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Structural Bias in Three-Dimensional Autoregressive Generative Machine Learning of Organic Molecules.

Zsuzsanna Koczor-Benda1, Joe Gilkes1,2, Francesco Bartucca1

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Generative machine learning models like G-SchNet can design novel molecules, but they often show bias. This study reveals G-SchNet generates molecules with fewer saturated bonds and more heteroatoms, impacting chemical space and properties.

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Area of Science:

  • Computational chemistry
  • Materials science
  • Machine learning

Background:

  • Generative machine learning models are increasingly used for designing novel molecules and materials.
  • Three-dimensional structure generation is crucial for quantum chemistry workflows and property prediction.
  • Model performance evaluation traditionally focuses on novelty, validity, and uniqueness, but chemical space reproduction is also vital.

Purpose of the Study:

  • To investigate the G-SchNet autoregressive model's ability to reproduce chemical space and property distributions of training data.
  • To assess the impact of G-SchNet's generation bias on molecular properties and chemical space coverage.
  • To explore methods for mitigating generation bias, such as functional group constraints and composite datasets.

Main Methods:

  • Analysis of elemental composition, size, bond-length, functional group, and chemical space distributions for training and generated molecules.
  • Principal Component Analysis (PCA) of chemical space to identify generation biases.
  • Application of decision tree models to discriminate between training and generated data and reveal chemical differences.

Main Results:

  • G-SchNet exhibits a generation bias, producing molecules that are less saturated and contain more heteroatoms, irrespective of hyperparameters or training data distribution.
  • Purely aliphatic molecules are largely absent in the generated set.
  • Decision tree models successfully identified the generation bias and highlighted key chemical differences affecting electronic properties like the HOMO-LUMO gap.

Conclusions:

  • The G-SchNet model demonstrates a significant bias in generating molecules, leading to an inaccurate representation of the training data's chemical space and properties.
  • Functional group constraints and composite datasets can partially alleviate the observed generation bias.
  • Understanding and addressing generation bias is critical for accurately designing functional molecules with desired electronic properties using generative models.