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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التفاصيل البيبلوغرافية
المؤلف الرئيسي:	Jialei Dai (13020462) (author)
مؤلفون آخرون:	Yutong Zhang (4844703) (author), Chen Shi (415895) (author), Yang Liu (4829) (author), Peng Xiu (543747) (author), Yong Wang (12837) (author)
منشور في:	2024
الموضوعات:	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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BEGAN: Boltzmann-Reweighted Data Augmentation for Enhanced GAN-Based Molecule Design in Insect Pheromone Receptors

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