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.
About the role
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.
Responsibilities:
- Develop state of the art web applications and APIs
- Identify, test, and fix usability and performance issues in distributed and live environments
- Integrate internal tools into cloud platforms and infrastructures.
Qualifications:
- Bachelor’s degree in computer science or a related field
- Experience in C, C++, Python, and web frameworks
- Proficient in data structures and algorithms
- Strong desire to dive in new fields and technologies.
Bonus skills:
- Experience in DevOps tools and procedures
- Proficient in Unix shells and shell scripts
- Experience in web and UI designing.
Benefits:
- Competitive compensation in the industry
- A fast-growing and friendly working environment
- Interesting and rewarding drug discovery teams and projects.
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.
About the role
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.
Responsibilities:
- Design state of the art algorithms to solve challenging problems in drug discovery.
- Implement, analyze, and optimize algorithms on a daily basis.
- Interface with our product team to productize the algorithms.
Qualifications:
- Bachelor’s degree in computer science or a related field
- Experience in C and C++
- Proficient in data structures and algorithms
- Strong desire to solve challenging problems.
Bonus skills:
- Experience in programming contests is a big plus
- Experience in high-performance computing
- Publications in data mining and machine-learning subjects.
Benefits:
- Competitive compensation in the industry
- A fast-growing and friendly working environment
- Interesting and rewarding drug discovery teams and projects.
Please send your CV and cover letter to career@accutarbio.com.
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.