Kim, D. H.; Khenmedekh, G.-O.; Park, i.; Kim, S.

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) alpha-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 an approximately 8.46 x 10^8-molecule mirror to a 10,000,000-molecule active set, representing an approximately 85-fold reduction before scoring, and 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, and Herbacetin, together with an epigallocatechin gallate (EGCG) positive control, were then tested in a thioflavin-T (ThT) aggregation assay at 100 uM. All three engine-nominated candidates suppressed alpha-synuclein aggregation, giving perfect prospective inhibitor-call concordance, 3 of 3 nominated candidates. Together with the EGCG positive control, all four assayed compounds inhibited aggregation, and two produced at least 80 percent 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.