Autonomous wet-lab experimentation requires more than plausible protocol text: biological intent, quantitative procedures, device constraints and experimental feedback must remain aligned from protocol and SOP design to code and physical execution. We developed ProtoPilot, a self-evolving multi-agent system, together with an expert-grounded benchmark and evaluation framework for testing this conversion as an experimental automation problem. The framework spans 294 synthetic-biology and molecular-biology tasks derived from 98 gold-standard protocols, wet-lab expert rubrics, device-level validity gates and real experimental tests. ProtoPilot incorporates layer-wise verifiability, multi-agent orchestration and a runtime-updated skill library to generate protocols, expand SOPs, synthesize SDK-compliant code and revise workflows from wet-lab feedback. It achieved a Top@3 expert-preference rate of 90.2%, an overall protocol-to-code gate pass rate of 89.5% and an Opentrons pass rate of 88.24%, compared with 32.35% for OpenTrons-AI. Wet-lab validation produced interpretable readouts, Sanger-confirmed products and feedback-corrected PCA-assembled DNA targets, establishing a verifiable route to autonomous experimentation. Together, these results show that the evaluation framework captures execution-relevant requirements for autonomous wet-lab automation, and that ProtoPilot can meet them by converting protocol and code generation into validated execution and feedback-guided revision.
Yankai Jiang, Wei Tang, Haoran Sun et al.· arXiv.org· 0 citations
Biomolecular binder design for peptides and antibodies requires generating diverse candidates that satisfy stringent three-dimensional geometric constraints while enabling affinity-oriented exploration under strong structural priors. In current generative models, the effective search space for structurally feasible binders is severely constrained, as the complexity of biochemical interactions is not explicitly encoded into a semantically grounded representation of viable molecular manifolds. To address this challenge, we propose Pretrained Representation Induced Molecular gEneration (PRIME), a unified generative framework for three-dimensional binder design across peptides and antibodies. PRIME grounds stochastic generation on frozen large-scale pretrained structural representations, inheriting robust physical priors to ensure structural feasibility without training a manifold from scratch. However, defining a feasible space alone is insufficient for effective exploration. Under commonly used isotropic perturbations, chain topology is ignored, allowing local noise to propagate into global structural distortions. To enable controlled exploration within the feasible space, we introduce Semantics-Preserving Exploratory Sampling (SPES), which integrates Graph Laplacian Spectral Noise to respect chain connectivity and Conditional Freedom Modulation to dynamically balance exploration with fidelity. By aligning stochastic exploration with structural semantics, PRIME enables diversity-enhanced generation without sacrificing geometric validity under the reported structural metrics, improving the empirical exploration--fidelity trade-off. PRIME achieves state-of-the-art performance on unified peptide and antibody benchmarks, effectively reconciling geometric validity with functional optimization under computational proxy metrics. The source code is available at https://github.com/simplaj/PRIME.
Zhihua Tian, Jiale Zhou, Rubo Wang et al.· Proceedings of the 32nd ACM...· 0 citations