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Accutar Biotech Inc., a technology company pioneering AI guided drug discovery to bring broader benefits for patients, announced Dr. Pasi A. Jänne joins scientific advisory board to support development of the company’s pipeline drugs in lung cancer.

“It is our honor to have world esteemed leaders like Dr. Pasi A. Jänne to advise us to develop precision medicines for unmet medical needs in the field,” said Jie Fan, PhD, founder and CEO of Accutar, “Dr. Jänne’s insights will strengthen our company’s Lung Cancer franchise.”


Dr. Pasi A. Jänne, M.D., Ph.D.

Dr. Jänne is the Director of the Lower Center for Thoracic Oncology at Dana-Farber Cancer Institute and a Professor of Medicine at Harvard Medical School. He is also the Director of the Belfer Center for Applied Cancer Science at the Dana-Farber Cancer Institute. After earning his MD and PhD from the School of Medicine at the University of Pennsylvania, Dr. Jänne completed his internship and residency in Medicine at Brigham and Women’s Hospital, Boston.  He subsequently completed fellowship training at Dana-Farber Cancer Institute/Massachusetts General Hospital combined program in medical oncology in 2001. In 2002 he earned a Master’s Degree in clinical investigation from Harvard University.

Dr. Jänne’s research combines laboratory-based studies, with translational research and clinical trials of novel therapeutic agents in patients with lung cancer. His main research interests center around understanding and translating the therapeutic importance of oncogenic alterations in lung cancer.  He has made seminal therapeutic discoveries, including being on one of the co-discoverers of EGFR mutations, and findings from his work has led to the development of several clinical trials. Dr. Jänne has received several awards for his work including from the American Association for Cancer Research, European Society for Medical Oncology and the American Society of Clinical Oncology.

Accutar Biotech has recently completed a new round of funding from Yunfeng Capital, Coatue, and 3W Healthcare Fund, in addition to the previous investments from ZhenFund, IDG Capital, YITU Technology, Primavera Capital Group, CDH Investments, and other institutional investors. Accumulatively, Accutar Biotech has now raised over US$100 million to support the advancement of the world-leading artificial intelligence (AI)-empowered platform and the expansion of our proprietary drug pipeline.

Accutar Biotech’s proprietary AI platform for drug discovery, design, and optimization leverages the innovative universal force-field-based modeling methodology. Demonstrated in multiple cases, Accutar Biotech has successfully reduced the average preclinical drug discovery time to only 1 year.

“Our vision is to use AI to significantly accelerate the research and development of novel drugs,” said Accutar Biotech’s Founder and CEO, Dr. Jie Fan. “All of our assets will be globally patented, and we are committed to continuously transforming the drug discovery industry with the power of disruptive technology.”

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Based on round 1 submission results, the Accutar Biotech team ranked number 1 of over 300 participants in the IDG-DREAM Drug-Kinase Binding Prediction Challenge.

Accutar Biotech CEO, Dr. Jie Fan, was invited to present at Amgen’s artificial intelligence conference.

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Dr. Jie Fan presents at the ‘Vision for the AI Future – Where Intelligence and Wisdom Meet’ 2018 World Artificial Intelligence Conference in Shanghai.

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Figure: Side chain flexible docking model (in green) enables the chimera design of two ligand fragments (in yellow and pink).

A bisubstrate nicotinamide N-methyltransferase (NNMT) inhibitor was developed with the aid of Accutar Biotech’s platform recently. The binding pose of the designed inhibitor with human recombinant NNMT was predicted by Accutar Biotech’s side chain flexible docking method. The docking result was further validated by crystallization experiments.

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Using a deep neural network architecture without physics-based assumptions, Accutar Biotech has demonstrated that side chain conformation prediction accuracy can be improved by more than 25%, especially for aromatic residues compared with current standard methods.

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