Scientists from Chongqing University and Zhejiang University used AI to design more than 7,000 brand-new proteins that dissolve easily, stay stable under heat, and are ready for lab testing—helping drug and diagnostic companies work faster and cut early-stage development costs.

Shandong University developed an advanced AI framework that predicts molecular properties in seconds with high accuracy and minimal computational resources, dramatically accelerating and democratizing early‐stage drug discovery.

Scientists at Shaanxi Normal University have developed an AI-powered dual-channel model that predicts miRNA–drug interactions with up to 96% accuracy—validated on public datasets and real-world drugs—to accelerate and economize the discovery of novel therapeutic targets.

Researchers from Nanjing University and UC Berkeley have unveiled a clustering-based reinforcement learning framework that balances novelty and reward to accelerate and enhance AI exploration across robotics, gaming, and real-world applications.

Researchers have developed a self-tuning AI framework that dynamically filters noisy graph data to boost reliability and accuracy across industries from healthcare to finance.

Researchers from Jilin University and the University of North Carolina have developed an energy-efficient, stability-boosting data-offloading method that uses advanced optimization algorithms to slash delays and power use in mobile crowdsensing, paving the way for smoother, greener smart-city services.