Ai derivation and exploration of antibiotic class spaces

Abstract

Purpose: The rapid evolution of antibiotic-resistant bacteria poses an urgent global health crisis. A key gap in current antibiotic discovery approaches is the absence of automated chemical synthesis methods designed to systematically generate and evaluate compounds within specific antibiotic classes. We address this gap through fragment-based computational experiments that systematically explore antibiotic chemical spaces.

Methods: Our computational methodology consists of three steps: fragmentation of known compounds (eMolFrag), generating new molecular structures by recombining fragments (eSynth), and filtering of candidates based on desired properties (eFilter). eFilter combines structural analysis, pathway information, and protein targets to predict pharmacokinetic properties and therapeutic efficacy. We conducted three experiments: historical reconstruction of penicillin derivatives, hybrid molecule design combining functional groups from multiple antibiotic classes, and chemical space exploration of recently discovered antibiotics.

Results: Starting from Penicillin G and Methicillin, eSynth generated over 1.4 million potential penicillin derivatives. eFilter computationally predicted ampicillin, amoxicillin, and 10 other penicillin derivatives as high-scoring candidates, demonstrating that the pipeline can navigate the chemical space of $\beta$ -lactam antibiotics. For hybrid molecules, 1.53% showed computational predictions suggesting broad-spectrum activity against penicillin and quinolone targets, showing predicted binding scores higher than reference antibiotics in all protein targets evaluated. Chemical space exploration successfully generated computational candidates resembling Halicin-like molecules, with the top compound showing a binding score of 13.4 against JNK1.

Conclusions: Our fragment-based pipeline demonstrates the feasibility of systematically exploring antibiotic chemical spaces through computational reconstruction of historical development pathways and generation of hybrid molecules with with predicted multi-target binding profiles. All results represent computational predictions requiring experimental validation.

Keywords: In silico analysis; Antibiotic discovery; Antibiotic resistance; Chemical space; Fragment-based drug design; Historical reconstruction; Hybrid antibiotics; Machine learning; Pharmacokinetics; Protein targets.