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Related Experiment Video

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Fabrication of a Bioactive, PCL-based "Self-fitting" Shape Memory Polymer Scaffold
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Combining structural and bioactivity-based fingerprints improves prediction performance and scaffold hopping

Oliver Laufkötter1,2, Noé Sturm3, Jürgen Bajorath4

  • 1Hit Discovery, Discovery Sciences, R&D, AstraZeneca, Gothenburg, Sweden. oliver.laufkotter@gmail.com.

Journal of Cheminformatics
|August 10, 2019
PubMed
Summary

This study introduces the BaSH fingerprint, combining structural and bioactivity data for improved drug discovery predictions. This hybrid approach enhances compound screening and identifies unique molecules more effectively.

Keywords:
Activity predictionCircular fingerprintsECFPHTSFPHigh throughput screeningMachine learningRandom forestScaffold hopping

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Area of Science:

  • Computational chemistry
  • Cheminformatics
  • Drug discovery

Background:

  • Existing methods for predicting chemical activity often rely solely on structural fingerprints.
  • High-throughput screening (HTS) data offers valuable bioactivity information that can complement structural data.
  • Integrating diverse descriptor types can lead to more robust predictive models.

Purpose of the Study:

  • To develop and evaluate a novel hybrid fingerprint, the bioactivity-structure hybrid (BaSH) fingerprint.
  • To demonstrate the benefits of combining chemical structure fingerprints with bioactivity-based fingerprints (HTSFPs) derived from HTS data.
  • To assess the performance of the BaSH fingerprint in an iterative screening scenario for targeted compound selection.

Main Methods:

  • Generated HTSFPs from PubChem HTS data.
  • Combined HTSFPs with ECFP4 structural fingerprints to create the BaSH fingerprint.
  • Benchmarked BaSH against individual ECFP4 and HTSFP fingerprints using retrospective analysis of PubChem HTS data.

Main Results:

  • The BaSH fingerprint exhibited improved predictive performance and scaffold hopping capability compared to individual fingerprints.
  • Synergistic effects were observed, with BaSH identifying unique compounds missed by ECFP4 or HTSFP alone.
  • Feature importance analysis revealed that a subset of HTSFP features significantly contributes to BaSH's performance.

Conclusions:

  • The hybrid BaSH fingerprint effectively integrates structural and bioactivity information for enhanced predictive power in drug discovery.
  • This approach supports activity prediction even for compounds with sparse bioactivity data by leveraging structural information.
  • The BaSH fingerprint offers a promising strategy for more targeted and efficient compound set selection in iterative screening processes.