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Related Experiment Video

Updated: Feb 28, 2026

Author Spotlight: Streamlining Protein Target Prediction and Validation via Molecular Docking and CETSA
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Author Spotlight: Streamlining Protein Target Prediction and Validation via Molecular Docking and CETSA

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OPLE: Drug Discovery Platform Combining 2D Similarity with AI to Predict Off-Target Liabilities.

Sarah E Biehn1, Juerg Lehmann1, Christoph Mueller2

  • 1Eurofins Discovery Services North America, LLC, 6 Research Park Drive, Saint Charles, MO 63304, USA.

Pharmaceuticals (Basel, Switzerland)
|February 27, 2026
PubMed
Summary
This summary is machine-generated.

This study introduces OPLE models combining machine learning and molecular similarity to predict small-molecule drug activity, improving early-stage drug discovery by identifying promising candidates and reducing off-target liabilities.

Keywords:
Tanimoto similarityextended-connectivity fingerprintsmachine learningmolecular similarityoff-target safetysafety pharmacology

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Area of Science:

  • Computational chemistry
  • Drug discovery
  • Machine learning in pharmacology

Background:

  • Off-target liabilities are a major hurdle in drug discovery, leading to candidate failure.
  • Artificial intelligence (AI) and machine learning (ML) are increasingly used to accelerate drug discovery.
  • Predictive models are needed to efficiently identify successful small-molecule drug candidates.

Purpose of the Study:

  • To develop and validate the OPLE (On-target Prediction of Liabilities) models.
  • To combine molecular similarity and machine learning for predicting compound activity.
  • To assess the predictive performance of OPLE models for drug safety liabilities.

Main Methods:

  • Models were trained using proprietary and public data for SafetyScreen panels 18 and 44.
  • Two-dimensional (2D) Tanimoto similarity from extended-connectivity fingerprints (ECFPs) was calculated.
  • Predictions from ECFP similarity and ML models were combined using belief theory.

Main Results:

  • A probability assignment curve demonstrated the relationship between similarity and activity.
  • OPLE models achieved recall values greater than 0.8 for over 80% of SafetyScreen targets.
  • The models showed favorable predictive ability, identifying active molecules and minimizing false negatives.

Conclusions:

  • Predicting safety liabilities early is crucial in small-molecule drug discovery.
  • The OPLE models serve as an early detection tool to save resources.
  • This approach helps focus efforts on drug candidates with minimal predicted or measured off-target liabilities.