<span class="word">Exploratory <span class="word allCaps">AI-<span class="word"><span class="changedDisabled">Assisted <span class="word allCaps">ML <span class="word"><span class="changedDisabled">Screening <span class="word">of <span class="word">ZINC15 <span class="word"><span class="changedDisabled">Compounds <span class="word">as <span class="word"><span class="changedDisabled">Potential <span class="word"><span class="changedDisabled">Bacterial <span class="word"><span class="changedDisabled">Signaling <span class="word"><span class="changedDisabled">Modulators: <span class="word"><span class="changedDisabled">A “<span class="word"><span class="changedDisabled">Signaling <span class="word"><span class="changedDisabled">First, <span class="word"><span class="changedDisabled">Killing <span class="word"><span class="changedDisabled">Later” <span class="word"><span class="changedDisabled">Proof <span class="word">of <span class="word"><span class="changedDisabled">Concept <span class

This technical note reports an exploratory, AI-assisted in silico proof of concept implementing a “signaling first, killing later” discovery paradigm: prioritizing compounds with high predicted affinity for bacterial quorum sensing (QS) pathways, then refining them for bactericidal potency. Using Claude Opus 4.6 (Anthropic), a custom SMILES-based descriptor calculator (170+ features) and a four-model ensemble (Random Forest, Gradient Boosting, SVM-RBF, Logistic Regression) were trained on 150 compounds (87 QS modulators, 63 negatives), achieving cross-validated AUC of 0.954 ± 0.024. Screening 218 ZINC15 CEBB tranche compounds identified 101 Tier 1 hits (46.3%), of which 91.1% were nitroaromatic. Bioisosteric modifications rescued 9/15 analogs (60%) as PAINS-clean. An orthogonal antibiotic-likeness model (44 antibiotics vs. 49 non-antibiotics, AUC = 0.809) identified a diacetyl hexahydroxytriphenylene prodrug as dual-high (P_QS = 0.849, P_Abx = 0.876). Six iterative optimization cycles across two phases—structural alert reduction followed by scaffold simplification—produced the final lead M6-12 (SMILES: CNCc1c(F)cc(OC)c2c(OC)c3C(O)CNCC3c(O)c12), a partially saturated fluorinated piperidine-fused tricyclic scaffold. M6-12 achieved: dual-high ML convergence (P_QS = 0.928, P_Abx = 0.792, Joint = 0.735, 4/4 ABX models &gt;0.5), zero PAINS, zero Brenk alerts, zero violations across all five drug-likeness filters, zero CYP inhibition (SwissADME 0/5, pkCSM 0/7), AMES-negative, high GI absorption, and “Very soluble” classification. RDKit validation confirmed: MW = 340.40, Crippen LogP = 0.48, TPSA = 82.98 Ų, HBD = 4, HBA = 6, Fraction Csp3 = 0.647. ChEMBL similarity: 0% at 95% threshold. Property-space MIC estimation: 2–32 μg/mL (Gram-positive), 1–11 μg/mL (Escherichia coli), 33–333 μg/mL (Pseudomonas aeruginosa), with 5/5 Richter rule compliance for Gram-negative penetration. A single pkCSM hepatotoxicity flag—contextualized by zero CYP inhibition, AMES-negative status, and low lipophilicity—probably constitutes the principal limitation requiring in vitro resolution. The signaling-first approach may enrich for molecules operating within biologically relevant chemical spaces, potentially offering a reduction in attrition compared to conventional MIC-first screening. All results require experimental validation.