Hyperspectral unmixing (HU) is a fundamental task for resolving the mixed pixel problem by decomposing a hyperspectral image (HSI) into constituent endmembers and their corresponding abundances. Although deep-learning-based HU methods have achieved promising performance, most rely on manually designed network architect...
Jing-Ran Wang, Feng-Chao Xiong, Zhiyuan Wen et al.· IEEE Transactions on Geoscie...· 0 citations
Targeted covalent inhibition is an important strategy in modern drug discovery, with cysteine being the most common residue targeted for covalent ligands. Accurate identification of covalently ligandable cysteines is therefore essential, especially for traditionally "undruggable" targets. However, structure-based metho...
Yan-Lin Ren, M. Mou, Yi-Miao Zhu et al.· Journal of Medicinal Chemist...· 0 citations
A scalable hybrid generative pipeline that combines a classical autoencoder for dimensionality reduction with a mixed-state quantum denoising diffusion probabilistic model (MSQuDDPM) operating in the learned latent space is proposed.
Qipeng Qian, Keli Deng, Yuntao Qian· arXiv.org· 0 citations
The Comprehensive VS Platform with AI Engine (CVSP-AIE) for drug discovery from compound libraries integrates three AI models: KarmaDock, a fast docking model that directly updates atomic coordinates; CarsiDock, an accurate docking model that predicts protein-ligand distances and reconstructs binding poses; and RTMScor...
