Agentic AI platforms for autonomous training and rule induction of human-human and virus-human protein-protein interactions

Computer Science > Artificial Intelligence arXiv:2604.23924 (cs) [Submitted on 27 Apr 2026] Title:Agentic AI platforms for autonomous training and rule induction of human-human and virus-human protein-protein interactions Authors:Hung N. Do, Jessica Z. Kubicek-Sutherland, Oscar A. Negrete, S. Gnanakaran View a PDF of the paper titled Agentic AI platforms for autonomous training and rule induction of human-human and virus-human protein-protein interactions, by Hung N. Do and 3 other authors View PDF Abstract:We instruct an AI agent to construct two separate agentic AI platforms: one for autonomous training of predictive ML models for human-human and virus-human PPI, and the other for inducing explicit general rules governing human-human and virus-human PPI. The first agentic AI platform for autonomous training of predictive ML models for PPI is designed to consist of five AI agents that handle autonomous data collection, data verification, feature embedding, model design, and training and validation on three-way protein-disjoint cross-fold datasets. For human-human and human-virus PPIs, the final three-way protein-disjoint ensemble achieves an accuracy of 87.3% and 86.5%, respectively. For cross-checking and interpretability purposes, the second agentic AI platform is designed to replace ML predictions with human-readable rules derived from protein embeddings, physicochemical autocovariance descriptors, compartment annotations, pathway-domain overlap, and graph contexts. For human-human PPI, it is defined by a two-rule induction, whereas human-virus is induced by a more complex set of weighted rules. The rules induced by the second agentic platform align with the SHAP-identified features from the predictive ML models built by the first agentic platform. Taken together, our work demonstrates the agentic AI's ability to orchestrate from data planning to execution, and from rule induction to explanation in ML, opening the door to various applications. Comments: Other correspondence email: [email protected] Subjects: Artificial Intelligence (cs.AI); Biomolecules (q-bio.BM) Report number: LA-UR-26-23090 Cite as: arXiv:2604.23924 [cs.AI] (or arXiv:2604.23924v1 [cs.AI] for this version) https://doi.org/10.48550/arXiv.2604.23924 Focus to learn more arXiv-issued DOI via DataCite (pending registration) Related DOI: https://doi.org/10.5281/zenodo.19701990 Focus to learn more DOI(s) linking to related resources Submission history From: Hung Do [view email] [v1] Mon, 27 Apr 2026 00:47:35 UTC (3,664 KB) Full-text links: Access Paper: View a PDF of the paper titled Agentic AI platforms for autonomous training and rule induction of human-human and virus-human protein-protein interactions, by Hung N. Do and 3 other authorsView PDF view license Current browse context: cs.AI < prev | next > new | recent | 2026-04 Change to browse by: cs q-bio q-bio.BM References & Citations NASA ADSGoogle Scholar Semantic Scholar export BibTeX citation Loading... BibTeX formatted citation × loading... Data provided by: Bookmark Bibliographic Tools Bibliographic and Citation Tools Bibliographic Explorer Toggle Bibliographic Explorer (What is the Explorer?) Connected Papers Toggle Connected Papers (What is Connected Papers?) Litmaps Toggle Litmaps (What is Litmaps?) scite.ai Toggle scite Smart Citations (What are Smart Citations?) Code, Data, Media Code, Data and Media Associated with this Article alphaXiv Toggle alphaXiv (What is alphaXiv?) Links to Code Toggle…

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