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

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 gene...

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Bibliographic Details
Main Author: Jialei Dai (13020462) (author)
Other Authors: Yutong Zhang (4844703) (author), Chen Shi (415895) (author), Yang Liu (4829) (author), Peng Xiu (543747) (author), Yong Wang (12837) (author)
Published: 2024
Subjects:
Biophysics
Biochemistry
Genetics
Molecular Biology
Neuroscience
Pharmacology
Immunology
Computational Biology
Biological Sciences not elsewhere classified
Chemical Sciences not elsewhere classified
Information Systems not elsewhere classified
superior binding properties
reweighted data augmentation
overall distribution shape
optimizing molecular generation
molecular discovery pipelines
machine learning techniques
improved binding affinities
higher binding affinities
generative adversarial networks
explore molecular spaces
related scaling hyperparameter
enhanced protein binding
rational molecular design
based molecule design
>/ τ ),
enhanced gan ),
gan ),
training based
hyperparameter τ
enhanced method
enhanced gan
u </
target proteins
reweighting process
reinforced gans
reasonable range
potentially increasing
parameter dependencies
method offers
essential tools
docking algorithms
comprehensive investigation
bind strongly
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