Geometric Enhanced Pretraining for Interatomic Potentials Introduction Molecular dynamics (MD) simulations play an important role in fields such as physics, chemistry, biology, and materials science, providing insights into atomic-level processes. The accuracy and efficiency of MD simulations depend on the choice of interatomic potential functions ...

Major Breakthrough in Neuroscience Research: Deep Learning Technique Achieves Decoding of Natural Speech from Brain Signals A cross-disciplinary research team at New York University recently achieved a major breakthrough in the fields of neuroscience and artificial intelligence. They developed a novel deep learning-based framework that can directly...

From early drug discovery researchers face a daunting challenge – to find drug-like candidate molecules among approximately 10^60 possible molecular structures. One successful solution is to start from smaller “fragment” molecules, a strategy known as fragment-based drug design (FBDD). In the FBDD process, the first step is to computationally scree...

This is a paper about MassFormer, a graph transformer model for small molecule mass spectrometry prediction. This research addresses the problem of molecular identification in mass spectrometry data and proposes a novel deep learning approach to predict mass spectra of small molecules. Background: Mass spectrometry (MS) is an analytical technique w...

Currently, there are significant challenges in studying the statistical and geometrical properties of particles carried by the fluid in turbulence. Despite outstanding efforts in theory, numerical simulations, and experiments over the past 30 years, there is still a lack of models that can realistically reproduce the statistical and topological fea...

This research proposes an online learning method for efficiently constructing surrogate models that can accurately emulate complex systems. The method consists of three key components: Sampling strategy for generating new training and testing data; Learning strategy for generating candidate surrogate models based on the training data; Validation me...

In this study, researchers explored the role of the TAK1 activation pathway in CAR T cell therapy-associated immune effector cell-associated neurotoxicity syndrome (ICANS). They established a mouse ICANS model and found that following the transfer of CAR19 T cells, cerebellar cells were activated, underwent morphological changes, and expressed more...

Overcoming Acquired Resistance in Colorectal Cancer by Targeting LGR4 Research Background: Acquired drug resistance is a major obstacle in cancer treatment and a leading cause of cancer-related deaths. However, the mechanisms of resistance are diverse, and how to specifically target resistant cancer cells remains a significant clinical challenge. A...

Utilizing Dynamic Single-Cell Analysis to Discover a Clinically Effective Chimeric Antigen Receptor T-Cell Subset for Treating Diffuse Large B-Cell Lymphoma Research Background Chimeric antigen receptor (CAR) T-cell therapy has been proven to be an effective treatment for B-cell malignancies. However, it remains challenging to predict individual cl...

This is a long-term outcome study on survivors of pediatric glioma. The main purpose of the study was to evaluate the impact of changes in treatment approaches for pediatric glioma over the past few decades on long-term mortality, chronic health conditions, and subsequent tumor risk among survivors. Background: In the past, treatment for pediatric ...