100 % Deterministic Spectral Retrosynthesis at Industrial Scale via Topological First Principles

100 % Deterministic Spectral Retrosynthesis at Industrial Scale via Topological First Principles | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Research Article 100 % Deterministic Spectral Retrosynthesis at Industrial Scale via Topological First Principles Andrés Sebastián Pirolo This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-8514617/v1 This work is licensed under a CC BY 4.0 License Status: Posted Version 1 posted You are reading this latest preprint version Abstract Current retrosynthetic tools function as single-purpose "black boxes" trained on specific reaction classes. We present PROMETHEUS (PRedictive Optimization via MOlecular THEology and Unified Spectral methods), a zero-shot spectral algorithm operating in three distinct modes: (1) Strategic Retrosynthesis for heteroatom disconnections, (2) Cross-Coupling Classification identifying Suzuki-type bi-aryl linkers requiring organometallic synthesis, and (3) Irreducibility Diagnostics flagging molecules unsuitable for retrosynthetic planning. Validated on 249,455 drug-like molecules (ZINC15), PROMETHEUS achieved 100% classification accuracy across all three modes (93.94% Mode 1, 6.0% Mode 2, 0.06% Mode 3) in 6.7 minutes on a standard CPU (619.8 molecules/second). The algorithm autonomously rediscovered Nobel Prize-winning cross-coupling logic without training data, demonstrating that molecular reactivity classification emerges from topological principles encoded in the Fiedler vector. Compared to transformer-based methods, PROMETHEUS requires 400 million times less storage (5 KB vs 2 TB) and 148 times less silicon (3B vs 443B transistors), enabling deployment on commodity hardware for industrial-scale virtual screening. Bioinformatics Retrosynthesis Spectral Graph Theory Molecular Classification Suzuki Coupling Fiedler Vector Green AI Zero-Shot Learning Cross-Coupling Drug Discovery Graph Laplacian Computational Chemistry Full Text Additional Declarations The authors declare no competing interests. Cite Share Download PDF Status: Posted Version 1 posted You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. 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