graph neural networks

Transferable FB-GNN-MBE framework for potential energy surfaces: Data-adaptive transfer learning in deep learned many-body expansion theory

Mechanistic understanding and rational design of complex chemical systems depend on fast and accurate predictions of electronic structures beyond individual building blocks. However, if the system exceeds hundreds of atoms, first-principles quantum mechanical (QM) modeling becomes impractical. In this study, we develop...

Lorentz-equivariance without limitations

Lorentz Local Canonicalization (LLoCa) ensures exact Lorentz-equivariance for arbitrary neural networks with minimal computational overhead. For the LHC, it equivariantly predicts local reference frames for each particle and propagates any-order tensorial information between them. We apply it to graph networks and tran...

Graph Convolutional Neural Networks Sensitivity Under Probabilistic Error Model (slides) - Slides

Graph Convolutional Neural Networks Sensitivity Under Probabilistic Error Model (video)

Abstract B002: Computational Discovery of Evolutionary Synthetic Lethality Networks for Personalized Gene Therapy and Small-Molecule Design in Treatment-Resistant Pediatric High-Grade Glioma

An integrative computational framework is developed to reconstruct resistance evolution, identify state-specific therapeutic vulnerabilities, and design personalized gene therapy and blood-brain barrier (BBB)-penetrant small-molecule therapeutics targeting treatment-resistant pediatric glioma.

Graph Convolutional Neural Networks Sensitivity Under Probabilistic Error Model (slides) - Slides

Abstract B004: Targeting Transient Cell-State Dependencies in Glioblastoma Through Single-Cell Evolutionary Modeling and Structure-Based Drug Design

Glioblastoma kills within a median of 15 months even after resection, radiotherapy, and temozolomide, largely because tumor cells adapt to therapy through transient drug-tolerant states long before stable genetic resistance takes hold. Most computational drug discovery pipelines are built on static molecular snapshot...

Structured PREreview of "Training, learning and inference: unified dynamics of neural systems"

This Zenodo record is a permanently preserved version of a Structured PREreview. You can view the complete PREreview at https://prereview.org/reviews/22899514. Does the introduction explain the objective of the research presented in the preprint? Yes The introduction clearly establishes the motivation, core research ob...

Graph Convolutional Neural Networks Sensitivity Under Probabilistic Error Model (slides) - Slides

Graph Convolutional Neural Networks Sensitivity Under Probabilistic Error Model (slides) - Slides

Graph-AI Frameworks for Integrative Multi-Omics Data Analysis

Multi-omics profiling technologies, measuring genomics, epigenomics, transcriptomics, proteomics, enable characterization of complex biological systems across multiple molecular layers. The large-scale multi-omics datasets provide complementary views of complex molecular mechanisms of complex diseases, yet integrative...

Graph Convolutional Neural Networks Sensitivity Under Probabilistic Error Model (video)