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Related Experiment Video

Updated: May 11, 2026

Optimization of Processing Technology for Tiebangchui with Zanba Based on CRITIC Combined with Box-Behnken Response Surface Method

Optimization of Processing Technology for Tiebangchui with Zanba Based on CRITIC Combined with Box-Behnken Response Surface Method

Published on: May 12, 2023

Analyzing compound and project progress through multi-objective-based compound quality assessment.

J Willem M Nissink¹, Sébastien Degorce

¹AstraZeneca, Oncology Innovative Medicines, Mereside, Alderley Park, Macclesfield, SK10 4TG, UK. willem.nissink@astrazeneca.com

Future Medicinal Chemistry

|May 9, 2013

Summary

This summary is machine-generated.

Drug design teams can now prioritize compounds using a new quality scoring method. This approach transparently handles missing data and tracks progress for better risk management.

Related Experiment Videos

Last Updated: May 11, 2026

Optimization of Processing Technology for Tiebangchui with Zanba Based on CRITIC Combined with Box-Behnken Response Surface Method

Optimization of Processing Technology for Tiebangchui with Zanba Based on CRITIC Combined with Box-Behnken Response Surface Method

Published on: May 12, 2023

Area of Science:

Medicinal Chemistry
Drug Discovery
Computational Chemistry

Background:

Concurrent evaluation of multiple drug properties is crucial for prioritizing drug design outputs.
Formalized multiparameter optimization strategies are underutilized in current drug design practices.

Purpose of the Study:

To introduce a novel compound-quality scoring method for drug discovery.
To address the challenge of missing data in drug design projects.
To provide confidence estimates for compound quality assessments.

Main Methods:

Developed a scoring system using simple and aggregated desirability functions based on medicinal chemistry rules.
Implemented transparent handling of missing data, essential for data-limited drug hunting.
Incorporated confidence estimation for the compound-quality score interpretation.

Main Results:

The scoring system provides systematic insights into the quality of newly synthesized compounds.
Associated confidence measures enhance the interpretability of compound quality.
Tracking quality scores over time reveals drug design team progress.

Conclusions:

Compound quality scores and confidences offer systematic insights into chemical asset quality.
Monitoring synthetic output quality over time aids in assessing drug design team progress.
The method has potential applications in portfolio risk and resource management for drug development.