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Practical High-Quality Electrostatic Potential Surfaces for Drug Discovery Using a Graph-Convolutional Deep Neural

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A new deep neural network (DNN) model rapidly generates electrostatic potential (ESP) surfaces for drug design. This accelerates the optimization of electrostatic complementarity, aiding in faster drug discovery.

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Area of Science:

  • Computational chemistry
  • Drug discovery
  • Artificial intelligence in chemistry

Background:

  • Optimizing electrostatic complementarity between proteins and ligands is crucial for drug design.
  • Accurate electrostatic potential (ESP) surface calculations typically require time-consuming quantum mechanics (QM) methods.
  • Faster methods are needed for interactive drug design.

Purpose of the Study:

  • To develop a rapid method for generating electrostatic potential (ESP) surfaces for ligands and proteins.
  • To enable interactive drug design by significantly reducing calculation time.
  • To provide a tool that aids in optimizing electrostatic complementarity for drug discovery.

Main Methods:

  • A graph convolutional deep neural network (DNN) model was trained on ESP surfaces from high-quality QM calculations.
  • The DNN model generates ESP surfaces for ligands in fractions of a second.
  • A method for constructing fast QM-trained ESP surfaces for proteins was developed.

Main Results:

  • The DNN model accurately generates ESP surfaces comparable to QM calculations.
  • The generated ESP values show good correlation with experimental medicinal chemistry properties.
  • The developed methods provide high-quality, interactive ESP surfaces.

Conclusions:

  • The developed DNN model and associated methods offer a significant acceleration in generating ESP surfaces.
  • These rapid, high-quality ESP surfaces are a powerful tool for advancing drug discovery programs.
  • The model and code are publicly available to facilitate their use in research.