Intology 称其自动化科研系统 Locus 在分子设计领域达到前沿水平，覆盖分子性质预测、基于 Boltz-2 的效力预测和抗体设计工作流。在留出测试数据上，Locus 表现优于 Claude Code 和 Codex，且能在有限数据下自主将 Boltz-2 等模型适配到私有靶点。Locus 自主提出实验想法并在专用基础设施上并行运行多组实验，依据评估反馈选择下一轮实验。
Highly recommended read.
Very few companies I know have achieved this leap in scaling autonomous AI R&D in science.
Intology claims their Locus system sets the frontier for automating molecular design.
That covers molecular property prediction, potency prediction with Boltz-2, and antibody design workflows.
What’s the challenge with automating AI R&D?
With so little data and noisy measurements, it's easy for an agent to overfit and fail on new molecules.
On held-out test data, Locus held up better than Claude Code and Codex.
Under the hood, Locus proposes experiment ideas and runs several of them in parallel on purpose-built infrastructure. It picks the next round of experiments based on evaluation feedback.
Proprietary data is where this gets really interesting.
Models like Boltz-2 are trained mostly on public data. Real drug discovery runs on private targets and chemical series that can look very different from that training data. Locus adapts these models to new targets with limited data on its own, work that usually needs expensive ML expertise.
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.在 X 查看被引用的帖子
来源：elvis · x.com