HOME- Bryn Mawr Conference
- Workshops & Training
- 2010 Oxford (Discovery)
- 2010 Oxford (ADMET)
- 2009 Oxford (Discovery)
- 2009 Oxford (ADMET)
- Bassan, A
- Cronin, M
- Hardy, B
- Helma, C
- Hopfinger, T
- Judson, P
- Leahy, D
- Madden, J
- Michielan, L
- Narayanan, D
- Myatt, G
- Obrezanova, O
- Thomas, S
- Zamora, I
- Poster Session
- Bursary Award
- 2008 Oxford
- 2006 Oxford
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Dr. Olga Obrezanova graduated from Rostov University in Russia and obtained her Ph.D. in Applied Mathematics from the same university in 1995. She then worked for four years as an Assistant Professor at Rostov University researching underwater acoustics. Between 1999 and 2003, Olga worked as a Research Associate at the University of Cambridge. Her research involved investigation into crack propagation in solid materials and fracture mechanics.
In 2005 Olga joined Inpharmatica, a drug discovery company that merged with BioFocus DPI at the end of 2006. In 2009 Olga joined Optibrium which was founded as a spin-out from BioFocus DPI. Her work has been focused on applying statistical modeling and machine learning methods to problems in drug discovery. In particular, Olga has been developing and using new computational techniques to build QSAR models of ADME properties. Most recently, Olga’s key research has been around algorithms enabling automatic model generation. Olga has been working as part of a team developing a commercial, decision making, software platform for compound design, optimization and prioritization (StarDrop) where she has led the research and design process for the “Auto-Modeler” tool. She is based in Cambridge, UK.
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ADME QSAR Modelling to Guide Drug Design
Olga Obrezanova (Optibrium)
The importance of optimizing ADME properties of potential drug molecules is now widely recognized. In silico predictive modelling offers a possibility to consider the likely ADME properties of a molecule before it is even synthesized. This workshop will focus on building predictive models and using them to guide drug design. The participants will build a QSAR/QSPR model of a property using an algorithm for automatic model generation based on Gaussian Processes, a powerful ‘machine learning’ technique. The predictions from this model will be used alongside other compound data, including in vitro measurements and in silico predictions from other ADME QSAR models, to identify compounds with a balance of appropriate ADME and potency. Using a ‘probabilistic’ scoring algorithm, the participants will be able to prioritise and select compounds most likely to meet the project criteria. The participants will see how a predictive model coupled with a visualisation tool, which provides a link between compound structure and predicted property values, can help to guide the redesign of compounds to overcome liabilities.
The workshop will be based on the StarDrop™ software platform which helps to guide decisions for compound optimization, design and prioritisation. The participants will have access to different functionalities within StarDrop: a suite of predictive ADME QSAR models, a model building tool (Auto-Modeler), a visualization tool (Glowing Molecule), and the unique probabilistic scoring algorithm which is able to rapidly integrate all the compound data, predicted and experimental, to prioritize compounds with the best balance of properties.
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