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Using matched molecular series as a predictive tool to optimize biological activity.

Noel M O'Boyle1, Jonas Boström, Roger A Sayle

  • 1NextMove Software , Cambridge, CB4 0EY, U.K.

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|March 8, 2014
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Summary

Matsy, a new method using matched molecular series, predicts R groups to enhance drug activity. This knowledge-based approach outperforms traditional methods by leveraging activity trends in related compounds.

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Area of Science:

  • Medicinal Chemistry
  • Computational Chemistry
  • Drug Discovery

Background:

  • Matched molecular pairs (MMPs) are widely used to analyze structure-activity relationships (SARs).
  • Matched molecular series (MMS) extend MMPs, offering a broader scope for SAR analysis by considering multiple R-group modifications at a single position.
  • Existing methods for R-group prediction often lack the ability to integrate diverse SAR trends.

Purpose of the Study:

  • To introduce Matsy, a novel knowledge-based method for predicting R groups that are likely to improve molecular activity.
  • To evaluate Matsy's performance by comparing its predictions against the Topliss tree using ChEMBLdb activity data.
  • To investigate the factors influencing the predictive success of Matsy, particularly the role of matched series length.

Main Methods:

  • Development of Matsy, a knowledge-based algorithm utilizing matched molecular series to predict R-group modifications.
  • Comparison of Matsy predictions with Topliss tree recommendations using curated activity data from ChEMBLdb.
  • Large-scale retrospective testing to quantitatively assess the predictive performance of Matsy.

Main Results:

  • Matsy demonstrates effective prediction of R groups that enhance activity based on observed activity orders within matched series.
  • The predictive success of Matsy is significantly correlated with the strength of preferred orders found in matched series, especially in longer series.
  • Matsy's performance was validated through extensive retrospective testing, showing its utility in drug design.

Conclusions:

  • Matsy provides a powerful tool for medicinal chemists to leverage SAR trends from various projects.
  • The algorithm can be applied as a Topliss-like recommendation system or a hypothesis-generation tool to guide compound design.
  • Integrating information from matched molecular series enhances the prediction of R groups for improved drug activity.