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Prescient Design

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Selected work

Representative Papers

Diffeomorphic Optimization

Jul 01, 2026

This work addresses differentiable optimization on low-dimensional data manifolds embedded in high-dimensional spaces, where conventional gradient descent often deviates from the manifold and struggles with non-convex loss landscapes. The authors propose a novel approach that leverages diffusion and flow models to construct a diffeomorphic mapping, pulling the manifold back to a simple base space for optimization. Using tools from differential geometry, they prove this procedure is equivalent to Riemannian gradient descent, inherently preserving trajectories on the manifold. This is the first integration of diffeomorphic mappings with Riemannian optimization, extended to the Lie groups SO(3) and SE(3), yielding an automatic differentiation–compatible SO(3) gradient and a generalized adjoint-state backpropagation for Lie group ODE solvers. In protein design tasks, FrameFlow achieves a 91.3% secondary structure targeting success rate (versus 63.3% baseline), doubles the peptide binding affinity optimization speed compared to OC-Flow, and significantly reduces Rosetta energy by thousands of units.

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PPI-Net connects molecular protein interactions to functional processes in disease

May 08, 2026

This study addresses the challenge of bridging molecular-level protein–protein interactions with higher-order functional processes in disease through a cross-scale, interpretable modeling framework. The authors propose a hierarchical graph neural network that uniquely integrates the STRING protein–protein interaction network with the Reactome pathway hierarchy. Leveraging graph attention mechanisms, the model aggregates patient-specific multi-omics data—RNA-seq and DNA meth日晚间—bottom-up into coherent functional programs. A multi-layer supervised learning strategy enables interpretable integration from gene-level signals to biologically meaningful modules. Evaluated across ten cancer types in TCGA, the approach achieves over 90% accuracy, outperforming PPI-only models by 6.7% and surpassing single-head prediction by 12.3%. The method successfully recapitulates known oncogenic modules such as TP53–AKT and uncovers novel functional programs.

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Recent publications

Latest Papers

Diffeomorphic Optimization

Jul 01, 2026

This work addresses differentiable optimization on low-dimensional data manifolds embedded in high-dimensional spaces, where conventional gradient descent often deviates from the manifold and struggles with non-convex loss landscapes. The authors propose a novel approach that leverages diffusion and flow models to construct a diffeomorphic mapping, pulling the manifold back to a simple base space for optimization. Using tools from differential geometry, they prove this procedure is equivalent to Riemannian gradient descent, inherently preserving trajectories on the manifold. This is the first integration of diffeomorphic mappings with Riemannian optimization, extended to the Lie groups SO(3) and SE(3), yielding an automatic differentiation–compatible SO(3) gradient and a generalized adjoint-state backpropagation for Lie group ODE solvers. In protein design tasks, FrameFlow achieves a 91.3% secondary structure targeting success rate (versus 63.3% baseline), doubles the peptide binding affinity optimization speed compared to OC-Flow, and significantly reduces Rosetta energy by thousands of units.

0 citationsRead paper

PPI-Net connects molecular protein interactions to functional processes in disease

May 08, 2026

This study addresses the challenge of bridging molecular-level protein–protein interactions with higher-order functional processes in disease through a cross-scale, interpretable modeling framework. The authors propose a hierarchical graph neural network that uniquely integrates the STRING protein–protein interaction network with the Reactome pathway hierarchy. Leveraging graph attention mechanisms, the model aggregates patient-specific multi-omics data—RNA-seq and DNA meth日晚间—bottom-up into coherent functional programs. A multi-layer supervised learning strategy enables interpretable integration from gene-level signals to biologically meaningful modules. Evaluated across ten cancer types in TCGA, the approach achieves over 90% accuracy, outperforming PPI-only models by 6.7% and surpassing single-head prediction by 12.3%. The method successfully recapitulates known oncogenic modules such as TP53–AKT and uncovers novel functional programs.

0 citationsRead paper