HealthTech

Hoth Therapeutics Joins NVIDIA Connect to Boost AI Research

Hoth Therapeutics Joins NVIDIA Connect to Boost AI Research

Hoth Therapeutics, Inc. (NASDAQ: HOTH), a clinical-stage biopharmaceutical company focused on developing breakthrough therapeutics today announced that it has been officially accepted into the NVIDIA Connect Program, a global initiative supporting innovative software and technology companies working with advanced computing and AI platforms.

Acceptance into the NVIDIA Connect Program provides Hoth Therapeutics with access to a suite of accelerated computing resources, technical guidance, and marketing support, enabling the company to further strengthen its AI-powered research initiatives across its therapeutic pipeline.

Strategic AI Integration to Enhance Hoth’s R&D Capabilities

Through the NVIDIA Connect Program, Hoth Therapeutics will gain access to:

  • NVIDIA GPU-accelerated developer tools to enhance computational-biology workflows, including target identification, protein-structure modeling, and preclinical data analytics.
  • Technical expertise, SDKs, and APIs that can support algorithmic modeling across Hoth’s programs, including HT-001 (pruritus reduction), HT-KIT (mast-cell modulation), HT-ALZ (GDNF-based Alzheimer’s therapy), and its metabolic-disease initiatives developed in partnership with leading academic institutions.
  • Co-marketing and technology enablement opportunities, increasing visibility of Hoth’s AI-driven approach to drug development.

Positioning Hoth for Accelerated Time-to-Market

Participation in the Connect Program is expected to streamline key aspects of Hoth’s R&D strategy, reducing computational bottlenecks and enhancing predictive modeling.

“Acceptance into the NVIDIA Connect Program is a meaningful milestone for Hoth,” said Robb Knie, Chief Executive Officer of Hoth Therapeutics. “NVIDIA is a global leader in accelerated computing and AI, and this partnership will enable us to leverage cutting-edge technology to enhance our drug-development programs, increase modeling speed, and improve the efficiency of our preclinical and clinical decision-making.”

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