AUGMENTING MOLECULAR DEEP GENERATIVE MODELS WITH TOPOLOGICAL DATA ANALYSIS REPRESENTATIONS

doi:doi:10.57702/3l6j3yiv

AUGMENTING MOLECULAR DEEP GENERATIVE MODELS WITH TOPOLOGICAL DATA ANALYSIS REPRESENTATIONS

Deep generative models have emerged as a powerful tool for learning useful molecular representations and designing novel molecules with desired properties, with applications in drug discovery and material design. Here we propose augmentation of deep generative models with topological data analysis (TDA) representations, known as persistence images, for robust encoding of 3D molecular geometry.

BibTex:

@dataset{Yair_Schiff_and_Vijil_Chenthamarakshan_and_Samuel_C_Hoffman_and_Karthikeyan_Natesan_Ramamurthy_and_Payel_Das_2025,
    abstract = {Deep generative models have emerged as a powerful tool for learning useful molecular representations and designing novel molecules with desired properties, with applications in drug discovery and material design. Here we propose augmentation of deep generative models with topological data analysis (TDA) representations, known as persistence images, for robust encoding of 3D molecular geometry.},
    author = {Yair Schiff and Vijil Chenthamarakshan and Samuel C. Hoffman and Karthikeyan Natesan Ramamurthy and Payel Das},
    doi = {10.57702/3l6j3yiv},
    institution = {No Organization},
    keyword = {'3D molecular geometry', 'deep generative models', 'molecular design', 'persistence images', 'topological data analysis'},
    month = {jan},
    publisher = {TIB},
    title = {AUGMENTING MOLECULAR DEEP GENERATIVE MODELS WITH TOPOLOGICAL DATA ANALYSIS REPRESENTATIONS},
    url = {https://service.tib.eu/ldmservice/dataset/augmenting-molecular-deep-generative-models-with-topological-data-analysis-representations},
    year = {2025}
}