pre-IPOStandigm

Standigm designs new drug candidates end-to-end with workflow AI

Standigm's AI platform goes from disease target to first-in-class lead compound — mining biomedical data, designing novel molecules, and partnering with pharma to carry them toward the clinic.

تستخدم ستانديغم الذكاء الاصطناعي لاكتشاف أهداف دوائية جديدة وتصميم مركّبات رائدة من البداية حتى المرشّح الدوائي، بالشراكة مع شركات الأدوية العالمية.

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  1. The Bottleneck

    What was broken?

    Developing one new drug takes 10+ years and about ₩1 trillion ($890M) because target discovery and lead design still run on slow, failure-prone trial and error.

  2. The Move

    Why it worked

    Built full-stack workflow AI — target identification, drug repositioning, literature mining, generative molecule design — so pharma partners buy validated candidates and joint programs, not just software seats.

  3. The Trap

    Battle scars

    AI-designed molecules must still survive animal and human trials; the company has stayed private since its 2021 pre-IPO round; and global AI-drug-discovery rivals compete for the same pharma partnerships.

The problem

Traditional drug discovery burns a decade and roughly ₩1 trillion per approved therapy, with most candidates dying in trials. When CEO Kim Jin-han co-founded Standigm in May 2015 after three years at Samsung’s Advanced Institute of Technology, AI-based discovery was barely known — the inefficiency he wanted to kill.

How it works

Standigm’s workflow AI covers the early pipeline end to end: literature mining and target identification, drug repositioning, and generative design of novel small molecules via its STELLA platform. Its database stores biomedical knowledge and public data that the models learn from. For the hard cases, STELLA-MGD designs molecular glue degraders — drugs that mark disease proteins for destruction — with selectivity engineered in rather than left to chance, validated in design-test-analyze loops with experimental partners like Protai.

Pain points

Disease-causing proteins long deemed “undruggable,” degrader side effects from poor selectivity, and the cost of finding out a candidate fails only after years of work.

Business model

Pharma-facing: paid joint-discovery programs plus licensing and milestone-style deals on AI-generated candidates, keeping a first-in-class internal pipeline as leverage.

Challenges

Every candidate still faces wet-lab and clinical attrition no algorithm can waive; fundraising shifted from the planned 2021 KRX IPO to staying private; and Insilico-style rivals plus pharma in-house AI teams crowd every deal.

Funding

  • Raised: $11.5M Series B (2019, Mirae Asset-led); $8.6M from SK Holdings (Nov 2019); $44.5M pre-IPO round (Mar 2021, co-led by SKS PE and Daishin Private Equity with KDB Bank, Kakao Ventures, SK Holdings and others) — its last disclosed round, intended to fund B2B sales and FDA IND filings.
  • Valuation: MISSING.

Latest — September 2026

Standigm and Protai launched a joint R&D program to design selective molecular glue degraders, pairing Protai’s structural-proteomics platform with STELLA-MGD under KORIL-RDF joint-foundation support over two years — targeting proteins previous drugs couldn’t touch.

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