Title: De Novo Antibiotic-Like Molecule Design via Descriptor-Guided Patch-Based GANs
Primary area: generative models
Keywords: Antibiotic Discovery, Generative Adversarial Networks (GANs), Molecular Graph Generation, Descriptor-Guided Learning, Beta-Variational Autoencoder (Beta-VAE), Patch-Based Graph Synthesis, Multi-Property Optimization, Mode Collapse Robustness, In-Silico Docking, QSAR Validation, ADMET Profiling

Abstract: The rise in antimicrobial resistance necessitates rapid discovery of novel antibiotics, but the majority of generative pipelines are unable to produce clinically viable candidates. The structural complexity of actual antibiotics is not captured by current models, which lack pharmacological depth and are frequently trained on datasets such as QM9 and ZINC. We present a modular, descriptor-guided framework for antibiotic-like molecule design that combines a property-aligned $\beta$-VAE for interpretable encoding, a descriptor-to-latent conditioner for controllable sampling, and a patch-based graph generator for fragment-wise synthesis. Trained on a curated subset of ChEMBL containing clinically validated antibiotics, our framework supports end-to-end generation from RDKit descriptors to final molecules, with adversarial refinement for structural realism. Beyond favorable ADMET profiles, our method establishes joint satisfaction reporting of drug-likeness thresholds as a benchmark standard, resists mode collapse under 50k-sample stress tests, and surfaces ligands that outperform ciprofloxacin and co-crystal references in docking assays. These results highlight chemically meaningful, pharmacologically informed generation $-$ overcoming limitations of black-box pipelines and general-purpose datasets.

TL;DR: We propose a descriptor-guided, patch-based GAN that generates antibiotic-like molecules, establishes joint satisfaction as a benchmark, resists mode collapse, and surfaces ligands surpassing ciprofloxacin in docking assays.