Results 151 to 160 of about 25,591 (263)

A Unified Hierarchical Multiscale Fusion Framework for Drug–Target Affinity Prediction: From Benchmark Performance to Nanomolar Inhibitor Discovery

Advanced Science, EarlyView.
A multimodal fusion framework integrating sequence, atomic, and fragment representations captures drug–target interactions across multiple scales. The model delivers strong predictive performance and enables efficient virtual screening. Applied to hematopoietic progenitor kinase 1 (HPK1), it identifies structurally diverse inhibitors with nanomolar ...
Shuo Liu, Xiang Zhang, Haixia Feng, Yuquan Li, Xiaoqing Gong, Yong Liang, Xiaojun Yao, Huanxiang Liu   +7 more
wiley   +1 more source

Drought tolerance classification using unmanned aerial systems based on RGB and multispectral data. [PDF]

Front Plant Sci
Pereira HD, Vieira Almeida Nonato J, Duarte RCRM, Gerhardt IR, Dante RA, Yassitepe JECT.   +5 more
europepmc   +1 more source

AI‐Assisted Tumor Boundary Delineation via Targeted Ultrasmall Iron Oxide Nanoprobe for High‐Contrast HER2‐Positive Tumor Imaging

Advanced Science, EarlyView.
A novel targeted and pH‐responsive MRI contrast agent was integrated with a 3D nnU‐Net deep learning framework to enable accurate delineation of tumor boundary morphology in HER2‐positive breast cancer imaging. ABSTRACT Breast cancer continues to be a leading cause of cancer‐related mortality in women globally, where precise diagnosis and clear tumor ...
Jiaying Zheng, Yi Zhu, Xinrui Li, Sunxian Dai, Xiangyuan Ma, Hongwei Lu, Zhongling Wang, Benqing Zhou   +7 more
wiley   +1 more source

Correcting model error bias in estimations of neuronal dynamics from time series observations. [PDF]

Sci Rep
Williams I, Taylor JD, Nogaret A.
europepmc   +1 more source

Quantum‐Trained AI Enables Inverse Design of Organic Frontier Orbitals at Billion‐Scale

Advanced Science, EarlyView.
Quantum‐trained AI links molecular substructures to frontier‐orbital energetics and enables interpretable screening across nearly one billion GDB‐13 molecules. By combining fragment‐ and ring‐level insights with donor–acceptor energy alignment against ITIC, the framework narrows an immense chemical space to a small set of promising candidates and ...
Yeongnam Ko, Se Jin Kim, Ki Chul Kim
wiley   +1 more source

Machine learning-enabled membership determination of open cluster AH03_J0748-26.9 using Gaia DR3 astrometry. [PDF]

Sci Rep
Badawy WA, Abdel Rahman HI.
europepmc   +1 more source

Predicting Single‐Cell Perturbation Responses Across Biological Contexts With a Deep Generative Model Integrating Optimal Transport

Advanced Science, EarlyView.
Single‐cell perturbation responses are predicted across held‐out biological contexts using scPILOT, a query‐conditioned two‐stage latent response‐transfer framework. A shared latent representation supports cell‐level response estimation by latent optimal transport, followed by Leiden‐localized query‐specific transfer and adaptive weighting.
Jialiang Wang, Ziqi Liu, Zhengqian Zhang, Yikun Cao, Junjun Ren, Peng Cheng, Jingjing Tian, Lingyun Xie, Xin Lu, Zhanwei Du, Yongzhuang Liu   +10 more
wiley   +1 more source

PMPIHGLL: predicting metabolite-protein interactions using dual hypergraph convolutional networks and large language models. [PDF]

Brief Bioinform
Chen L, Jin Z, Shao Y, Zhou B.
europepmc   +1 more source

Robots and Minimal, Physics‐Informed Features: A Hybrid Framework for Enzyme Catalysis

Advanced Science, EarlyView.
Robotic experimentation and physics‐informed machine learning combine to predict enzyme substrate scope. With a handful of interpretable features derived from docking and quantum mechanics calculations, our model rivals descriptor‐heavy AI approaches and extrapolates to unseen substrates and enzyme classes.
Natalia Onishchenko, Eric S. Larsen, Govind Paneru, Kisung Lee, Diana V. Kolygina, Yankai Jia, Elizabeth Maria Clarissa, Wai‐Shing Wong, Bartosz A. Grzybowski   +8 more
wiley   +1 more source

IP-ConTex: detail-consistent texture generation with image prompt. [PDF]

Vis Comput Ind Biomed Art
Wang L, Feng J.
europepmc   +1 more source