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QDπ: A Quantum Deep Potential Interaction Model for Drug Discovery.

Jinzhe Zeng1, Yujun Tao1, Timothy J Giese1

  • 1Laboratory for Biomolecular Simulation Research, Institute for Quantitative Biomedicine and Department of Chemistry and Chemical Biology, Rutgers University, Piscataway, New Jersey08854, United States.

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We introduce QDπ-v1.0, a novel quantum mechanical/machine learning potential correction model for accurately predicting drug molecule energies. This advanced model excels in handling electrostatic interactions and protonation states, showing high accuracy for drug discovery applications.

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Area of Science:

  • Computational Chemistry
  • Drug Discovery
  • Machine Learning in Chemistry

Background:

  • Accurate modeling of molecular internal energy is crucial for drug discovery.
  • Existing semiempirical and machine learning potentials have limitations in accuracy and handling electrostatic interactions.
  • Developing efficient and accurate computational models is essential for accelerating drug design.

Purpose of the Study:

  • To introduce QDπ-v1.0, a new QM/Δ-MLP model for calculating the internal energy of drug molecules.
  • To evaluate the accuracy and performance of QDπ-v1.0 compared to existing computational models.
  • To demonstrate the utility of QDπ-v1.0 in modeling biologically relevant chemical reactions.

Main Methods:

  • Developed QDπ-v1.0 using a fast third-order self-consistent density-functional tight-binding (DFTB3/3OB) model corrected by a deep-learning potential (DeepPot-SE).
  • Trained the model against high-level reference data (ωB97X/6-31G*).
  • Compared QDπ-v1.0 performance against multiple established semiempirical and machine learning potentials.

Main Results:

  • QDπ-v1.0 accurately models intra- and intermolecular interactions, including protonation/deprotonation energies and tautomers.
  • Demonstrated exceptional performance in modeling RNA strand cleavage reactions with average errors under 0.5 kcal/mol.
  • Outperformed other tested models by over an order of magnitude in accuracy for the studied reactions.

Conclusions:

  • QDπ-v1.0 offers a significant advancement in accurately modeling drug molecule internal energies.
  • The model's ability to handle electrostatic interactions and protonation states makes it highly suitable for drug discovery.
  • QDπ-v1.0 shows great promise as a potential force field model for accelerating drug discovery and development.