Pharmacological Lattice Quantisation without Number-Theory

This dataset supports a two-layer computational platform for neglected-disease drug discovery, focused on antimicrobial resistance and malaria: (A) a ligand-based drug-triage and de-risking platform, and (B) a structure-based generative design pipeline (SBDD) built on top of it. It provides cleaned, reproducible, honestly-evaluated bioactivity data together with the results needed to judge model reliability, plus the full structure-based pipeline and the candidate molecules it produced. Data were derived from two open resources: ChEMBL (bioactivity, CC BY-SA 3.0) and the Open Targets Platform (human target-disease genetics, CC BY 4.0). Bioactivity records were standardised to a p-scale (p = -log10 of the molar concentration; pIC50 for binding assays, pMIC for whole-cell antibacterial assays, converting ug/mL via molecular weight), deduplicated by InChIKey with replicate measurements aggregated by median, and canonicalised with RDKit. Each compound carries a Bemis-Murcko scaffold split (train/test) so the held-out evaluation can be reproduced exactly, with no analog leakage between splits. Building on the ligand layer, a pathogen-agnostic structure-based pipeline discovers essential-but- undrugged protein targets and designs candidate binders for them. For a chosen pathogen it: (1) finds targets that are essential (real gene-essentiality data - BV-BRC for bacteria; PlasmoDB piggyBac Mutagenesis Index Scores for Plasmodium), undrugged, host-selective (no close human homolog), and druggable (site-anchored pocket hydrophobicity/enclosure), with resistance barriers from CARD; (2) fetches the AlphaFold structure and UniProt catalytic residues; (3) scores small-molecule binding with a positive-control-validated Boltz-2 co-folding oracle returning a binding probability and a 3D pose - validated per target against known drug/target pairs (e.g. DHFR/methotrexate 0.998 vs decoy 0.13; A. baumannii FabI/triclosan lifted 0.577->0.912 in the FabI+NAD+ ternary complex), where rigid docking (AutoDock Vina) failed the same control and the oracle is shown deterministic (sigma ~0.003); (4) checks whether the co-folded pose engages the catalytic site; (5) generates candidate molecules with a Markov-chain / fragment-crossover / substrate-growing generator seeded from each target's native substrate or inhibitor pharmacophore, under a baked-in reactive-group/PAINS/charge/catechol/ thiocarbonyl filter that prevents oracle-gaming (documented and rejected artifacts: boron and thiolate exploits); and (6) diagnoses the binding ceiling as a search, oracle, target, or cofactor limit (e.g. the FabI ceiling was pre-registered as cofactor-limited and confirmed by the NAD+ lift).