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
CCSite is presented, a protein language model-based framework for discovering covalently ligandable cysteines from protein sequences that uniquely integrates low-rank adaptation of ESM Cambrian with a cysteine-centered encoder-decoder module to capture local microenvironment features and long-range contextual informati...
Yan-Lin Ren, Min-Jie 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...
