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Quantitative Structure-Activity Relationship, Activity Prediction, and Molecular Dynamics of Non-nucleotide Reverse Transcriptase Inhibitors
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1AstraZeneca R&D Mölndal, Pepparedsleden 1, Mölndal, 431 83 Sweden. Andy.davis@astrazeneca.com
Molecular Pharmaceutics
|January 16, 2013
Summary
Medicinal chemistry is shifting towards "quality at the point of design" to reduce drug development attrition. AstraZeneca
Area of Science:
- Medicinal Chemistry
- Drug Development
- Computational Chemistry
Background:
- The pharmaceutical industry faces significant changes, with high attrition rates during drug development being a major challenge.
- Emerging definitions of
- compound quality
- focus on retrospective analysis of developmental attrition, prompting a shift towards
- quality at the point of design
- in medicinal chemistry.
Purpose of the Study:
- To explore the impact of automation in quantitative structure-activity relationship (QSAR) model building and validation.
- To demonstrate how an informatics system can enhance predictive power for project design teams.
Main Methods:
- Description of AstraZeneca's experience with automated QSAR model building and validation.
- Implementation of an informatics system to support predictive modeling.
Main Results:
- Empirical QSAR models, when carefully constructed, offer predictive control but require improvements in accuracy and precision.
- Automation and informatics systems can significantly enhance the predictive capabilities of QSAR models.
Conclusions:
- The paradigm shift to
- quality at the point of design
- necessitates improvements in predictive model quality.
- Automated QSAR model building and validation through informatics systems offer a step-change in predictive power for drug design.
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