Currently, deep learning, especially large-scale generative AI models, is ushering in a new era of digitalized, intelligent biomanufacturing. Following the breakthrough in protein structure prediction achieved by AlphaFold2, this year’s AlphaFold3, based on diffusion generative models, can predict the...
As an alternative to adversarial training, adversarial purification refers to a group of defense methods that transform an adversarial example into its counterpart on the manifold of normal data using generative models. Meng and Chen proposed MagNet [20], which uses a collection of auto-encoders [...
however, the key missing piece is generating new proof terms. In the above algorithm, it can be seen that such terms form the subset ofPthatNis independent of. In other words, these terms
Mosaic Score +23 points in the past 30 days About Biogeometry Biogeometry specializes in developing generative AI foundation models for protein design. Its AI-integrated biologics design platform known as GeoBiologics and high-throughput wet-lab validation platform form a design-build-test-learn close...
Benefiting from the proposed architecture, the generative ability of 3D Gaussians is enhanced, especially in structured regions. Our proposed pipeline achieves state-of-the-art performance in novel view synthesis and geometric reconstruction, as evaluated qualitatively and quantitatively on public datasets....
Generative AI —Advancing Towards De Novo Protein Design We are the first to apply generative diffusion models to molecular design. Deep generative models are expressive approximators for high-dimensional probability distributions. We have a repertoire of expressive generative models at hand for evaluating...
(see Methods). Furthermore, using statistical mechanical mean-field techniques, we derive algorithms for measuring the capacity,RMandDMfor manifolds given by either empirical data samples or from parametric generative models36,41. This theory assumed that the position and orientation of different ...
Surfaces serve as a natural parametrization to 3D shapes. Learning surfaces using convolutional neural networks (CNNs) is a challenging task. Current paradigms to tackle this challenge are to either adapt the convolutional filters to operate on surfaces,
As an alternative to adversarial training, adversarial purification refers to a group of defense methods that transform an adversarial example into its counterpart on the manifold of normal data using generative models. Meng and Chen proposed MagNet [20], which uses a collection of auto-encoders [...
Benefiting from the proposed architecture, the generative ability of 3D Gaussians is enhanced, especially in structured regions. Our proposed pipeline achieves state-of-the-art performance in novel view synthesis and geometric reconstruction, as evaluated qualitatively and quantitatively on public datasets....