Desktop-Scale Hit-Point Discovery for Intrinsically Disordered α-Synuclein Using State-Space Compression and a Discrete Phase-Interference Search Operator

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) -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 ~8.46x108-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 {micro}M: all three engine-nominated candidates suppressed -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.