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Stereochemically-aware bioactivity descriptors for uncharacterized chemical compounds.

Arnau Comajuncosa-Creus1, Aksel Lenes1, Miguel Sánchez-Palomino1

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Stereochemistry significantly impacts drug activity, with ~40% of stereoisomers showing distinct bioactivities. New deep learning models generate stereochemically-aware descriptors for better small molecule bioactivity prediction.

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Area of Science:

  • Medicinal Chemistry
  • Computational Chemistry
  • Pharmacology

Background:

  • Stereochemistry is crucial in pharmacology, influencing drug efficacy and safety.
  • Understanding stereoisomer-specific bioactivity is essential for drug discovery and development.

Purpose of the Study:

  • To systematically investigate the relationship between stereoisomerism and bioactivity across a large dataset of molecules.
  • To develop novel deep learning models for generating stereochemically-aware bioactivity descriptors.
  • To assess the ability of these descriptors to differentiate stereoisomers and predict their binding profiles.

Main Methods:

  • Analysis of over 1 million compounds to quantify differences in bioactivity between stereoisomers.
  • Training of deep neural networks (Signaturizers3D) using 3D molecular representations.
  • Evaluation of descriptor performance in distinguishing stereoisomers and predicting target binding.

Main Results:

  • Approximately 40% of stereoisomer pairs exhibit distinct bioactivities.
  • Signaturizers3D models successfully generate bioactivity descriptors capturing effects at various biological complexity levels.
  • The developed descriptors accurately distinguish between stereoisomers and their target binding profiles.

Conclusions:

  • Stereochemically-aware descriptors offer a more faithful representation of small molecule bioactivity.
  • These descriptors effectively capture critical differences in the activity of stereoisomers.
  • The approach advances the prediction of small molecule bioactivity by incorporating stereochemical information.