Antimicrobial peptides (AMPs) are a promising solution to antimicrobial resistance, yet generative models for their design cannot control the physicochemical properties and motifs that shape activity and selectivity. Here, we present OmegAMP, a conditional diffusion framework controlling net charge, mean hydrophobicity, and sequence length, supporting de novo, analog, and motif-guided design. Across 204 wet-lab characterized peptides, de novo generation yielded antimicrobials with broad activity against multidrug-resistant Gram-negative isolates. Analog generation converted six inactive prototypes into antimicrobials, with the prototype determining each analog’s membrane-disruption mode and mammalian-cell safety. Motif-guided analog generation preserved lipopolysaccharide engagement of active prototypes, and a redesigned non-antimicrobial leucine zipper acquired antimicrobial activity while retaining DNA-perturbing character in vitro. In murine skin and thigh infection models, leads reduced bacterial burden, with a motif-guided DNA-perturbing lead matching the fluoroquinolone control systemically. OmegAMP opens a programmable route to new peptide antibiotics whose mechanism and safety follow from the chosen prototype.
Paulina Szymczak, Marcelo Der Torossian Torres, Diogo Soares et al.· bioRxiv· 0 citations
Detecting anomalous inputs is a critical prerequisite for the safe deployment of reinforcement learning (RL) agents in real-world environments. Although anomaly detection (AD) has been extensively studied in other domains, its application to reinforcement learning remains challenging due to high-dimensional sensory observations and complex temporal dependencies. Existing approaches in this setting are limited and often rely on access to internal representations of trained agents, creating an undesirable coupling between policy and safety mechanisms. In this work, we propose ITRM, a novel approach to anomaly detection in visual reinforcement learning that is fully agent-agnostic and does not require access to policy internals. Our method is based on the observation that deviations from nominal environment dynamics can be identified through discrepancies in the latent representations of a learned world model. Specifically, we leverage predictive components of a recurrent state-space model to generate deterministic latent embeddings that serve as normative references for anomaly detection. Anomalies are detected by comparing predicted latent features against a reference set of nominal embeddings using a similarity-based criterion. Operating in the world model’s latent space allows the detector to capture semantically meaningful deviations. Extensive empirical evaluations and ablation studies demonstrate that the proposed approach achieves strong detection performance. On the Anomaly-Gym benchmark, our method outperforms existing baselines, achieving an average AUROC of 0.853 and an FPR95 of 0.279.
Tom Haider, Karsten Roscher, Stephan Günnemann· Machine-mediated learning· 0 citations
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