Drylab Raises $3 Million to Automate Bioinformatics With AI Agents
Drylab announced Monday it closed a $3 million seed round led by Vertex Ventures Southeast Asia & India, with participation from PatSnap, the innovation intelligence platform. The Singapore and San Francisco-based startup declined to disclose its valuation.
The company builds what it calls autonomous research agents capable of running scientific workflows across more than 800 integrated bioinformatics tools and databases. It's a pitch that has attracted early paying customers from pharmaceutical companies, cancer centers, biomedical research institutes and universities scattered across the US, Europe and Asia, according to statements from the company.
Drylab's platform differs from conventional bioinformatics software in its degree of autonomy. Rather than requiring researchers to manually chain together analysis steps, the AI agents are designed to understand scientific objectives, formulate research strategies, select appropriate tools, execute analyses and interpret results. When something breaks, they troubleshoot on their own. The system runs within customer infrastructure, keeping proprietary data and intellectual property behind corporate firewalls.
The startup offers both a web platform and a desktop application for local-first analysis, allowing data to remain in secured environments. Its tool catalog spans protein structure and design, molecular docking, molecular dynamics, genomics, single-cell analysis and quality control. Examples include OpenFold3, TankBind, Kallisto GPU, OpenMM and Flye.
Behind the company are three co-founders with complementary technical backgrounds. CEO John Phan previously conducted AI research with ByteDance's Seed team. CTO Huy Phan worked as a visiting research engineer at Andrew Ng's Landing AI. Chief scientific officer Nhi Nguyen is completing a PhD in biomedical science at Nanyang Technological University. LinkedIn data indicates the startup currently employs between two and 10 people, a small team for a company claiming to orchestrate hundreds of scientific tools.
Founded in 2025, Drylab now plans to deploy the fresh capital toward hiring across engineering, biology and sales. It also intends to pursue obtaining security and compliance certifications that large research organizations typically require before adopting new software. The company's website states alignment with AICPA SOC Type 2 and ISO 27001 frameworks, though no third-party certificates are publicly displayed yet.
Pricing follows the familiar software-as-a-service playbook, with tiers from Individual through Enterprise. Higher tiers include high-performance computing and cloud integration, on-premises deployment, and GPU specifications reaching H100 and H200 for enterprise customers. Public pricing, however, is not listed.
Drylab enters a market where incumbents have already begun staking claims in autonomous laboratory systems. Danaher, the life sciences conglomerate, announced an AI-powered autonomous lab initiative in October 2026. A Tufts Center for the Study of Drug Development analysis circulated last year suggested that AI agents could shorten cancer trial development by roughly 10 weeks and trim late-stage costs by up to $5.6 million.
Whether those projections hold in practice remains to be seen. Bioinformatics has long been a field of fragmented tools and competing standards, and integrating 800 of them reliably is no small engineering feat. Still, the investor appetite is real. PatSnap, the deal participant and fellow Vertex portfolio company, itself raised a $300 million Series E led by SoftBank Vision Fund 2 and Tencent back in March 2021.
"Our vision is to bring that capability into the daily work of a highly regulated industry like biopharma, through a swarm of autonomous AI agents," Phan said in a statement.
Genping Liu, general partner at Vertex Ventures SEAI, echoed the transformative framing that has become common in AI-for-science investing. "Just as AI transformed software engineering, AI for Science is set to reinvent laboratory research," Liu said.
Whether Drylab can deliver on that vision at scale will depend on factors the startup has yet to fully prove: reliability across diverse research environments, the ability to handle edge cases that trip up automated systems, and whether life sciences organizations will trust black-box agents with mission-critical research. For now, the company has capital and early customers. The hard part comes next.
