Phoenix, a decentralized AI elastic compute platform, this month is set to support AI models for drug discovery, life sciences, and computational biology via its AI scaling and elastic compute layer SkyNet. The rollout will first start with Google Deepmind’s AlphaFold, a breakthrough AI system for predicting protein structures and protein folding, which has been traditionally a daunting and slow task without AI and deep neural networks. AlphaFold is being used in transformative ways across pharmaceutical, scientific, and genetic research domains across use cases such as AI drug discovery, vaccine development, rare disease research, etc.
This will enable academic researchers, pharmaceutical companies, and biotech startups, to deploy and use AlphaFold’s AI capabilities seamlessly, with no overhead cost, and with little technical expertise required through SkyNet. SkyNet will effectively provide the AI compute, scaling, workflow tools, and quick deployment features specifically catered towards AlphaFold. This effectively will significantly lower the barrier to entry for any individual, developer or organization seeking access to this once resource and cost-intensive technology.
Previously cloud-based deployment and scaling solutions for AlphaFold were scarce, with Google’s Vertex AI being one of the only cloud-based automation and scaling solutions. With this development, SkyNet is set to become a decentralized alternative that provides a workflow and infrastructure suite for AlphaFold users utilizing an elastic compute per-use unit cost approach.
Phoenix’s core development has hired a new team specifically for AI-driven life sciences use case, funded predominantly by its $20 million AI Ecosystem Fund announced earlier in March. This team includes computational biology and AI experts that will work with pharmaceutical, academic, and life science partners to maximize the adoption of AlphaFold using SkyNet’s compute infrastructure.
Leadership at Phoenix have noted that AlphaFold is a significant AI use case within the life sciences umbrella, but not the only one. Federated learning, a privacy-compliant variation of decentralized AI, and other models, may be added in the future to support various healthcare-related bioscience use cases where patient data privacy is a core issue.
Phoenix currently is building an AI super-platform focused on delivering value across select vertical AI use cases, currently including markets & trading, predictive analytics, AIGC, and machine vision. Our new AlphaFold automation solution shows that we also strongly believe in the future of AI-enabled computational biosciences”, said XP Lee, Director of Partnerships at Phoenix.
AboutPhoenix
Phoenix is a decentralized AI elastic compute infrastructure, and smart auto-scaling network for AI.
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