Artificial Intelligence-based Drug Development Simulation Tool - Attracted $5.2 Million
Computational Biology, Bio-platform, SaaS, Machine intelligence, personalized healthcare.
VeriSIM is building personalized disease models that will change how patients' treatment is administered.
By proposing the optimal compound for drug development based on machine learning, it helps to bring a variety of personalized drugs to market quickly.
High accuracy and efficiency significantly reduce animal testing, reducing R&D costs by more than 50%, and allowing for faster review of potential treatment options.
VeriSIM Life
It’s the mission to increase human life expectancy through intelligence driven biosystem simulations to truly personalize patient treatment. Fueling the acceleration of research and development in drug discovery, we create the tools needed for unprecedented medical breakthroughs to take place much earlier in the drug discovery and development timeline. It’s our intention to create innovative products in the medical industry that will increase the efficiency and effectiveness of healthcare worldwide.
General Information
- Headquarters : San Francisco, California
- Founders : Jo Varshney
- Categories : Artificial Intelligence, Health Care, Machine Learning, SaaS
- Founded : 2017
- Contact : info@verisimlife.com
Funding
- Investors : Intel Capital, Loup Ventures, OCA Ventures, Serra Ventures, Stage Venture Partners, Susa Ventures, Twin Capital Management LLC, Village Global
- Funding Rounds (2) - $6.4M
- Aug 14, 2019 Seed Round / $5.2M
- Jan 1, 2018 Seed Round / $1.2M
1. Reason for attention and features
- Blockbuster drug sales are expected to exceed USD 1 billion, but R&D costs of more than USD 1 billion and 15 years are required to develop a new drug.
- Using artificial intelligence, the development period for new drugs can be cut in half, and cost savings are expected.
- VeriSiM leverages big data to use machine-learned recommendation tools to narrow the range of compounds to review for drug development, thereby reducing drug candidates, halving the number of preclinical animal trials and consequently R&D costs and cut time in half.
2. Current service status and challenges
- VeriSim is currently conducting in-vitro testing for 5,000 compounds, animal testing for 100 new drug candidates, and clinical trials for 10 new drug candidates, with the goal of reducing this in half through pharmacodynamic modeling and simulation.
- However, the technical difficulty is high, as in the case where IBM stopped the development and sale of ‘Watson for Drug Discovery’, an artificial intelligence-based drug discovery solution.
- In order to increase the reliability of the model, it is necessary to collect medical big data, which may be difficult to obtain because it contains a lot of sensitive personal information.
- Due to the nature of deep learning, it is difficult to explain the algorithm and it may be difficult to apply the model to a wide range of drug development as it is suitable only for specific diseases.
- VeriSim is currently conducting in-vitro testing for 5,000 compounds, animal testing for 100 new drug candidates, and clinical trials for 10 new drug candidates, with the goal of reducing this in half through pharmacodynamic modeling and simulation.
- However, the technical difficulty is high, as in the case where IBM stopped the development and sale of ‘Watson for Drug Discovery’, an artificial intelligence-based drug discovery solution.
- In order to increase the reliability of the model, it is necessary to collect medical big data, which may be difficult to obtain because it contains a lot of sensitive personal information.
- Due to the nature of deep learning, it is difficult to explain the algorithm and it may be difficult to apply the model to a wide range of drug development as it is suitable only for specific diseases.
3. Future Prospects
- Lowering R&D costs and faster drug discovery can accelerate the production of personalized medicines and broaden the scope of health benefits.
- In addition to discovering candidate substances, AI can also be used to discover new efficacy of existing drugs or to predict side effects of drugs.
- Lowering R&D costs and faster drug discovery can accelerate the production of personalized medicines and broaden the scope of health benefits.
- In addition to discovering candidate substances, AI can also be used to discover new efficacy of existing drugs or to predict side effects of drugs.
Source: VeriSIM Life
👉• Web: http://verisimlife.com/
👉• Video: https://youtu.be/84sGt00_qOk
0 Comments