Leveraging large language models for predictive chemistry

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The process of protein aggregation, central to neurodegenerative diseases like Huntington’s, is challenging to study due to its unpredictable nature and relatively rapid kinetics. Understanding its biomechanics is crucial for unraveling its role in disease ...

Neural networks have shown to be a powerful tool to represent the ground state of quantum many-body systems, including fermionic systems. However, efficiently integrating lattice symmetries into neural representations remains a significant challenge. In th ...

Cyclic peptides are attractive for drug discovery due to their excellent binding properties and the potential to cross cell membranes. However, by far not all cyclic peptides are cell permeable, and measuring or predicting their membrane permeability is no ...

Visual affordance segmentation identifies image regions of an object an agent can interact with. Existing methods re-use and adapt learning-based architectures for semantic segmentation to the affordance segmentation task and evaluate on small-size dataset ...

Decentralized learning (DL) is an emerging approach that enables nodes to collaboratively train a machine learning model without sharing raw data. In many application domains, such as healthcare, this approach faces challenges due to the high level of hete ...

Nuclear Magnetic Resonance (NMR) chemical shifts are powerful probes of local atomic and electronic structure that can be used to resolve the structures of powdered or amorphous molecular solids. Chemical shift driven structure elucidation depends critical ...

Leveraging large language models for predictive chemistry

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Self-driving microscopy detects the onset of protein aggregation and enables intelligent Brillouin imaging

Spectroscopy of two-dimensional interacting lattice electrons using symmetry-aware neural backflow transformations

Bulk measurement of membrane permeability for random cyclic peptides in living cells to guide drug development

Segmenting Object Affordances: Reproducibility and Sensitivity to Scale

Fair Decentralized Learning

A Deep Learning Model for Chemical Shieldings in Molecular Organic Solids Including Anisotropy