Nevermore: Target-Conditioned Protein–Ligand Representation Learning for Multi-Objective Lead Optimization with Database-Grounded Retrieval 1. Nevermore introduces a novel framework for multi-objective lead optimization that combines protein–ligand affinity prediction with ADMET constraints, leveraging a database-grounded approach to ensure chemical validity and interpretability. 2. The framework utilizes a geometry-aware protein–ligand affinity oracle, which aligns protein and ligand representations under contrastive objectives, improving over previous benchmarks and providing a stronger scoring signal for downstream optimization. 3. Nevermore optimizes in count-based Morgan fingerprint space, enabling discrete and interpretable edits to molecular structures. This approach allows for sparse, integer-constrained modifications that can be directly mapped to chemically meaningful substructures. 4. A key innovation is the use of Nevergrad for derivative-free optimization in fingerprint space, coupled with nearest-neighbor retrieval from a large compound library to convert optimized fingerprints into valid molecules without exhaustive enumeration. 5. Evaluations on Menin and SARS-CoV-2 Mpro targets demonstrate that Nevermore consistently retrieves candidate sets with improved affinity–property trade-offs compared to random sampling and similarity-based retrieval, maintaining explicit control and interpretability through discrete feature-space edits. 6. The study highlights the importance of balancing affinity optimization with ADMET constraints, showing that Nevermore can identify meaningful Pareto improvements even for challenging targets like Menin, which has a PPI-like pocket geometry. 7. Structural analysis of optimized ligands reveals that Nevermore introduces or strengthens multiple substructure motifs aligned with the intended edits, providing a plausible binding rationale and supporting the framework's ability to enhance interaction quality. 8. The approach is fast enough for large library settings and offers transparency by linking discrete fingerprint edits to specific chemical motifs, making it a practical tool for medicinal chemistry decision-making. 📜Paper: biorxiv.org/content/10.64898… #Nevermore #LeadOptimization #ProteinLigand #MultiObjective #DatabaseGrounded #DrugDiscovery #ComputationalBiology