# VIDRAFT's Open Discovery Challenge: Claude vs. GPT-4 on Malaria & TB Drug Candidates — Early Benchmark Data

> Source: <https://dev.to/ai_openfree_b23025ef075cf/vidrafts-open-discovery-challenge-claude-vs-gpt-4-on-malaria-tb-drug-candidates-early-2p0i>
> Published: 2026-08-24 23:01:23+00:00

TL;DR:Korean AI startup VIDRAFT launchedOpen Discovery Challengeon Hugging Face — an open-format drug discovery competition where participants use any frontier LLM (Claude, ChatGPT, Gemini, DeepSeek, etc.) to propose real candidate molecules for malaria and tuberculosis. Early 3-day data shows Claude-family submissions scoring 43.7 vs. OpenAI-family at 31.7 on the malaria track, with the same ranking order repeating on the TB track. Developers and ML engineers can participate directly on Hugging Face right now.

**Open Discovery Challenge** is a publicly hosted, open-enrollment drug discovery benchmark created by VIDRAFT and published on Hugging Face. Unlike traditional fixed-dataset benchmarks — where models are evaluated on held-out prediction accuracy — this challenge is *generative and competitive*: participants freely explore chemical space using whichever AI model they choose, then submit actual candidate molecules for scoring.

Key structural facts (from public reporting):

The choice of malaria and TB as initial targets is deliberate. Both diseases carry enormous global burden — particularly in low-income populations — yet face a well-documented structural R&D gap: low commercial returns deter private pharmaceutical investment, a problem the WHO has flagged for decades. VIDRAFT is using open AI competition to lower the barrier to generating novel candidate compounds for these neglected disease areas.

At a conceptual level, the challenge follows an **open-ended generative evaluation loop**:

This is a meaningful architectural departure from static benchmarks. Rather than testing whether a model can predict a known answer, it tests whether AI-assisted generation can surface *novel, plausible preclinical candidates* — a far harder and more practically relevant task.

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Caveat:The exact scoring algorithm, weighting of sub-metrics, and specific model versions used by submitters have not been publicly disclosed by VIDRAFT. The numbers below reflect early-stage data.

These are the publicly reported early figures, captured from the first **3 days** of challenge activity:

| Track | AI Family | Median Score |
|---|---|---|
| Season 1 — Malaria | Claude family | 43.7 |
| Season 1 — Malaria | OpenAI family | 31.7 |
| Season 2 — Tuberculosis | Claude family | 39.9 |
| Season 2 — Tuberculosis | OpenAI family | 30.9 |

**What to take away — and what to hold loosely:**

The challenge is publicly accessible on **Hugging Face**. Based on public reporting, you can find it by searching for VIDRAFT or "Open Discovery Challenge" on the Hugging Face platform:

```
# Browse or search on Hugging Face directly
# huggingface.co — search: "Open Discovery Challenge" or "VIDRAFT"
```

No specific dataset slug, model card URL, or API endpoint has been officially confirmed in the public sources available at time of writing. Visit [huggingface.co](https://huggingface.co) and search for the challenge name to find current submission instructions, scoring criteria, and the live leaderboard.

Participation requires no lab equipment — only an account on Hugging Face and access to at least one LLM (commercial API or self-hosted).

**Q: What does the challenge score actually measure?**

A: Based on VIDRAFT's public description, scores aggregate simulated estimates of drug efficacy, toxicity, target-binding affinity, and ADME properties for submitted candidate molecules. The precise sub-metric weights and scoring algorithm have not been publicly released.

**Q: Can I participate using an open-source or self-hosted model?**

A: Yes — the challenge explicitly lists custom/proprietary AI as a valid tool alongside commercial frontier models. Any AI-assisted molecular generation approach is in scope.

**Q: Are the results statistically conclusive?**

A: No, and VIDRAFT acknowledges this. The figures come from the first three days of an ongoing competition with a limited initial sample size. Treat them as early directional data, not a peer-reviewed benchmark conclusion.

**Q: Will VIDRAFT expand to other disease targets beyond malaria and TB?**

A: The source article indicates this is a possibility under consideration, but no confirmed additional targets or timelines have been announced publicly.

*Originally reported by 비젠메디컬 (2026-08-23) — source article.*