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An open-source framework for fast-yet-accurate calculation of quantum mechanical features.

Eike Caldeweyher1, Christoph Bauer1, Ali Soltani Tehrani1

  • 1Data Science and Modelling, Pharmaceutical Sciences, R & D, AstraZeneca, Gothenburg, Sweden. eike.caldeweyher@astrazeneca.com.

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Summary

The open-source kallisto framework efficiently calculates quantum mechanical features for molecules, offering robust predictions for molecular polarizabilities and van der Waals radii. It enhances machine learning models for chromatography retention times, providing physically motivated descriptors.

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Area of Science:

  • Computational Chemistry
  • Machine Learning
  • Quantum Mechanics

Background:

  • Accurate calculation of molecular properties is crucial for various scientific disciplines.
  • Existing methods for quantum mechanical feature calculation can be computationally expensive.
  • Machine learning models often rely on limited descriptors, potentially missing crucial physical insights.

Purpose of the Study:

  • Introduce the open-source kallisto framework for efficient and robust quantum mechanical feature calculation.
  • Evaluate the predictive power of kallisto for molecular polarizabilities and van der Waals radii.
  • Assess the impact of kallisto-derived physicochemical features on machine learning models for chromatographic retention times.

Main Methods:

  • Developed the open-source kallisto framework for quantum mechanical calculations.
  • Benchmarked molecular polarizability predictions against experimental data and second-order perturbation theory.
  • Created a van der Waals radius model based on atomic static polarizabilities.
  • Applied kallisto features to machine learning models for predicting chromatographic retention times, including super-critical fluid chromatography.

Main Results:

  • Kallisto's predictive power for molecular polarizabilities rivals established methods at lower computational cost.
  • Robust calculation of isotropic molecular polarizabilities for over 80,000 molecules.
  • Efficient calculation of van der Waals radii for proteins, including the SARS-CoV-2 spike protein.
  • Machine learning models incorporating kallisto features showed up to 10.6% improvement in predicting chromatographic retention times compared to models using only molecular fingerprints.
  • Shapley additive explanation values confirmed the retention of physical meaning in the machine learning models.

Conclusions:

  • The kallisto framework offers an efficient, robust, and cost-effective solution for calculating quantum mechanical features.
  • Physicochemically motivated features derived from kallisto significantly enhance the predictive power of machine learning models.
  • Kallisto is recommended as a valuable tool for future machine learning studies in chemistry and related fields.