BEGAN: Boltzmann-Reweighted Data Augmentation for Enhanced GAN-Based Molecule Design in Insect Pheromone Receptors

Identifying
small molecules that bind strongly to target proteins
in rational molecular design is crucial. Machine learning techniques,
such as generative adversarial networks (GAN), are now essential tools
for generating such molecules. In this study, we present an enhanced
method for molecule generation using objective-reinforced GANs. Specifically,
we introduce BEGAN (Boltzmann-enhanced GAN), a novel approach that
adjusts molecule occurrence frequencies during training based on the
Boltzmann distribution exp­(−ΔU/τ),
where ΔU represents the estimated binding free
energy derived from docking algorithms and τ is a temperature-related
scaling hyperparameter. This Boltzmann reweighting process shifts
the generation process toward molecules with higher binding affinities,
allowing the GAN to explore molecular spaces with superior binding
properties. The reweighting process can also be refined through multiple
iterations without altering the overall distribution shape. To validate
our approach, we apply it to the design of sex pheromone analogs targeting Spodoptera frugiperda pheromone receptor SfruOR16,
illustrating that the Boltzmann reweighting significantly increases
the likelihood of generating promising sex pheromone analogs with
improved binding affinities to SfruOR16, further supported by atomistic
molecular dynamics simulations. Furthermore, we conduct a comprehensive
investigation into parameter dependencies and propose a reasonable
range for the hyperparameter τ. Our method offers a promising
approach for optimizing molecular generation for enhanced protein
binding, potentially increasing the efficiency of molecular discovery
pipelines.