Intology 推出自动化研究系统 Locus，分子预测超过 GPT-6 Astra 与 Claude…

Locus 为自动化分子设计树立了前沿标杆 我们的自动化研究系统 Locus 开发出超越 SOTA 专家解决方案的科学 AI 模型 释放科学 AI 的潜力需要利用专有数据。Locus 能够自主实现这一点 🧵👇 -- 我们在多个与分子评分和设计相关的任务上测试了 Locus：为一项专有任务训练分子性质预测模型、微调 Boltz-2 以预测小分子效价，以及围绕 BoltzGen、Boltz-2、ESMC 和 ESMFold2 构建抗体设计工作流。 上述每一种场景都是真实世界的 AI for science 用例，其中的任务对现有基础模型而言往往处于分布之外，且数据有限，同时评估器昂贵且噪声较大。与针对这些任务进行优化的最先进基线和 LLM 智能体相比，Locus 取得了更高的性能。此外，尽管数据有限，Locus 的泛化能力更好，能找到样本效率更高的解决方案，并探索出更多样化的解决方案。

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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.