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Related Experiment Videos

Solubility prediction by recursive partitioning.

Xiaoyang Xia1, Edward Maliski, Janet Cheetham

  • 1Research Informatics, Amgen Inc., Thousand Oaks, California 91320, USA. xxia@amgen.com

Pharmaceutical Research
|November 19, 2003
PubMed
Summary
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A computational model using a decision tree improved the selection of compounds for nuclear magnetic resonance (NMR) screening. This approach doubled the success rate of identifying suitable compounds, enhancing cost-effectiveness.

Area of Science:

  • Computational chemistry
  • Drug discovery
  • Chemical informatics

Background:

  • Selecting suitable compounds for screening is crucial for efficient drug discovery.
  • Insolubility is a common issue leading to wasted resources in compound selection.
  • Predictive models can optimize the selection of compounds for high-throughput screening.

Purpose of the Study:

  • To develop and validate a computational model for predicting small molecule solubility.
  • To enhance the cost-effectiveness of selecting vendor compounds for nuclear magnetic resonance (NMR) screening.

Main Methods:

  • A recursive partitioning decision tree classification model was built using commercial software.
  • The model was trained on 1992 compounds using calculated physical and topological properties.

Related Experiment Videos

  • A test set of 2851 compounds was used to assess predictive accuracy, guiding the purchase of 686 compounds for NMR screening.

    • Main Results:

    • The decision tree model doubled the percentage of acceptable compounds for NMR screening compared to a cLogP cutoff.
    • The successful selection rate improved from 25% to 50% when using the decision tree model.
    • This demonstrates a significant improvement in identifying soluble compounds for screening.

    Conclusions:

    • A simple recursive partitioning decision tree model effectively predicts compound solubility for NMR screening.
    • The model improves cost-effectiveness by minimizing the purchase of insoluble vendor compounds.
    • This approach reduces waste and increases the efficiency of NMR screening campaigns.