State-Space Compression Enables Desktop-Scale Hit Discovery for Intrinsically Disordered α-Synuclein

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Abstract The accessible chemical space dwarfs any tractable screening budget, and most artificial intelligence drug discovery pipelines respond by docking and ranking a small sublibrary. The resulting hit list is agnostic to selectivity, brain penetration, toxicity, synthetic accessibility, and chemical novelty. We present ISTP-DPISO DrugEngine, an end-to-end engine developed by ISTP Tech that integrates the Local Information Criticality Principle (LICP) with a Discrete Phase-Interference Search Operator (DPISO). We demonstrate the engine on the intrinsically disordered protein (IDP) α-synuclein, whose non-amyloid-component (NAC, residues 61–95) drives Parkinson-associated aggregation. The resulting LICP active set focuses the expensive LICP‑DPISO scoring: in a production‑scale run, the engine compressed a ~ 8.46×10⁸‑molecule mirror to a 10,000,000‑molecule active set (~ 85‑fold) before scoring, then converged to a compact, safety‑gated shortlist plus de novo designs. The entire campaign ran on a single desktop workstation, without any high‑performance‑computing cluster. Three engine‑prioritized, commercially available candidates (2‑D08, Uralenol, Herbacetin) and an (−)-epigallocatechin gallate (EGCG) positive control were then tested in a thioflavin‑T (ThT) aggregation assay at 100 µM: all three engine‑nominated candidates suppressed α‑synuclein aggregation, giving perfect prospective inhibitor‑call concordance (3/3 nominated); together with the EGCG positive control, all four assayed compounds inhibited aggregation (4/4 total), two by ≥ 80% plateau reduction. ISTP‑DPISO DrugEngine reframes virtual screening from post‑hoc score fusion to a single, state‑space‑compressed, safety‑gated, experimentally validated discovery pipeline.

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