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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| منشور في: |
2024
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| _version_ | 1852025029904039936 |
|---|---|
| author | Jialei Dai (13020462) |
| author2 | Yutong Zhang (4844703) Chen Shi (415895) Yang Liu (4829) Peng Xiu (543747) Yong Wang (12837) |
| author2_role | author author author author author |
| author_facet | Jialei Dai (13020462) Yutong Zhang (4844703) Chen Shi (415895) Yang Liu (4829) Peng Xiu (543747) Yong Wang (12837) |
| author_role | author |
| dc.creator.none.fl_str_mv | Jialei Dai (13020462) Yutong Zhang (4844703) Chen Shi (415895) Yang Liu (4829) Peng Xiu (543747) Yong Wang (12837) |
| dc.date.none.fl_str_mv | 2024-11-21T16:03:45Z |
| dc.identifier.none.fl_str_mv | 10.1021/acs.jpcb.4c06729.s004 |
| dc.relation.none.fl_str_mv | https://figshare.com/articles/dataset/BEGAN_Boltzmann-Reweighted_Data_Augmentation_for_Enhanced_GAN-Based_Molecule_Design_in_Insect_Pheromone_Receptors/27728222 |
| dc.rights.none.fl_str_mv | CC BY-NC 4.0 info:eu-repo/semantics/openAccess |
| dc.subject.none.fl_str_mv | 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 |
| dc.title.none.fl_str_mv | BEGAN: Boltzmann-Reweighted Data Augmentation for Enhanced GAN-Based Molecule Design in Insect Pheromone Receptors |
| dc.type.none.fl_str_mv | Dataset info:eu-repo/semantics/publishedVersion dataset |
| description | 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(−Δ<i>U</i>/τ), where Δ<i>U</i> 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. |
| eu_rights_str_mv | openAccess |
| id | Manara_8740c6f086f68896f88c3152b00aaf76 |
| identifier_str_mv | 10.1021/acs.jpcb.4c06729.s004 |
| network_acronym_str | Manara |
| network_name_str | ManaraRepo |
| oai_identifier_str | oai:figshare.com:article/27728222 |
| publishDate | 2024 |
| repository.mail.fl_str_mv | |
| repository.name.fl_str_mv | |
| repository_id_str | |
| rights_invalid_str_mv | CC BY-NC 4.0 |
| spelling | BEGAN: Boltzmann-Reweighted Data Augmentation for Enhanced GAN-Based Molecule Design in Insect Pheromone ReceptorsJialei Dai (13020462)Yutong Zhang (4844703)Chen Shi (415895)Yang Liu (4829)Peng Xiu (543747)Yong Wang (12837)BiophysicsBiochemistryGeneticsMolecular BiologyNeurosciencePharmacologyImmunologyComputational BiologyBiological Sciences not elsewhere classifiedChemical Sciences not elsewhere classifiedInformation Systems not elsewhere classifiedsuperior binding propertiesreweighted data augmentationoverall distribution shapeoptimizing molecular generationmolecular discovery pipelinesmachine learning techniquesimproved binding affinitieshigher binding affinitiesgenerative adversarial networksexplore molecular spacesrelated scaling hyperparameterenhanced protein bindingrational molecular designbased molecule design>/ τ ),enhanced gan ),gan ),training basedhyperparameter τenhanced methodenhanced ganu </target proteinsreweighting processreinforced gansreasonable rangepotentially increasingparameter dependenciesmethod offersessential toolsdocking algorithmscomprehensive investigationbind stronglyIdentifying 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(−Δ<i>U</i>/τ), where Δ<i>U</i> 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.2024-11-21T16:03:45ZDatasetinfo:eu-repo/semantics/publishedVersiondataset10.1021/acs.jpcb.4c06729.s004https://figshare.com/articles/dataset/BEGAN_Boltzmann-Reweighted_Data_Augmentation_for_Enhanced_GAN-Based_Molecule_Design_in_Insect_Pheromone_Receptors/27728222CC BY-NC 4.0info:eu-repo/semantics/openAccessoai:figshare.com:article/277282222024-11-21T16:03:45Z |
| spellingShingle | BEGAN: Boltzmann-Reweighted Data Augmentation for Enhanced GAN-Based Molecule Design in Insect Pheromone Receptors Jialei Dai (13020462) 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 |
| status_str | publishedVersion |
| title | BEGAN: Boltzmann-Reweighted Data Augmentation for Enhanced GAN-Based Molecule Design in Insect Pheromone Receptors |
| title_full | BEGAN: Boltzmann-Reweighted Data Augmentation for Enhanced GAN-Based Molecule Design in Insect Pheromone Receptors |
| title_fullStr | BEGAN: Boltzmann-Reweighted Data Augmentation for Enhanced GAN-Based Molecule Design in Insect Pheromone Receptors |
| title_full_unstemmed | BEGAN: Boltzmann-Reweighted Data Augmentation for Enhanced GAN-Based Molecule Design in Insect Pheromone Receptors |
| title_short | BEGAN: Boltzmann-Reweighted Data Augmentation for Enhanced GAN-Based Molecule Design in Insect Pheromone Receptors |
| title_sort | BEGAN: Boltzmann-Reweighted Data Augmentation for Enhanced GAN-Based Molecule Design in Insect Pheromone Receptors |
| topic | 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 |