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Different notations are used to represent the three-dimensional structure of molecules on two-dimensional surfaces. One of the most commonly used representations is the dash-wedge formula. The dashed wedges, solid wedges, and the plane lines indicate the groups situated behind the plane, coming out of the plane, and in the plane, respectively.
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Adolf von Baeyer attempted to explain the instabilities of small and large cycloalkane rings using the concept of angle strain — the strain caused by the deviation of bond angles from the ideal 109.5° tetrahedral value for sp3  hybridized carbons. However, while cyclopropane and cyclobutane are strained, as expected from their highly compressed bond angles, cyclopentane is more strained than predicted, and cyclohexane is virtually strain-free. Hence, Baeyer’s theory that...
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Efficient Generation of Conformer Ensembles Using Internal Coordinates and a Generative Directional Graph Convolution

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We developed GINGER, a neural network algorithm for fast molecular conformer generation. This method efficiently predicts low-energy 3D molecular structures from 2D chemical graphs, accelerating drug discovery.

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Area of Science:

  • Computational Chemistry
  • Artificial Intelligence in Chemistry
  • Molecular Modeling

Background:

  • Accurate prediction of molecular 3D structures is crucial for understanding chemical and biological processes.
  • Traditional methods for conformer generation can be computationally expensive and slow, limiting their application to large datasets.
  • Developing high-throughput and accurate algorithms for molecular conformer generation is an ongoing challenge in computational chemistry.

Purpose of the Study:

  • To present a novel neural-network-based algorithm for high-throughput molecular conformer generation.
  • To enable the prediction of low-energy conformers from 2D chemical topology using internal coordinate representations.
  • To provide a computationally efficient and accurate method for generating conformer ensembles.

Main Methods:

  • A chemical graph-convolutional network was trained to predict low-energy conformers using internal coordinates (bond lengths, bond, and torsion angles).
  • A generative neural network architecture was employed for denoising in torsion space, generating conformer ensembles correlated with torsion energy profiles.
  • Force-field-based energy minimization was used to refine the generated conformers, with GPU optimization for computation-intensive stages.

Main Results:

  • The GINGER algorithm demonstrated highly competitive results in conformer recovery when benchmarked on a standard PDB test set.
  • The method achieved high throughput rates, suitable for processing large-scale compound libraries (giga-scale).
  • Conformer ensembles generated by GINGER showed populations well-correlated with torsion energy profiles.

Conclusions:

  • The GINGER algorithm offers an efficient and accurate approach for high-throughput molecular conformer generation.
  • This method significantly advances the capability to process large chemical libraries for drug discovery and molecular modeling.
  • A web server for interactive conformer generation and viewing is available, facilitating broader access and application.