Shu-Jen Chen

Chief Scientific Officer
​​AnHorn Medicines Co., Ltd.
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Dr. Shu-Jen Chen received her bachelor's degree in pharmacy and her master's in biochemistry training in Taiwan. She completed her Ph.D. in biochemistry and her postdoctoral training in the US. After returning to Taiwan, Dr. Chen joined the National Health Research Institution and established the first high-throughput drug screening system for drug discovery. Later, during her tenure at TaiGen Biotech, Dr. Chen led the in vitro pharmacology department for oncology and antiviral drug discovery. She designed and implemented the automated assay system, which integrates all in vitro tests to facilitate the drug discovery process. She also established a fully integrated chemistry-biology database to enable the seamless analysis of SAR. In 2006, Dr. Chen joined Chang Gung University and taught cancer biology, genomics, bioinformatics, and drug discovery in the Biomedicine graduate institutes. She also helps established the genomic core facility in the Molecular Medicine Research Center. In 2014, Dr. Chen co-founded ACT Genomics and served as Chief Scientific Officer to build a fully accredited next-generation gene sequencing clinical laboratory in Taiwan. She also led the company to obtain FDA clearance for an NGS-based cancer genome profiling assay. Currently, she is the Chief Scientific Officer of AnHorn Medicines, a dynamic new drug discovery company that embarks on a transformative journey to revolutionize the field through the integration of artificial intelligence. Dr. Chen’s 20 years plus experience in drug discovery, big data analysis, automation, and genomics will help to guide the company in future drug discovery endeavors.

Integrating artificial intelligence in the design and discovery of novel protein degraders

Traditional drug discovery methods often hinge on painstaking trial and error, a process plagued by inefficiency and prohibitive time constraints. However, the advent of artificial intelligence (AI) heralds a new era of innovation, empowering researchers to navigate the vast expanse of chemical space with unparalleled dexterity.
By harnessing AI algorithms' computational prowess, scientists can swiftly analyze vast datasets, pinpointing molecular structures with the optimal attributes for protein degradation. AI systems adapt and evolve through machine learning and predictive modeling, continuously refining their understanding of the intricate relationship between chemical structure and biological function.
Moreover, AI-driven platforms enable researchers to explore unconventional avenues in drug design, transcending the limitations imposed by conventional wisdom. By leveraging advanced algorithms to simulate and predict molecular interactions, scientists can engineer bespoke compounds tailored to target and degrade disease-causing proteins with pinpoint accuracy selectively.
The integration of AI in protein degrader discovery holds transformative potential across a myriad of therapeutic areas, from oncology to neurodegenerative disorders. By expediting the identification and optimization of candidate compounds, AI-driven approaches promise to accelerate the translation of scientific insights into clinically viable treatments, ushering in a new era of precision medicine.