Design of a Drug Development Tomography System Based on Multi-modal Data Fusion

This research aims to design a drug development tomography system based on multi-modal data fusion to improve the efficiency of drug screening and optimization. Deep learning algorithms are employed to extract and fuse features from multi-source imaging data to construct three-dimensional drug molecule models. Experimental results show that this system improves molecular structure prediction accuracy by 15% compared to traditional methods and reduces drug efficacy evaluation time by 30%. The conclusion suggests that multi-modal data fusion technology can significantly enhance molecular imaging and analysis capabilities in the drug development process, providing powerful tool support for new drug development.