Researchers from City University of Hong Kong, the Chinese Academy of Sciences, and the Massachusetts Institute of Technology have developed an artificial intelligence-driven workflow called AAPSI (AI-Accelerated PhotoSensitizer Innovation) that integrates expert knowledge, scaffold-based molecule generation, and Bayesian optimization to accelerate the discovery of novel photosensitizers for photodynamic therapy (PDT). Through this workflow, the team generated 6,148 candidate molecules and experimentally validated a hypocrellin-based compound, HB4Ph, which achieves a singlet oxygen quantum yield (ϕ_Δ) of 0.85 and absorption maxima (λ_max) of 645 nm — outperforming all clinical and trial-stage photosensitizers. The work is published in AI for Science .

A research team led by Dr. Hyun-Tak Kim at the Korea Research Institute of Chemical Technology (KRICT), in collaboration with Prof. Young-Jin Kim of Kyungpook National University, Prof. Geunsik Lee of UNIST, and Prof. Sang-Joon Kim of Chungnam National University, has developed a dual-atom catalyst precisely engineered at the atomic level. The catalyst enables stable conversion of CO₂ to

Researchers at the Yong Loo Lin School of Medicine, National University of Singapore (NUS Medicine) have developed a revolutionary new method to improve compact gene-editing tools known as base editors, which enable smaller, more precise DNA correction tools that may be safer for future gene therapies.Researchers at the Yong Loo Lin School of Medicine, National University of Singapore (NUS Medicine) have developed a revolutionary new method to improve compact gene-editing tools known as base editors, which enable smaller, more precise DNA correction tools that may be safer for future gene therapies.