Intology's Locus automates molecular design and beats Codex, Claude Code
Autonomous research agents that beat generalist coding agents on narrow, data-scarce scientific tasks signal where vertical AI is heading: closed loops on private data rather than broader model scale. Labs and biotech teams running property prediction or antibody workflows can benchmark Locus-style pipelines against their own expert-built setups. The comparison with Codex and Claude Code gives practitioners a concrete reference point for agent capability limits.
Intology introduced Locus, an automated research system that builds scientific AI models without expert intervention. Locus was tested on three tasks: training molecular property prediction models on proprietary data, fine-tuning Boltz-2 for small-molecule potency prediction, and building antibody design workflows around BoltzGen, Boltz-2, ESMC, and ESMFold2. The company reports Locus outperforms state-of-the-art baselines and generalist LLM agents such as Codex and Claude Code on generalization, sample efficiency, and solution diversity. Intology positions proprietary and limited data as the key unlock for closed-loop AI-driven scientific discovery.
Closed-loop discovery is starting to look real. This is autonomously optimizing models for molecular property prediction, potency, and antibody design on proprietary/limited data and beating generalist agents (Codex, Claude Code) on generalization and sample efficiency. So cool Intology @intology Locus sets the frontier for automating molecular design Our automated research system Locus develops scientific AI models that outperform SOTA expert solutions Unlocking the potential of scientific AI requires leveraging proprietary data. Locus achieves this autonomously 🧵👇 -- We test Locus across a number of tasks related to molecular scoring and design: training molecular property prediction models for a proprietary task, fine-tuning Boltz-2 to predict small-molecule potency, and building antibody design workflows around BoltzGen, Boltz-2, ESMC, and ESMFold2. Each of these settings are real-world AI for science use cases, where tasks are often out-of-distribution for existing foundation models and limited in data, while featuring expensive and noisy evaluators. Compared to state-of-the-art baselines and LLM agents set to optimize these tasks, Locus achieves higher performance. Additionally, Locus generalizes better despite limited data, finds more sample efficient solutions, and explores a higher diversity of solutions. 🔗 View Quoted Tweet 💬 2 🔄 0 ❤️ 6 👀 1514 📊 2 ⚡
