combinations of anticancer kinase targets, EGFR-VEGFR, EGFR-PDGFR, EGFR-FGFR and EGFR-Src. The results show our consensus SVR QSAR models yield equivalent predictive accuracy for newly discovered chemicals and improved hit-rates and enrichment factors in identifying inhibitors from large chemical databases. In particular, our method also shows some level of capability in the identification and activity assessment of highly novel inhibitors outside similarity-based ADs.
URI: http://scholarbank.nus.edu.sg/handle/10635/36328
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