Google Cloud Launches AI-powered Solutions to Safely Accelerate Drug Discovery and Precision Medicine
公司规模
Large Corporate
国家
- United States
产品
- Target and Lead Identification Suite
- Multiomics Suite
- Vertex AI
- Google Cloud Storage
- BigQuery
技术栈
- AlphaFold2
- NVIDIA's Parabricks
- Google's DeepVariant
- Compute Engine
- Looker
实施规模
- Enterprise-wide Deployment
影响指标
- Digital Expertise
- Innovation Output
- Productivity Improvements
技术
- 分析与建模 - 机器学习
- 分析与建模 - 预测分析
- 平台即服务 (PaaS) - 数据管理平台
适用行业
- 生命科学
- 药品
- 医疗保健和医院
适用功能
- 产品研发
- 质量保证
用例
- 数字孪生
- 预测性维护
- 远程资产管理
服务
- 云规划/设计/实施服务
- 数据科学服务
- 系统集成
关于客户
Cerevel, Colossal Biosciences, and Pfizer are prominent players in the pharmaceutical and biotech industries, each with a unique focus on advancing drug discovery and precision medicine. Cerevel is known for its work in neuroscience, aiming to develop treatments for neurological and psychiatric disorders. Colossal Biosciences, on the other hand, is a cutting-edge biotech company focused on de-extinction and genetic engineering, leveraging advanced genomic technologies to bring back extinct species and enhance biodiversity. Pfizer, a global pharmaceutical giant, is renowned for its extensive portfolio of medicines and vaccines, with a strong emphasis on research and development to address a wide range of health challenges. These companies are at the forefront of innovation, utilizing AI-powered solutions from Google Cloud to accelerate drug discovery processes and enhance precision medicine capabilities. By adopting these advanced technologies, they aim to streamline research, reduce development timelines, and ultimately bring novel therapeutics to market more efficiently.
挑战
Speeding up target and lead identification is critical for the race to drug discovery. Currently, developing a new drug from an original idea to the launch of a finished product is a complex process that can take 12–15 years and cost more than $1 billion, according to the British Journal of Pharmacology. In addition, identifying a biological target involved in the disease that is viable for drug intervention can take up to 12 months (NIH, National Center for Biotechnology Information). At the same time, most companies use X-ray crystallography and nuclear magnetic resonance (NMR) to determine protein 3D structures, but this has a high ratio of failures. Finally, once the drug discovery process is underway, it's not easy to scale supporting technology up or down based on demand.
解决方案
Google Cloud's Target and Lead Identification Suite enables biopharma companies to bring therapeutics to market faster by enabling more efficient in silico drug design. Its target identification will help companies quickly predict antibody structures, assess the structure and function of amino acid mutagenesis, and accelerate de novo protein design. This solution also enables lead optimization that can be used to discover novel, high-quality candidates at low cost for Quantitative Structure Activity Relationship (QSAR) studies or for Free Energy Perturbation (FEP) calculations. The Target and Lead Identification Suite includes data ingestion, target identification using AlphaFold2 and Vertex AI pipelines, and lead identification with cost-effective high-performance computing resources. The Multiomics Suite advances precision medicine care by transforming multiomics data into insights to advance scientific discoveries. Organizations can use this solution to streamline and accelerate analysis of genomic data, design clinical genomics, accelerate personalized medicine, and interpret genomic data to unlock new discoveries. The solution also provides structure and processes for researchers and data scientists to collaborate, saving time on developing net new paths, algorithms, or methods. What sets the Multiomics Suite apart is that it is cloud agnostic, allowing organizations to leverage existing investments in multiomics in a simplified environment. It also offers complete traceability through Vertex AI, so customers can organize millions of artifacts in their cloud environments.
运营影响
数量效益
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