LCCH:A low computational complexity hybrid model based on the half-router attention for biopharmaceutical indicators prediction
Yichen Song, Chenxi Xia, Simengxu Qiao, Changdi Li, Qunshan He, Xinggao Liu · 2024
AI4Biopharmaceutical is an important field of interest in both academia and industry. Due to the characteristics of biopharmaceutical industry data and the hardware limitation of real factories, biopharmaceutical indicators prediction models need to balance computational complexity and prediction effectiveness. We propose a low computational complexity hybrid model (LCCH) combing the biopharmaceutical mechanism with artificial intelligence for biopharmaceutical indicators prediction. We use the Doolittle method to reduce the computational complexity of the online decomposition phase to O(1), while employing the half-router attention incorporating biopharmaceutical mechanism to significantly reduce the computational complexity of the prediction phase. The model achieves the state-of-the-art results for the prediction of five biopharmaceutical indicators: amino nitrogen, reducing sugar, total sugar, bacterial concentration,and viscosity in the erythromycin pharmaceutical scenario. Compared to the other outstanding baseline models from the last three years, LCCH demonstrates the superiority and potential of the hybrid model for biopharmaceutical industrial applications.