Use this URL to cite or link to this record in EThOS: http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.686275
Title: Development of a logic-based approach for the design of low molecular weight regulators of biological activity
Author: Reynolds, Christopher Robert
ISNI:       0000 0004 5918 3614
Awarding Body: Imperial College London
Current Institution: Imperial College London
Date of Award: 2014
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Abstract:
This project developed, assessed and improved an existing virtual screening technology; the Investigational Novel Drug Discovery by Example (INDDEx) package; and uses the technology to discover new drug hits and leads. INDDEx performs ligand-based virtual screening: learning from molecules with known activity and fragmenting them into substructural elements. A model is built based on logical rules defining required distances between elements of substructure. In the optimisation phase of this project, the program's speed was increased, a support vector machine method was implemented and a graphical output for the generated rules was added. In the investigation and screening phase, INDDEx screened the ZINC database to find new inhibitors for SIRT2, a poorly investigated target with few known inhibitors. The top ranked molecules were docked with GOLD to produce a consensus score. A new molecule was found with 50% inhibitory concentration of 0.67μm against SIRT2. In the assessment phase, the performance of INDDEx as a virtual screening tool was assessed by benchmarking it on the DUD database and comparing it with the performances of eHiTS LASSO, PharmaGist and DOCK. INDDEx gave 1% Enrichment Factors of 69.2, 82.7 and 90.4, and 0.1% Enrichment Factors of 492, 631 and 707, both when learning from 2, 4 and 8 active ligands. A strength of INDDEx is its scaffold-hopping ability. Scaffold-hopping is important for developing new drug leads, and is regarded as a challenge. Considering only ligands structurally dissimilar to ones in the benchmarking learning data, INDDEx gave 1% Enrichment Factors of 52.3, 63.6 and 66.9 when learning from 2, 4 and 8 active ligands. In the improvement phase, an algorithm was implemented to take hits found by INDDEx and explore the synthetic space resulting from synthetic modification of those hits. This was realised using a library of virtual reactions and a method of predicting which molecules have the most potential for modification. Two assessments quantified the additional molecular space made available to search and qualified the new method as a significant improvement on a naïve approach.
Supervisor: Sternberg, Michael ; Muggleton, Stephen Sponsor: Biotechnology and Biological Sciences Research Council ; Equinox Pharma Limited
Qualification Name: Thesis (Ph.D.) Qualification Level: Doctoral
EThOS ID: uk.bl.ethos.686275  DOI: Not available
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