no code implementations • 26 Feb 2024 • Masatoshi Uehara, Yulai Zhao, Kevin Black, Ehsan Hajiramezanali, Gabriele Scalia, Nathaniel Lee Diamant, Alex M Tseng, Sergey Levine, Tommaso Biancalani
It is natural to frame this as a reinforcement learning (RL) problem, in which the objective is to fine-tune a diffusion model to maximize a reward function that corresponds to some property.
no code implementations • 23 Feb 2024 • Masatoshi Uehara, Yulai Zhao, Kevin Black, Ehsan Hajiramezanali, Gabriele Scalia, Nathaniel Lee Diamant, Alex M Tseng, Tommaso Biancalani, Sergey Levine
Diffusion models excel at capturing complex data distributions, such as those of natural images and proteins.
1 code implementation • 1 Nov 2023 • Nathaniel Diamant, Ehsan Hajiramezanali, Tommaso Biancalani, Gabriele Scalia
SPICE is compatible with two different efficient-to-compute conformal scores, one oracle-optimal for marginal coverage (SPICE-ND) and the other asymptotically optimal for conditional coverage (SPICE-HPD).
no code implementations • 5 Jun 2023 • Alex M. Tseng, Nathaniel Diamant, Tommaso Biancalani, Gabriele Scalia
Diffusion models have achieved state-of-the-art performance in generating many different kinds of data, including images, text, and videos.
1 code implementation • 30 May 2023 • Colin A. Grambow, Hayley Weir, Nathaniel L. Diamant, Alex M. Tseng, Tommaso Biancalani, Gabriele Scalia, Kangway V. Chuang
Macrocyclic peptides are an emerging therapeutic modality, yet computational approaches for accurately sampling their diverse 3D ensembles remain challenging due to their conformational diversity and geometric constraints.
1 code implementation • 7 Feb 2023 • Alex M. Tseng, Nathaniel Diamant, Tommaso Biancalani, Gabriele Scalia
Our framework for graph diffusion can have a large impact on the interpretable conditional generation of graphs, including the generation of drug-like molecules with desired properties in a way which is informed by experimental evidence.
1 code implementation • 25 Jan 2023 • Nathaniel Diamant, Alex M. Tseng, Kangway V. Chuang, Tommaso Biancalani, Gabriele Scalia
However, one of the main limitations of existing methods is their large output space, which limits generation scalability and hinders accurate modeling of the underlying distribution.
1 code implementation • 21 Dec 2022 • Alex M. Tseng, Max Shen, Tommaso Biancalani, Gabriele Scalia
We highlight several advantages of branched diffusion models over the current state-of-the-art methods for class-conditional diffusion, including extension to novel classes in a continual-learning setting, a more sophisticated form of analogy-based conditional generation (i. e. transmutation), and a novel interpretability into the generation process.
no code implementations • 3 Nov 2022 • Austin Atsango, Nathaniel L. Diamant, Ziqing Lu, Tommaso Biancalani, Gabriele Scalia, Kangway V. Chuang
Molecular shape and geometry dictate key biophysical recognition processes, yet many graph neural networks disregard 3D information for molecular property prediction.
no code implementations • 21 Oct 2022 • Max W. Shen, Ehsan Hajiramezanali, Gabriele Scalia, Alex Tseng, Nathaniel Diamant, Tommaso Biancalani, Andreas Loukas
How much explicit guidance is necessary for conditional diffusion?
no code implementations • 7 Oct 2019 • Gabriele Scalia, Colin A. Grambow, Barbara Pernici, Yi-Pei Li, William H. Green
Advances in deep neural network (DNN) based molecular property prediction have recently led to the development of models of remarkable accuracy and generalization ability, with graph convolution neural networks (GCNNs) reporting state-of-the-art performance for this task.