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. 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.
Molecular modeling is an important topic in drug discovery. Decades of research have led to the development of high quality scalable molecular force fields.
In this paper, we show that neural networks can be used to train a universal approximator for energy potential functions.
By incorporating a fully automated training process we have been able to train smooth, differentiable, and predictive potential functions on large-scale crystal structures. A variety of tests have also been performed to show the superiority and versatility of the machine-learned model.
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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.”
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.
We are looking for an experienced software engineer to join our full-stack engineering team. The team is responsible for developing cloud infrastructure to empower our internal software and algorithms in the drug discovery field. The team develops solutions ranging from web front-ends for internal algorithms and software products, to database systems managing our experimental projects and data, and complex distributed computation and job scheduling systems.
Please send your CV and cover letter to career@accutarbio.com.
Figure. Left panel: Percentage R2 improvement over Cubist using Chemi-Net. Right panel: Overall network architecture.
Absorption, distribution, metabolism, and excretion (ADME) studies are critical for drug discovery. Conventionally, these tasks, together with other chemical property predictions, rely on domain-specific feature descriptors, or fingerprints. Following the recent success of neural networks, Accutar Biotech developed Chemi-Net, a completely data-driven, domain knowledge-free, deep learning method for ADME property prediction. To compare the relative performance of Chemi-Net with Cubist, one of the popular machine-learning programs used by Amgen, a large-scale ADME property prediction study was performed on-site at Amgen. The results showed that our deep neural network method improved current methods by a large margin. Accutar Biotech foresee that the significantly increased accuracy of ADME prediction seen with Chemi-Net over Cubist will greatly accelerate drug discovery.
We are looking for a talented individual to join our algorithm team to solve some of the most challenging algorithm problems in the world.
Our algorithm team is responsible for developing state of the art algorithms to implement our drug discovery methodology. We work with our biology teams to develop novel models with a strong biochemistry background. We utilize algorithms in various areas including, but not limited to, computational geometry, machine learning, optimization theory, combinatorial optimization, and bioinformatics.
We not only focus on the performance of our algorithms, but also work closely with our product team. Our products are used by the world’s best pharmaceutical companies and research institutions. To accomplish this, we spend a lot of time making our algorithm run efficiently on major hardware platforms. Besides tuning the algorithm, we also develop custom distributed platforms, machine-learning toolkits, and 3D visualization tools to get the most from the hardware we use.
