Andrew Hedin & Marla Jalbut, M.D. & Grace Dai — Bessemer Venture Partners - Building biology-native data infrastructure for the AI era - April 2026 logo

Andrew Hedin & Marla Jalbut, M.D. & Grace Dai — Bessemer Venture Partners - Building biology-native data infrastructure for the AI era - April 2026

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Biology-native data infrastructure for the AI era in drug discovery

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Type
Open Source
Company
Bessemer Venture Partners

About Andrew Hedin & Marla Jalbut, M.D. & Grace Dai — Bessemer Venture Partners - Building biology-native data infrastructure for the AI era - April 2026

This article from Bessemer Venture Partners provides a market map and analysis of biology-native data infrastructure for the AI era in drug discovery. It covers the need for data infrastructure, three principles of biology-native infrastructure, agentic AI across R&D workflows, and closed-loop lab automation. It highlights key players like Insilico Medicine, Isomorphic Labs, and NOETIK, and discusses the trend of AI labs (e.g., Anthropic) acquiring biotech startups. The article is authored by Andrew Hedin, Marla Jalbut, M.D., and Grace Dai, and was published in April 2026 on Bessemer's Atlas blog.

Key Features

Biology-native data infrastructure principles
Agentic AI across R&D workflows
Closed loop lab automation
Market map of AI drug discovery ecosystem
Insights from venture capital perspective

Pros & Cons

Pros
  • Provides a comprehensive overview of the AI drug discovery landscape
  • Written by experienced venture capitalists with domain expertise
  • Includes specific case studies (e.g., Insilico Medicine, Isomorphic Labs)
  • Highlights emerging trends like agentic AI and lab automation
Cons
  • Not a hands-on tool or platform; purely analytical article
  • May lack technical depth for practitioners implementing solutions
  • Focuses on early-stage and venture-backed companies, not established platforms

Best For

Drug discovery and developmentTarget identification and validationMolecule design and optimizationClinical trial candidate selectionBiologics and small molecule design