CBM Seminar Series – Decoding RNA's Functional Logic for Therapeutic Discovery Using a Structure Informed AI Approach

The College of Biomedicine was honored to host Professor Qiangfeng Cliff ZHANG, Tsien Tang Associate Professor in the School of Life Sciences at Tsinghua University, as a distinguished speaker for the CBM Seminar Series on 28 September 2026. Professor Zhang is internationally recognized for his pioneering contributions at the intersection of artificial intelligence (AI) and life sciences, with extensive publications in leading journals such as Cell, Nature Biotechnology, and Nature Genetics.

In his talk entitled "Decoding RNA's Functional Logic for Therapeutic Discovery Using a Structure Informed AI Approach", Professor Zhang introduced RNAverse, a multimodal foundation model trained on 540 billion nucleotides that integrates RNA sequences, structure probing profiles, and functional annotations. He highlighted how RNAverse achieves state of the art performance in structural modeling and function prediction, substantially outperforming concurrent models. Building on this foundation, he presented SMRTnet for predicting RNA-small molecule interactions in drug screening, and SMRTdesign for programmable RNA design, including snoRNA scaffolds, ligand responsive aptazymes, and optimized 5' UTRs for mRNA vaccine constructs.

The seminar fostered active discussion among faculty, researchers, and students, highlighting significant breakthroughs in using AI to advance RNA biology and therapeutic discovery. Professor Zhang's lecture illustrated how interdisciplinary innovation in AI and molecular biology can catalyze meaningful progress in biomedical research and translational medicine.

(*Disclaimer: For details of the academic content described in this write-up, please refer to the original abstract provided in the event poster.)