Iterative discovery of potent polymeric antibiotics via multi-stage and multi-task learning against antimicrobial resistance

Domain: chemistry
Published: 2026-01-21
Editorial outcome: major_revision

Abstract
Drug-resistant bacterial infections pose a serious threat to global health, driving the development of antibacterial strategies beyond classic antibiotics. Host defense peptide mimetic polymeric antibiotics have emerged as promising candidates to combat drug resistance, however, navigating the vast chemical space of polymers remains a significant challenge due to complex structure–activity relationships, while data-driven approaches are further constrained by polymer complexity and scarce labeled data. To address this, we develop PolyCLOVER, a framework that integrates multi-stage self-supervised learning, active learning, and high-throughput experimentation to iteratively discover polymeric antibiotics with potent antibacterial activity and low toxicity. Applied to a combinatorial library of ~100,000 poly(β-amino ester)s, the framework uncovers three lead compounds that self-assemble into stable nanoparticles (SANPs) with minimum inhibitory concentrations of 4 μg/mL and 8 μg/mL against multidrug-resistant S. aureus and A. baumannii, respectively. These SANPs also serve as adjuvant antibiotic carriers, restoring bacterial sensitivity to penicillin G. In vivo studies demonstrate their therapeutic efficacy both as monotherapies and in combination therapies with antibiotics. PolyCLOVER may become a powerful framework for discovery of new polymeric biomaterials without reliance on external datasets.

Introduction
Antimicrobial resistance (AMR), particularly of bacterial origin, poses an escalating global health threat, with 4.71 million associated and 1.14 million attributable deaths reported in 2021. Current strategies to combat bacterial AMR primarily focus on two approaches: developing novel antibiotics to bypass resistance, and employing combination therapies to restore efficacy, including combinations of existing antibiotics and co-administration of antibiotic adjuvants. With antibiotic discovery becoming increasingly challenging, host defense peptides (HDPs), or antimicrobial peptides (AMPs), have attracted attention as alternative therapeutics owing to their ability to disrupt membranes, modulate immunity, and regulate inflammation, thereby slowing resistance development. However, their clinical translation is limited by inherent biological toxicity, poor in vivo stability, and high production costs. In this context, synthetic polymeric antibiotics inspired by AMP mechanisms can achieve comparable antimicrobial efficacy, while their flexible and scalable synthesis addresses the limitations of AMPs in stability and manufacturability. Moreover, their broad chemical design space enables antibacterial activity as standalone agents and synergistic effects as antibiotic adjuvants, together with extendable capabilities such as drug loading, barrier penetration, and targeted delivery. These characteristics make synthetic polymeric antibiotics a particularly promising platform for next-generation antimicrobial design.

Current strategies for polymeric antibiotic discovery remain predominantly empirical, relying on modulation of charge and amphiphilicity coupled with inefficient trial-and-error screening. The complexity of antimicrobial mechanisms and the structural diversity of candidate polymers hinder mechanistic understanding, limiting the elucidation of structure–activity relationships and posing a major barrier to the systematic exploration of this vast chemical space. Deep learning (DL), with its capacity to model complex, non-linear relationships, has emerged as a powerful tool for data-driven prediction and optimization. DL has achieved notable success in AMP discovery, including our previous work on empirical 6–9-mer peptide sequences, and has further accelerated omics research by improving the throughput and efficiency of genome and proteome mining. Despite this, DL applications to polymeric antibiotics remain scarce, primarily due to challenges in data availability, structural diversity, and molecular representation. AMPs, derived from diverse natural sources and extensively studied, are supported by large, labeled datasets (typically >10^5 sequences). Their conserved backbone and standardized sequence representation further facilitate model learning. Polymeric antibiotics, by contrast, suffer from limited and fragmented data resources; existing data often vary in synthesis conditions, characterization protocols, and structural distributions, making it difficult to support the development of generalizable models. The high tunability of polymer backbones and side chains further complicates feature extraction and demands greater model generalization, while current representations remain inadequate for capturing their structural complexity. Nonetheless, this flexibility presents a unique opportunity to design multifunctional polymeric antibiotics.

Herein, we propose a comprehensive framework (PolyCLOVER) for the design of polymeric antibiotics with integrated adjuvant effects to combat antibacterial drug resistance. To overcome the challenges posed by data availability, structural diversity, and molecular representation, we employ two critical designs in the framework. 1) A graph-based polymer foundation model, developed in our recent work, is applied to capture the intricate structural characteristics of polymer systems, thereby enhancing predictive performance. 2) Multi-stage self-supervised learning, active learning, and high-throughput experiments are integrated to form an autonomous evolution workflow, eliminating the need for a pre-established dataset. Within a combinatorial library, PolyCLOVER can identify polymeric antibiotics that exhibit both potent antibacterial activity and low toxicity, enabling efficient navigation in a large chemical space without reliance on external datasets. The ensemble multi-task predictor recommends candidate combinations based on properties, diversity, and uncertainty, which are then labeled through experiments. The resulting data are used to update the pretrained model, which then predicts the next round of candidates. When applied to a combinatorial poly(β-amino ester) library comprising about 100,000 candidates, three polymers with outstanding antibacterial efficacy against a wide range of clinically isolated drug-resistant pathogens and low toxicity are successfully identified. These leading compounds self-assemble into stable nanoparticles (SANPs), which facilitate in vivo transportation and distribution. Their potent therapeutic efficacy as polymeric antibiotics is validated in a mouse pneumonia model. Importantly, the candidate polymer showed ultra-high encapsulation efficacy and loading capacity when co-assembled with penicillin G. The co-assembled NPs showed a synergistic antibacterial effect and restored the sensitivity of drug-resistant Gram-positive pathogens to penicillin G, demonstrating a synergistic therapeutic effect in a mouse peritonitis model. Taken together, the discovered polymers are promising candidates as next-generation dual-functional antibiotics, and suggest that PolyCLOVER can be a viable approach for the efficient discovery of polymeric nanomedicine.

Results
Multi-stage self-supervised learning boosts model performance on limited data
Previous studies have demonstrated that positive charges present in polymers can effectively interact with the negatively charged bacterial cell membrane, while groups such as alkyl chains can insert into the bacterial cell membrane and disrupt its normal physiological function, thereby achieving a bactericidal effect. Accordingly, positively charged and hydrophobic units were incorporated into polymers to impart potential antibacterial activity. We also introduced hydrophilic units and regulated the ratios of each component in a suitable range to promote the self-assembly of the polymers into stable SANPs. Guided by this design rationale, a combinatorial library comprising 98,304 (~100,000) poly(β-amino ester) SANPs was constructed via a multi-component Michael addition reaction.

Next, we randomly selected 220 samples from the constructed library for laboratory synthesis and testing of their antibacterial and hemolytic properties to form the initial dataset. The initial dataset spans a vast chemical space, incorporating all monomer types presented in the library. We chose Methicillin-resistant Staphylococcus aureus (MRSA) as the model pathogen because it stands as one of the most prevalent pathogens, capable of causing life-threatening diseases such as sepsis, pneumonia, and multi-organ failure. The antibacterial activity against MRSA and hemolytic behavior of the samples were quantitatively assessed using optical density (OD) values at 595 nm and the hemolysis ratio, respectively. The results reveal that the probability of identifying effective SANPs through random sampling in the extensive combinatorial library is very low. Most SANPs exhibit weak antibacterial activity, while a few samples with high antibacterial activity show high hemolytic toxicity. These findings underscore the considerable challenge of achieving a balance between high antibacterial activity and low hemolytic toxicity, highlighting the importance of ML in guiding the design of antibacterial SANPs.

After collecting the initial dataset, we employed graph neural networks (GNNs) for modeling, due to their strong capability in capturing molecular structural features. However, the intrinsic complexity of copolymers, particularly the absence of a well-defined and consistent topology, makes it challenging to represent them using the vanilla molecular graph. To enable effective message passing between subgraphs corresponding to individual building blocks, virtual nodes were introduced as communication bridges, following our previously reported approach. In addition, three local virtual nodes were connected to their respective subgraphs to encode the copolymerization ratios. We evaluated the average test performance of 10 commonly used GNN backbones on antibacterial and hemolytic tasks and finally selected a graph transformer with multi-head attention.

Self-supervised learning has demonstrated remarkable success across various domains of artificial intelligence by leveraging intrinsic structural information in large-scale unlabeled data, enabling models to learn general representations that can be effectively transferred to downstream tasks. This paradigm significantly reduces reliance on manual annotations and improves model generalization. To this end, we employed a periodicity-aware pretraining strategy to learn rich and generalizable representations from a large corpus of unlabeled general polymer data. Building on this, our pool-based screening strategy enables the predictive model to access candidate SANPs that will be evaluated during the inference phase. That is, the model is exposed to the candidate set during training, effectively establishing a transductive learning scenario. Therefore, inspired by domain-specific large language models, we here propose a two-stage pretraining approach, in which the model undergoes continual pretraining on the entire accessible unlabeled library of poly(β-amino ester)s to further exploit the structural priors inherent in the screening space and to bridge the gap between pretraining and fine-tuning domains. The ratio information of individual building blocks was extracted using a ratio embedding layer and assigned as the initial features of the corresponding virtual nodes.

Subsequently, we analyzed the model’s latent space to evaluate the effect of the two-stage pretraining. To visualize the learned polymer representations in two-dimensional space, we applied t-SNE—a widely used dimensionality reduction algorithm that preserves local similarities—to both the pretrained and unpretrained models. It can be observed that the representations generated by the pretrained model are evenly distributed in the latent space and exhibit clear clustering patterns, where structurally similar SANPs (the same diacrylates) show high consistency in their representations. In the unpretrained model, the representations are compressed into narrow regions, indicating limited structural awareness and reduced information retention. These results demonstrate that the pretrained model effectively captures key structural features while preserving maximal information.

The pretrained model was then fine-tuned on the initial dataset to predict the antibacterial and hemolytic properties of SANPs. We compared the test performance of the two-stage self-supervised learning approach with baseline methods. Due to the extreme scarcity of data, the randomly initialized model fails to capture structural patterns, resulting in poor performance. The weighted sum approach used ratio-weighted building block representations extracted by the pretrained encoder, followed by a prediction head. Its performance showed a slight improvement but remained inadequate. In contrast, our proposed approach achieves significantly better performance across all metrics, demonstrating its effectiveness under data-limited conditions. Moreover, the fine-tuned latent space reveals an ordered, continuous distribution that aligns with activity levels, reflecting better capture of the underlying structure–activity relationship. Therefore, we adopt this model as the base learner for subsequent structure optimization of SANPs.

PolyCLOVER drives iterative improvements in antibacterial activity and hemocompatibility
Although the model was optimized, the limited amount of training data still poses a significant challenge for generalizing across the full combinatorial library. To address this, PolyCLOVER constructs an ensemble predictor composed of 20 homogeneous base learners, which is then embedded into a Bayesian optimization-based active learning framework. This pipeline facilitates efficient exploration of the vast chemical design space of SANPs to identify structures with potentially optimal properties.

Then, the ensemble predictor was employed to predict the activities of unlabeled SANPs in the remaining combinatorial library. The mean prediction across base learners was used as the activity estimate, while the standard deviation served as an estimate of predictive uncertainty. Each candidate was then scored using the Upper Confidence Bound (UCB) acquisition function, which balances exploration of uncertain regions with exploitation of potentially optimal candidates. To further improve optimization efficiency, we applied the K-means algorithm to cluster the top-ranked SANPs and selected representative structures from each cluster to enhance data diversity. PolyCLOVER then recommended 60 candidate SANPs for wet-lab synthesis and characterization. The newly generated data were subsequently incorporated into the initial dataset to support the next round of optimization.

After four rounds of iterative optimization, the activities of the recommended SANPs plateaued, suggesting convergence of the active learning loop. At this stage, a greedy acquisition strategy was employed to further exploit the most promising candidates, without considering uncertainty or diversity. Compared to the initial dataset, the antibacterial activity of the selected SANPs showed a substantial improvement. Although the hemolytic rate increased slightly in the intermediate rounds due to the enhancement of antibacterial potency, it was successfully reduced to an acceptable level in the final round of optimization. As a result, a substantial number of SANPs identified in the final round exhibited both high antibacterial efficacy and low hemolytic toxicity.

To gain deeper insight into the model’s behavior, we further investigated the evolution of chemical structures and key physicochemical properties of the SANPs recommended by PolyCLOVER. In the first round, an increase in LogP indicated a shift toward higher hydrophobicity, suggesting that the model initially prioritized antibacterial activity. However, excessive hydrophobicity (e.g., from longer alkyl chains) was often associated with a higher potential for cell toxicity, the dual-objective task subsequently regulated this property to balance antibacterial activity and biocompatibility. Additionally, the initially low ratio of positive charges, which may have limited interactions with negatively charged bacterial membranes, was substantially increased during optimization. A sharp increase in the de-protected primary amine of B10 suggested that the model favored a short chain length of positive charge, a tendency that has also been noted in some other polymer systems. We also found the unique integration of polyfluorocarbon unit (C16) effectively preserved antibacterial efficacy while reduced toxicity, which aligned with previous studies. Nevertheless, this moiety falls under the category of per- and polyfluoroalkyl substances (PFAS), and warrants attention in practical applications.

To further identify SANPs, we established a preliminary criterion for high-potential candidates: an antibacterial optical density (OD) value of less than 0.15 and a hemolytic rate of less than 50%. All SANPs meeting this criterion were synthesized and purified in a single batch, followed by detailed evaluation of their antibacterial and hemolytic properties. The minimum inhibitory concentration (MIC) and half-hemolytic concentration (HC50) were determined for each polymer, and the selectivity index was calculated as the ratio of HC50 to MIC. The results showed that most SANPs exhibited strong antibacterial activity and low hemolytic toxicity, with selectivity indices exceeding eight. Finally, samples with a selectivity index greater than 20 were selected, resulting in the identification of three optimal SANPs. Notably, PolyCLOVER enabled the discovery of three highly antibacterial and low-toxic SANPs from a combinatorial library of ~100,000 polymer candidates in just 20 days, demonstrating its remarkable efficiency in accelerating the discovery of new functional materials within large design spaces.

Identified SANPs exhibit promising in vitro performance
The identified SANPs were designated as H1, H2, and H3. The positively charged amines (B) used in the formulation consisted of deprotected primary amines with short chains. The hydrophobic amines (C) featured either a relatively short tertiary alcohol or a short chain with fluorinated side groups, which likely mitigated the hemolytic toxicity associated with longer hydrophobic chains. The hydrophilic amines (D) included oligomeric polyethyleneglycol (PEG) side groups in H1 and H3, while H2 contained a unique amide side group. Notably, the proportion of hydrophilic components was the lowest among the three segments, while the proportion of positively charged groups was the highest, which is crucial for interaction with negatively charged bacterial cell membranes. Additionally, due to an excess of diacrylate during polymer preparation, all three SANPs retained double bond residues at both ends, allowing for potential future functional modifications. The MIC of all three SANPs were 8 μg/mL against MRSA ATCC 43300. Both the HC50 (H1=168 μg/mL, H2 > 512 μg/mL, H3=266 μg/mL) and the half-maximal inhibitory concentration (IC50; H1=53 μg/mL, H2=162 μg/mL, H3=109 μg/mL; assessed in L929 fibroblast cells) followed a consistent trend, which was also observed in the MLE-12 lung epithelial cell line, with H2 exhibiting the lowest toxicity among the three. These findings underscore the necessity of leveraging a dual-objective optimization strategy to achieve a balance between antibacterial efficacy and toxicity. The self-assembly properties of H1–3 were confirmed by dynamic light scattering (DLS) and transmission electron microscopy (TEM). Three SANPs exhibited particle diameters of ~100 nm, with H2 having a larger diameter compared to H1 and H3, and all three SANPs carried positive charges.

We further evaluated the bactericidal efficacy of the identified SANPs by benchmarking them against streptomycin (SM), a broad-spectrum antibiotic. After 9 h of co-incubation at 32 μg/mL, H1 and H2 reduced MRSA viability by 3 orders of magnitude, while H3 achieved a 4-order reduction. SM exhibited the strongest antibacterial effect, with a 5-order reduction. Meanwhile, when the tryptic soy broth (TSB) medium was replaced with fetal bovine serum (FBS), a more complex environment, we found that the SANPs maintained their antibacterial efficacy, with MIC values fluctuating by only 1-2 fold. This stability may be attributed to the biologically inert fluorinated and PEG side chains. Next, we assessed the bactericidal kinetics of the SANPs, which is another key metric in the practical application of antibacterials. All three SANPs demonstrated rapid bactericidal action. Notably, H1 and H2 eliminated 90% of bacteria within 60 min, while H3 achieved this reduction in just 4 min. In contrast, SM exhibited significantly slower killing kinetics. As an antibiotic that inhibits bacterial protein synthesis by targeting ribosomes, SM requires prolonged exposure, aligning with prior reports that antibiotics generally exert bactericidal effects over several hours or longer. These findings highlighted that the identified SANPs not only outperformed antibiotics in bactericidal speed but also likely operated via a different antibacterial mechanism. Furthermore, compared to SM, the SANPs exhibited a substantially lower tendency for resistance development. Over a 28-day testing period, the MIC values for H1–3 fluctuated modestly (1–4 fold), while SM showed a significant 28-fold increase in MIC. Among 11 clinically isolated drug-resistant pathogens, the SANPs exhibited antibacterial efficacy comparable to that observed in reference strains. In particular, the SANPs exhibited potent activity against all tested Gram-positive bacteria, whereas activity among Gram-negative strains was restricted to A. baumannii. These results indicate that the antibacterial spectrum of the SANPs is selective and biased towards Gram-positive pathogens.

Subsequently, we investigated the antibacterial mechanism of the identified SANPs. TEM images were obtained after 5 min of co-incubation with the identified SANPs. In the control group, the MRSA membrane maintained a clear and intact structure. In contrast, after interaction with the SANPs, significant membrane disintegration was observed, leading to the release of bacterial cell contents and resulting in bacterial death. Live/dead bacterial staining assays were performed after 5 min of exposure to SANPs H1–3. The assays employed N01, which penetrated both live and dead bacteria, binding to nucleic acids and emitting green fluorescence, while propidium iodide (PI) entered only damaged or dead bacteria, binding to DNA and emitting red fluorescence. Confocal fluorescence microscopy revealed intense red fluorescence in MRSA exposed to SANPs H1–3, even after a short incubation period. To assess their effect on bacterial membrane potential, we used the fluorescent probe DiSC3(5). This probe accumulates in intact bacterial membranes, leading to fluorescence quenching. When the membrane is disrupted, the probe leaks from the membrane, causing fluorescence emission as its concentration decreases. Bacterial suspensions without SANPs exhibited negligible fluorescence intensity. In contrast, the addition of Triton X-100, a strong surfactant, resulted in a significant increase in fluorescence emission due to membrane destabilization. SANPs H1–3 induced robust fluorescence signals, confirming their ability to disrupt bacterial membrane potential. SM, however, did not induce fluorescence, consistent with its non-membrane-targeting antibacterial mechanism. The membrane-attaching mechanism of the SANPs was further revealed by molecular simulations. All-atom molecular dynamics results demonstrate that when the SANPs approach the cell membrane, they rapidly adsorb to the surface. This process is primarily driven by electrostatic interactions. Analysis of these results suggests that the identified SANPs likely exert their bactericidal effects through strong interactions with the cell membranes of MRSA in a short timeframe.

Identified SANPs eradicate lung infection with high efficacy and biocompatibility
The therapeutic efficacy of the identified SANPs was further investigated in a mouse lung infection model. MRSA-induced acute pneumonia is among the most encountered lung infections, featuring a high incidence rate and significant mortality. Female mice were inoculated with MRSA to establish an acute pneumonia model by intratracheal instillation. The identified SANPs were administered via tail intravenous injection (IV injection) on the following day. Mice were euthanized one day post-treatment, and lung tissues were collected for morphological observation and bacterial counting. The PBS-treated group presented unhealthy dark red lung appearances. In contrast, the lungs of the identified SANPs-treated group appeared healthy and pink. Bacterial counts from tissue homogenates indicated that the SANPs-treated group demonstrated significant improvement, reducing bacterial counts by three orders of magnitude, comparable to the results observed in the SM-treated group. The results of H&E staining revealed that the alveolar structure was collapsed in the PBS treatment group. In contrast, it remained porous in the SANPs-treated group, indicating the relief of lung infection. Additionally, Masson staining showed no significant blue fiber deposition, further suggesting that H1–3 exhibited a favorable therapeutic effect. And CD68 staining demonstrated that H1–3 modulated the excessive inflammatory response.

To examine the distribution of the SANPs in mice post-injection, rhodamine B was conjugated to the SANPs through a reaction between isothiocyanate and the amine groups. Statistical results and fluorescence images of mouse organs were captured 2 h post-injection. The results revealed that most of the fluorescence was concentrated in the liver, with a notable presence in the lungs, indicating that the SANPs effectively reached lung tissue via the tail vein injection. Next, we assessed the tissue toxicity of the SANPs. Histological analysis of heart, liver, spleen, lung, and kidney samples from SANPs-treated mice revealed no significant tissue necrosis compared to the PBS control group. The lung sections exhibited a healthy, loose alveolar structure, and no damage was observed in the glomeruli or renal tubules of the kidney sections. In conclusion, the identified SANPs demonstrated no evident tissue toxicity.

Identified SANPs restore MRSA susceptibility to penicillin G
The above investigation demonstrated the intrinsic antibacterial activity and self-assembly properties of the identified SANPs. Furthermore, we evaluated the potential of the SANPs as antibiotic adjuvants. It was anticipated that abundant functional groups within the SANPs could be integrated with antibiotics to form nanocomposites, thus enhancing the penetration of antibiotics through bacterial membranes. MRSA resistance primarily arises from the acquisition of the mecA gene, which encodes penicillin-binding protein 2a (PBP2a). PBP2a has a significantly reduced affinity for β-lactam antibiotics, such as penicillin G (PG), making these agents largely ineffective against MRSA. The integrated nanocomposites held promise for bypassing this resistance barrier by facilitating more effective antibiotic delivery.

We demonstrated that the SANPs efficiently encapsulated PG, thereby reinstating the susceptibility of MRSA to this antibiotic. Checkerboard assays revealed a clear synergistic effect between the identified SANPs and PG, with all fractional inhibitory concentration index (FICI) values less than 1. Notably, the FICI value between H2 and PG reached 0.375. At sub-MIC concentrations of H2, the MIC of PG was reduced from 128 μg/mL to 4 μg/mL. At optimal ratios, the SANPs and PG formed stable spherical nanocomposites with diameters ranging from ~100 to 200 nm. TEM images obtained in the dry state clearly showed PG crystals encapsulated within the nanocomposites. Interestingly, in contrast to previous observations, the H2-PG nanocomposites exhibited the smallest size and a more uniform distribution among the three. Furthermore, H2 almost completely encapsulated PG, with a drug loading rate close to 50%, indicating a strong interaction between the two components. Molecular dynamics simulations further confirmed the presence of electrostatic interactions between H2 and PG, with a distinct assembly trend observed over 50 ns. Based on the above findings, we speculate that, due to the cationic and hydrophobic properties, the SANP nanocomposites can act as membrane-disruptive carriers that preferentially accumulate and integrate into bacterial membranes. This process leads to passive enrichment of PG at the membrane interface, thereby increasing its local concentration. At the same time, the membrane-disruptive effect of SANPs may generate additional cell wall fragments in the surrounding environment, which could kinetically facilitate the acylation of the active-site serine of PBP2a by β-lactams, ultimately overcoming the low-affinity resistance mechanism. For subsequent in vivo evaluations, we selected the H2-PG nanocomposite, which exhibited optimal synergy (FICI=0.375), the highest encapsulation efficiency (EE=97%), and a smaller particle size (106 nm).

To evaluate the in vivo therapeutic potential of H2 as a penicillin enhancer, we tested H2-PG nanocomposites in a lethal MRSA peritonitis model, comparing the results with treatments using H2 and PG alone. This model induces systemic diseases, such as sepsis, more readily than respiratory infection models, making it a stringent test for therapeutic strategies. We used a neutropenic mouse model, which is well-established for evaluating antibiotic efficacy. Immunosuppression in mice was induced by cyclophosphamide injection prior to infection. On day 4, mice were infected via intraperitoneal injection with MRSA (1×10^7 CFU), using a lab-induced strain highly resistant to PG in vitro (MIC > 64 μg/mL), followed by the administration of various treatments. After 24 h, the mice were euthanized. Blood and tissues (liver, kidney, spleen, and lung) were harvested for bacterial quantification. Treatment with H2 (10 mg/kg), PG (10 mg/kg), SM (10 mg/kg), and H2-PG nanocomposites (5 mg/kg) resulted in a reduction of the average bacterial loads in these organs by 1.2, 0.9, 2.3, and 2.5 log units, respectively, compared to untreated controls. This result suggests that in the context of MRSA infection, PG exhibits minimal therapeutic efficacy. In contrast, the H2-PG nanocomposites exhibited anti-infective efficacy comparable to that of streptomycin. Serum inflammatory cytokine analysis revealed a similar trend, with significant reductions in pro-inflammatory markers observed in the H2-PG nanocomposite-treated group. Histological examination through H&E staining further confirmed that the H2, SM, and H2-PG treatment groups significantly ameliorated tissue damage compared to untreated controls. Acute toxicity evaluation in healthy mice showed no significant changes in body weight, organ indices, or serum biochemical markers after treatment with H2 or H2-PG, indicating the absence of detectable systemic toxicity in vivo. These results collectively demonstrate that H2 has substantial potential as an antibiotic adjuvant, capable of restoring bacterial susceptibility to antibiotics.

Methods
Chemical synthesis of poly(β-amino ester)s
The step-growth polymerization of Michael addition produces linear polymers with ester and tertiary amine structures in their skeleton. In this study, reaction occurred between diacrylates and three types of amines to produce random copolymers. The polymer is formed by alternating diacrylate and amine units, with three types of amines randomly distributed along the chains. The molar ratio of diacrylate to amine was set at 1.2:1. Initially, the amine monomer solution (3 M in DMF) was pipetted to a reaction vial containing 48 μL of diacrylate (3 M in DMF) according to the specified ratio. The reaction mixture was then heated to 90°C and stirred magnetically for 24 h. For combinations with Boc-protected amine monomers, 80 μL of trifluoroacetic acid was added after polymerization. The solution was stirred overnight at room temperature to remove the protective group. Finally, the crude product was purified by precipitating in ether/dichloromethane (1:1 in vol%) solution three times. All samples involved in the screening process are summarized in Supplementary Data 1.

Antibacterial and hemolytic activity labeling
The synthesized and purified polymer was dissolved in DMSO at the concentration of 1.28 mg/mL. Fresh MRSA (ATCC 43300) was incubated in TSB overnight (120 rpm, 37°C). The bacterial suspension was then diluted and calibrated to a concentration of 10^5 CFU/mL. 195 μL bacterial solution was added to each well in the 96-well plate, followed by addition of 5 μL polymer solution (final concentration at 32 μg/mL). The bacterial suspension was further incubated at 37°C for 9 h. OD values at 595 nm of each well were by the microplate reader.
For the hemolysis assay, 4% red blood cell (RBC) suspension obtained from New Zealand White rabbits (purchased from Dr. Can Bioscience Inc., Hangzhou, Zhejiang, China) was used. First, 195 μL suspension was added to each well of the 96-well plate, and then 5 μL polymer solution was added to achieve a final polymer concentration of 128 μg/mL. The negative control group was added with 5 μL PBS, and the positive control group was added with 5 μL 0.4% Triton X-100. The plates were incubated at 37°C for 1 h. After incubation, the plates were centrifuged at 4°C (200×g, 10 min). After centrifugation, the 100 μL supernatant was gently transferred into a new 96-well plate. OD values at 576 nm were measured, and the hemolysis ratio was calculated according to the following formula:
Hemolysis ratio = (OD_sample - OD_negativecontrol) / (OD_positivecontrol - OD_negativecontrol) (1)

Dataset construction and preprocessing
In this study, a combinatorial polymer library of 98,304 poly(β-amino ester) SANPs is constructed via the Cartesian product of 8 diacrylates (A), 16 cationic amines (B), 16 hydrophobic amines (C), 6 hydrophilic amines (D), and 8 specific formulation ratios. Given the high-throughput nature of this study, direct characterization of actual monomer incorporation ratios for each candidate would be neither cost-effective nor necessary. Instead, feeding ratios—combined with chemical structures—are used as practical and consistent model inputs. This design enables efficient, data-driven prediction of biological activity, while maintaining alignment between training and inference conditions. The four-component Michael addition poly-condensation product is treated as a random arrangement of three building blocks (A-B, A-C, A-D). The structure of each building block is generated by the reaction module of the RDKit package according to the general formulas:
'[NH2:1][C;!$(C=O):6].[C:2]=[C:3][C:4]=[O:5] » [*]-[N:1]([C;!$(C=O):6])-[C:2][C:3][C:4]=[O:5]' and '[C:1]=[C:2][C:3]=[O:4] » [*]-[C:1][C:2][C:3]=[O:4]'.

The copolymers are further converted into graph representations. Specifically, repeating units of the copolymer is first transformed into three separate molecular line graphs {G_i}_{i=1}^3. The atom and bond features are summarized in Supplementary Tables S5, 6. The initialization of node features in the line graphs is defined as:
h_v^real = concat(W_v x_i + W_e x_{i,j}, W_v x_j) (2)
where W_v ∈ R^{0.5d×d_atom} and W_e ∈ R^{0.5d×d_bond} are trainable weights, d denotes the dimensionality of the embedding space, x_{i,j} is the feature of a bond represented as a node in the line graph, and x_i, x_j are the features of the atoms at its two ends. Then, a global virtual node v_global is introduced and connected to all nodes across subgraphs to facilitate inter-subgraph message passing. Three local virtual nodes {v_local_i}_{i=1}^3 are connected to their corresponding components to specify the ratios of individual building blocks {r_i}_{i=1}^3. The features of these virtual nodes are initialized according to the following equation:
h_v^virtual = embedding(r_i), if v ∈ {v_local_i}_{i=1}^3
h_v^virtual = h_global, if v = v_global (3)
where embedding: r_i → R^d is a trainable embedding function that maps discrete indices to continuous vector, h_global ∈ R^d is a trainable vector. Furthermore, to enrich the graph representation, we incorporate RDKit and Mordred descriptors of each repeating unit into each subgraph via additional virtual nodes {v_add_i}_{i=1}^3. As a result, we use G=(V,E) to denote the ensembled graph of poly(β-amino ester)s in the following.

Model construction
Model architecture. We selected graph transformer (GT) architecture as the backbone of neural network, specifically adopting LiGhT, due to its superior performance among ten commonly used GNN variants. To encode graph structural information into Transformer, Path Encoding (PE) and Distance Encoding (DE) are employed. Formally, the path feature a_p_{i,j} between v_i and v_j in the path attention matrix A_p ∈ R^{N_v×N_v} is defined as follow:
a_p_{i,j} = W_p (1/N_p) Σ_{n=1}^{N_p} W_p^n h_v^n (4)
where h^n represent the feature of the n-th node in the shortest path between v_i and v_j, N_p represents the number of nodes in the path, W_p ∈ R^{d_p×d} and W_p^n ∈ R^{1×d_p} are trainable weights. Similarly, the distance feature a_d_{i,j} between v_i and v_j in the distance attention matrix A_d ∈ R^{N_v×N_v} is calculated as follow:
a_d_{i,j} = W_d^2 σ(W_d^1 d_{i,j}) (5)
where d_{i,j} represents the distance between v_i and v_j, W_d^1 ∈ R^{d_d×1} and W_d^2 ∈ R^{1×d_d} are trainable weights. Based on the structural encodings, GT processes the graphs through stacked attention layers. For a given feature matrix H^{(l)} ∈ R^{N_v×d}, the backbone of GT updates node representations using the following formulation:
Q_k = H^{(l)} W_k^Q, K_k = H^{(l)} W_k^K, V_k = H^{(l)} W_k^V
A_k = softmax(Q_k K_k^> / sqrt(d_k) + A_p + A_d), O_k = A_k V_k (6)
H_attn = LayerNorm(concat(O_1, O_2, ..., O_h) + H^{(l)})
H^{(l+1)} = LayerNorm(W_2 σ(W_1 H_attn) + H_attn)
where W_k^Q, W_k^K, W_k^V ∈ R^{d×d_k} are trainable weights of k-th head, W_1 ∈ R^{4d×d}, W_2 ∈ R^{d×4d} are trainable weights of the feed-forward network, σ(·) is the GELU activation function, d_k = d/h is the dimension of each attention head, h stands for the number of heads. After the final GT layer, the learned node representations are aggregated into a graph-level embedding using a readout operator:
h_G = concat(mean({h_v^real}), mean({h_v^virtual}), mean({h_v^add})) (7)
Finally, the graph-level embedding h_G is then passed through a projection head to produce the final predictions:
y^ = W_proj^2 σ(W_proj^1 h_G) (8)
where y^ ∈ R^2 denotes the predicted antibacterial and hemolytic properties, W_proj^1 ∈ R^{d×4d} and W_proj^2 ∈ R^{2×d} are trainable weights.

Training objectives. In this work, we propose a multi-stage learning strategy designed to fully leverage the structural priors of polymers. This strategy consists of three sequential stages: (1) general polymer knowledge learning, (2) continual pretraining to capture structural features specific to poly(β-amino ester)s, and (3) fine-tuning for activity prediction.
In the first stage, we adopt the pretraining approach proposed in our previous work, performing SSL on about one million unlabeled structures to capture general knowledge of polymers. Specifically, given a batch of N polymers, Periodicity Augmentation is employed to generate different views as positive pairs (different head-to-tail orders and numbers of repeating units), while different polymers are treated as negative pairs. Next, random masking is applied to 50% of the nodes in the constructed graphs. 80% of the selected nodes are replaced with a special [MASK] token, 10% are replaced with random nodes, and the remaining 10% are left unchanged. We adopt a multi-task SSL strategy to capture the hierarchical structural features of polymers. A contrastive loss L_CL (InfoNCE Loss) is applied to pull positive pairs closer and push negative pairs apart, while a reconstruction loss L_recon (Cross Entropy Loss) is applied to recover the masked nodes:
L_InfoNCE(i) = -log(exp(z_i^> z_i^0 / γ) / (Σ_{k≠i} exp(z_i^> z_k^0 / γ) + Σ_{k=1}^N exp(z_i^> z_k^0 / γ)))
L_CL = L_InfoNCE(i) + L_InfoNCE^0(i)
L_pretrain = L_CL + L_recon (9)
where z_i and z_i^0 are the latent embeddings of two augmented views of the same polymer, γ is a temperature parameter, L_InfoNCE^0(i) denotes the symmetric loss L_InfoNCE(i) for the paired positive sample.
However, there exists a substantial gap between the pretraining dataset and the downstream fine-tuning domain. In this work, we propose to adopt a continual pretraining strategy, in which the graph encoder is further pretrained on the constructed unlabeled library of poly(β-amino ester)s to fully exploit the structural priors inherent in the screening space. In this stage, two independent random masks are applied to the same poly(β-amino ester) to form a positive pair, while different poly(β-amino ester)s serve as negative pairs. The pretraining objective remains consistent with Eq. (9).
After the two-stage pretraining, the model is fine-tuned to predict antibacterial and hemolytic activities. For this purpose, a randomly initialized projection head is attached to the pretrained graph encoder to output the predicted activity values. We optimize the model using the MSE Loss, where the objective is to minimize the difference between the predicted activities y^ and the experimentally measured activities y for each sample. The loss is defined as:
L_finetune = (1/N) Σ_{i=1}^N ||y_i - y^_i||^2 (10)

Training details. In pretraining stage, the model is trained using the Adam optimizer with a learning rate of 2×10^-4, a weight decay of 1×10^-6, and a batch size of 96 for 10,000 steps. In fine-tuning stage, the model is trained with a batch size of 32. Hyperparameters, including learning rate, dropout, and weight decay, are selected via grid search based on validation performance (Supplementary Table S7). The learning rate is dynamically adjusted using the PolynomialDecayLR scheduler. Early stopping with a patience of 20 epochs is employed based on validation performance. All experiments are implemented in PyTorch with DGL and trained on three NVIDIA RTX 4090 GPUs.

Evaluation metrics. To quantitatively assess model performance on each tasks, we employ the following metrics: root mean squared error (r.m.s.e.), mean absolute error (m.a.e.), Pearson coefficient (R), and top-n r.m.s.e. for test samples with the highest activity. These metrics are defined as follows:
r.m.s.e. = sqrt((1/N) Σ_{i=1}^N (y_i - y^_i)^2)
m.a.e. = (1/N) Σ_{i=1}^N |y_i - y^_i|
R = (Σ_{i=1}^N (y_i - y_bar)(y^_i - y^_bar)) / sqrt(Σ_{i=1}^N (y_i - y_bar)^2 Σ_{i=1}^N (y^_i - y^_bar)^2)
Top-n r.m.s.e. = sqrt((1/N) Σ_{i∈T_n} (y_i - y^_i)^2) (11)
where y is the measured value, y^ is the predicted value, y_bar is the mean of measured values, y^_bar is the mean of predicted values, T_n denotes the indices of the top-n test samples with the highest activity.

Active learning strategy
An active learning framework is applied over multiple rounds to optimize antibacterial and hemolytic properties of candidates, formulated as a dual-objective regression task. The optimization target for each sample is defined as a scalar score, computed as a weighted sum of the predicted antibacterial activity and hemolysis ratio:
s(x) = Σ_i λ_i y^_i (12)
where λ_i is the weighting coefficient that balances the relative importance of the two objectives. y^_i is the prediction for the i-th task. In this work, λ for antibacterial activity was empirically set to 0.7, while λ for hemolytic activity was set to 0.3. To estimate the model’s prediction μ(x) and uncertainty σ(x), an ensemble learning approach is employed. Specifically, 20 independent models are trained, and for each sample, the predicted mean and standard deviation are calculated from the outputs of the ensemble members. To select candidate SANPs in each round, the UCB acquisition function is employed, balancing exploitation of high-performing samples and exploration of uncertain regions. The acquisition function is defined as:
α_UCB(x) = μ(x) + βσ(x) (13)
where β is a hyper-parameter controlling the trade-off between exploration and exploitation (β=2).
In each round, K-Means algorithm is applied to group the top-ranked samples (γ=0.1) into 20 clusters. From each cluster, the top 3 samples based on are selected, yielding a total of 60 candidates (Supplementary Table S8). These samples are then experimentally tested for antibacterial activity and hemolysis ratio. After several iterations of this process, the entire library is predicted and ranked using the final model. The top 60 candidates, based on their predicted scores s(x), are synthesized and characterized again to validate the model’s predictive performance and assess the effectiveness of the active learning approach.

MIC assay. MRSA was incubated in TSB overnight (120 rpm, 37°C). The bacterial suspension was then diluted and calibrated to a concentration of 10^5 CFU/mL. Purified SANPs were dissolved in DMSO to a concentration of 2.56 mg/mL. 195 μL MRSA suspension was added to the 96-well plate, and 5 μL tested SANPs solution was added to each well. The SANPs concentration was serially diluted two-fold, ranging from 32 μg/mL to 4 μg/mL. The plate was incubated for 9 h. TSB solution served as the control group. The MIC was determined as the lowest concentration that resulted in a completely clear solution.

HC50 assay. RBC suspension (195 μL) was added to each well of a 96-well plate, followed by the addition of 5 μL of purified SANPs solution. The final SANPs concentrations tested were 8, 16, 32, 64, 128, 200, 256, 512 μg/mL, and with 5 μL PBS, and the respective positive control group was added with 5 μL 0.4% Triton X-100. The plates were incubated at 37°C for 1 h, then centrifuged at 4°C (200×g, 10 min). After centrifugation, 100 μL supernatant was transferred into a new 96-well plate. OD values at 576 nm were measured using a microplate reader, and the hemolysis ratio for each SANPs concentration was calculated. HC50 was determined by fitting the hemolysis ratio-concentration curve to identify the SANPs concentration at which 50% hemolysis occurred.

Bacterial strains. The model strain used for high-throughput screening, S. aureus ATCC 43300 (MRSA), was purchased from the Shanghai Bioresource Collection Center (Shanghai, China). For subsequent characterization assays, three reference strains (S. aureus ATCC 29213, S. epidermidis ATCC 35984, and A. baumannii ATCC 19606) and eleven clinically isolated drug-resistant pathogens, including S. aureus-MRSA, VISA, S. capitis-MRSC, E. faecalis-VRE, A. baumannii-MDRAB, P. aeruginosa-CRPA, K. pneumoniae-CRKP, were obtained from the Department of Infectious Diseases, Sir Run Run Shaw Hospital, School of Medicine, Zhejiang University.

Checkerboard assay. A checkerboard assay was performed to evaluate the synergistic effects of penicillin G sodium (PG) and the identified polymers (H1–3) against MRSA. The MICs of H1–3 were first determined, followed by the checkerboard assay conducted in 96-well microtiter plates. PG was serially diluted along the vertical axis from 128 μg/mL to 8 μg/mL, while the polymers were diluted along the horizontal axis: H1 and H3 ranged from 16 μg/mL to 1 μg/mL, and H2 ranged from 32 μg/mL to 2 μg/mL. The final row and column contained wells where both the polymer and PG concentrations were 0 μg/mL as controls. The plates were incubated at 37°C for 9 h, and all experiments were performed in triplicate to ensure reproducibility and reliability.

Preparation and characterization of H2-PG nanocomposites. The optimal formulation ratio for H2-PG nanocomposites was determined based on checkerboard assays. A stock solution of PG was prepared at 10 mg/mL in deionized water, while the polymer stock solution was 10.24 mg/mL in DMSO. Prior to use, the polymer solution was diluted with deionized water. For NC preparation, PG was diluted to 1 mg/mL in deionized water, and the polymer solution was diluted to the optimal concentration. Equal volumes of the PG and polymer solutions were mixed, vortexed for 30 s, and incubated at room temperature for 5 min to allow nanocomplex formation.
The hydrodynamic diameter of the NCs was measured using DLS on a Malvern Zetasizer Advance Pro (Blue Label). The zeta potential was also determined using the same instrument. The morphology of the NCs was examined by TEM.
The encapsulation efficiency (EE) and drug loading capacity (LC) of the antibiotic-loaded NCs were quantified using UV–Vis spectroscopy. NCs were prepared as described above and subsequently subjected to ultrafiltration using a 3 kDa molecular weight cutoff (MWCO) membrane to separate free PG from the nanocomplexes. The filtrate was collected and diluted appropriately, and its absorbance spectrum was recorded in the wavelength range of 185–220 nm using a UV–Vis spectrophotometer. Due to interference from the mixture, a slight shift in the position of the maximum absorption peak was observed; thus, the peak exhibiting the highest absorbance within this range was used to determine the concentration of unencapsulated PG. The EE and LC were calculated according to the following equations:
EE(%) = (W_total - W_free) / W_total × 100
LC(%) = (W_total - W_free) / (W_polymer + (W_total - W_free)) × 100 (14)
where W_total represents the initial total amount of PG, W_free is the total amount of unencapsulated PG quantified from the filtrate, and W_polymer + (W_total - W_free) corresponds to the total mass of the nanocomplexes.

Ethical statement
Mice were obtained and maintained in pathogen-free conditions at the Laboratory Animal Centre of Zhejiang University and raised at 20–22°C and 40–70% humidity, with a dark-light cycle of 12 h. All animal studies were performed in compliance with the guide for care and use of laboratory animals and adopted the protocol that has been approved by Institutional Animal Care and Use Committee of CDSER of Zhejiang University (IACUC-25-S257).

Mouse pneumonia model. On day 0, healthy female ICR mice (18–20 g) were anesthetized with isoflurane. MRSA suspension (10^8 CFU/mL, 60 μL) was administered intratracheally. Successfully infected mice were randomly divided into five groups (n=3). On day 1, polymers H1, H2, H3, and SM were administered via tail vein injection (5 mg/kg). PBS served as the control. On day 2, the mice were euthanized, their lungs were excised for morphological observation. The lung tissue was homogenized for further bacterial colony counts.

Lethal MRSA peritonitis model. A lethal MRSA peritonitis model was established in 20 male C57BL/6 mice (18–20 g), randomly assigned to five groups (n=4). Cyclophosphamide was administered intraperitoneally (150 mg/kg on day 1 and 100 mg/kg on day 3) to induce immunosuppression. On day 4, mice were intraperitoneally infected with 1×10^7 CFU of MRSA. Post-infection, mice received intraperitoneal treatments: H2 (10 mg/kg), PG (10 mg/kg), SM (10 mg/kg), H2-PG nanocomplex (5 mg/kg), or PBS (100 μL, control). After 24 h, mice were euthanized, and serum, spleen, lung, liver, and kidney samples were collected. Serum samples were used for the evaluation of inflammatory cytokine levels, while the harvested organs were analyzed for bacterial load and histopathological examination.

Statistics and reproducibility
Statistical analyses were performed using GraphPad Prism (version 10.1.2). One-way ANOVA followed by post hoc multiple-comparison tests was used. Data are presented as mean ± standard deviation (SD). Exact p values are shown on the figures. No statistical method was used to predetermine sample size, no data were excluded from the analyses.

Peer review rounds
[{"round": 1, "reviewer_comments": {"reviewer_1": "The manuscript entitled ‘Iterative discovery of potent polymeric antibiotics via multi-stage and multi-task learning against antimicrobial resistance’ describes a framework (PolyCLOVER) to discover by Deep Learning application novel potent and selective antimicrobial polymers that mimic the properties of Host Defence / Antimicrobial Peptides (HDPs / AMPs), among combinatorial libraries. The validity of this framework is demonstrated through the successful identification of 3 candidates from a library of approximately 100,000 poly(β-amino ester)s, which are subsequently evaluated, validated in vitro and in vivo, in mono and combination therapy studies, as antibiotic candidates and adjuvant antibiotic carriers, respectively.\nThe significance of these results is justified by the structural complexity that can be displayed by antimicrobial polymers, which are of interest to a broad community of researchers and for a number of important applications. They can present a broad diversity and fill a vast chemical space, while being peptidomimetics of AMPs, which have complex mechanisms of action and structure-activity relationships already. As a result, the traditional trial-and-error approach for the identification of novel candidates is particularly ineffective. In addition, model learning for antimicrobial polymers is complicated by other factors such as data availability and standardisation, as well as molecular representation, as explained by the authors. The successful identification by PolyCLOVER of 3 potent and selective candidates from ternary copolymers based on a poly-amino ester backbone in 20 days, is therefore significant.\nThe manuscript is well written and presented and discloses a comprehensive, well designed and executed study, including two in vivo models. As polymer antibiotics are more commonly developed as single agents (antibiotic candidates), the ability of the poly(β-amino ester)s identified in the present work to self-assemble into stable nanoparticles for the effective delivery of a classical antibiotic that synergise the two agents appears to extend the development of these polymers to a higher level than generally performed and to address an additional challenge as self-assembly of antimicrobial polymers can in fact result in a loss of antimicrobial activity.\nI cannot comment on the computational aspects of the work reported in this manuscript. However, from a general reader’s perspective, I find difficult to understand the following:\n- Are the poly(β-amino ester)s statistical or block copolymers? It would also be helpful to add (in supplementary information) some information on the reaction underlying the synthesis of these polymers, either a synthetic scheme or a reference describing this reaction;\n- Scope of the framework: PolyCLOVER appears extendable to polymer backbones other than poly(β-amino ester)s; if this is correct, is it also applicable to non-linear polymer architectures, such as branched, star-shaped, graft/bottlebrush polymers?\nSeparately, the following points require revision and/or clarification:\n- For the combinatorial library of 220 candidates and the subsequent one of 60 candidates constructed experimentally, the details of the synthetic procedure(s) should be provided in supplementary information. These should also clarify the ratios of monomers used; in figure S16, the feeding ratios for x, y and z are provided, but are they for (preformed?) A-B, A-C and A-D blocks? Also, are these feeding ratios matching the actual ratios of the selected candidates in the library, or do they account for the different polymerisation kinetics of the diverse monomers? In other terms, how is the correspondence between the computed and experimental ratios ensured?\n- Figure 4c and lines 305-306: Streptomycin, used as a reference/control, is an aminoglycoside; why are H1-H3, as polymeric mimics of AMPs, not benchmarked to a representative AMP?\n- Restored MRSA susceptibility to penicillin G: these are interesting results, that are difficult to understand at the molecular level, but which have been previously reported with another antimicrobial polymer mimic of AMPs (eg., DOI: 10.1021/acsinfecdis.0c00016). However, the interpretation of these results proposed by the authors, based on an increased affinity model, would benefit from additional details, proposed alternatives or evidences (experimental and/or from the literature). The hypothesis advanced is that the accumulation and integration of H2 nanoparticles in the bacterial membrane ‘leads to passive enrichment of PG at the membrane interface, thereby enhancing its affinity for penicillin-binding proteins (PBPs) and overcoming the low-affinity resistance mechanism’. But it has for example been proposed that the reduced binding of beta-lactam antibiotics to PBP2a is caused by a closed active site, so it is difficult to conciliate the effect of the polymer at the membrane level with an opening of the active site. On the other hand, a conformational change in this protein can induce this active sire opening, thereby restoring access to beta-lactam antibiotics (DOI: 10.1021/ja0434376). As the bacterial membrane can be involved in the PBP2a stabilisation, would a PBP2a conformational change induced by a membrane destabilisation and resulting in an opening of the PBP2a active site not be a potential alternative mechanism? On the other hand, have the authors some evidence that penicillin encapsulated in H2 remain stable, considering the presence of amino groups which could potentially react with the beta-lactam?\n- In would be interesting in the discussion to compare the properties of the copolymers identified by PolyCLOVER, with those of previously developed polymers (experimentally). For example, structurally, PolyCLOVER selected repeating units of the amino groups with short branching chains, while a number of antimicrobial peptides have been reported as benefiting from longer chains (eg., 4 methylene groups). The typical activities and selectivities could also be compared, although HC10 values are generally used, while HC50 values are reported here.\n- Figure S16: are the MW calculated from the NMR spectra, or otherwise? These NMR spectra should be complemented by GPC chromatograms to provide sufficient characterization of the copolymers for publication. Also, without integration of the proton NMR spectra, the characteristics of the copolymers cannot be verified. The chemical shift for protonated primary amine appears to be higher than usual in proton NMR? It is also a sharp signal, while the one attributed to protons f, which is not bound to a heteroatom is broad. There appears to be some residual DCM and diethyl ether in H2 and H3. In this case, because of the presence of diethyl ether, the signal at ~3.5ppm cannot be assigned, to protons j. If the latter signal is masked by diethyl ether, this should be clarified in the legend.\nPlease find here also some minor comments:\n- Line 296: ‘The MIC of all three SANPs were 8 μg/mL’; is it against MRSA ATCC 43300, as the results in figure 4g for LAC and 779 show different MICs?\n- In Figure 2a, it would be helpful to refer to table S1 for the codes A1, B1, …\n- Figure 2b, it would be helpful to briefly describe the units of the x and y scales: how are they calculated; are higher values showing lower/higher antibacterial activities and higher/lower hemolytic toxicities?\n- Line 158 (and supplementary information figures): what is t-SNE?\n- Figure 2e,f and figures S2-5, what are the colour codes of the acrylates and amines?\n- Lines 250-251: ‘integration of polyfluorocarbon unit (C16) effectively preserved antibacterial efficacy while reduced toxicity’; this statement might need to be balanced by a quote that, according to the revised definition from the Economic Co-operation and Development (OECD), the compounds containing this moiety (including shorter analogues such as in H2 and H3) would qualify as PFAS.\n- Line 268: the hydrophobic portions of the copolymers are attributed to the amine of type C, but are the gem-dimethyl and phenyl groups of the diacrylates not also contributing?\n- Figure 4i: is ‘control’, no treatment (ie., no antimicrobial agent)?\n- Line 297: these IC50 results are described in the supplementary information, but not introduced in the manuscript.\n- Line 344: ‘SANPs H1-3 induced robust fluorescence signals, confirming their ability to disrupt bacterial membrane potential. SM, however, did not induce fluorescence’; apologies if I am misreading figure 4j, but H1 seems to show the same level of membrane potential disruption than the HEPES buffer, while SM shows a higher level of membrane potential disruption than Triton-X?\n- Figure 5e: is the colour code and values of the scale bar shown on the right-hand side matching the results shown for the different organs? Blue colours on the scale seems to be associated with low values of fluorescence\n- Figure 5d: what does CD68 show?\n- Line 430: PG crystals shown within the nanocomposites: the magnification required to view the particles' details make the images blurry. Some darker regions in the largest H1 particles, can be seen; are these crystals? Could images be included with higher resolutions and arrows pointing the crystals be added?\n- Are references 21 and 50 duplicated?\n- Table S2: should it be Amines B, C and D, as per the above list?", "reviewer_2": "The manuscript presents an innovative polymeric platform for the autonomous exploration of polymeric antibiotics, leveraging a self-supervised deep learning approach that integrates graph-based molecular representations, active learning, and high-throughput synthesis to explore large chemical spaces without reliance on pre-existing datasets. Experimentally, the study focuses on the systemic efficacy of self-assembled nanoparticle polymers (SANPs), including in pneumonia and intraperitoneal infection models. Specifically, it describes the development of PolyCLOVER, a deep learning framework that integrates multi-stage self-supervised learning, active learning, and high-throughput synthesis to navigate the vast and structurally complex chemical space of host defense peptide–mimetic polymeric antibiotics. By applying PolyCLOVER to a combinatorial library of ~100,000 poly(β-amino ester)s, the authors identified three lead compounds that self-assemble into stable nanoparticles (SANPs) with potent antibacterial activity against multidrug-resistant S. aureus and A. baumannii, as well as adjuvant properties that restore bacterial sensitivity to penicillin G. The in vivo efficacy of these SANPs, both as monotherapies and in combination with conventional antibiotics, underscores the potential of this AI–experimental integration to accelerate antibacterial discovery in the absence of large pre-existing datasets. While the concept is compelling and the integration of AI-driven molecular design with in vivo validation is of high interest, several key areas require clarification and expansion to enable a thorough peer review and to strengthen the scientific rigor and reproducibility of the work.\nComments\nMachine Learning Methods: The ML/AI methodology in the current form is difficult to follow and lacks sufficient detail for reproducibility and thorough peer review.\n• The manuscript does not explicitly state what molecular features were used to train the models. While Tables S5 and S6 appear to contain some feature-related information, it remains unclear whether these are the only features employed. This should be explicitly clarified in the Methods section.\n• Tables S5 and S6 are not referenced in the main text. All supplementary tables should be clearly cited and contextualized where relevant.\n• Table S7 lists the hyperparameter search space, but the final hyperparameter values chosen for each model are not reported. These are essential for reproducibility and should be included.\n• The authors should provide a clear, structured description of the entire ML workflow, including:\no Data preprocessing steps\no Feature selection/engineering\no Model architectures\no Training and validation strategies\no Performance evaluation metrics and thresholds\nThis structured reporting will enable assessment of methodological rigor and facilitate replication or adaptation by other researchers.\nExperimental Concerns:\n1. Toxicity assay relevance (Figure S17, panel B): The cell line toxicity assay does not specify which cell lines were tested. For a pneumonia model, inclusion of toxicity data from lung epithelial cell lines would be more relevant. This should be addressed in either the current dataset or with additional experiments.\n2. Toxicity markers in vivo (MRSA peritonitis model): In the mouse model of lethal MRSA peritonitis, standard toxicity markers such as serum alanine transaminase (ALT) and kidney function assays should be included to provide a more complete safety profile.\n3. Generalizability of PolyCLOVER platform: To demonstrate that PolyCLOVER can be applied more broadly to polymeric antibiotic discovery, the authors should include at least one example using a chemical framework or backbone other than poly(β-amino ester)s.\n4. Activity against bacterial persistence phenotypes: Testing SANPs against biofilms and persister cells would help establish their potential against challenging infection states and broaden their translational relevance.", "reviewer_3": "This manuscript presents a machine learning–guided, iterative framework for the discovery of polymeric antibiotics, leveraging a multi-stage, multi-task learning design to jointly predict antibacterial and hemolytic activities. By integrating computational predictions with experimental feedback, the authors establish a closed-loop discovery cycle that accelerates candidate identification. Using this approach, they identified polymeric antibiotics with potent activity against resistant bacterial strains, with wet-lab validation confirming that several candidates outperformed existing comparators in efficacy while maintaining acceptable safety profiles. Overall, the manuscript is clear and easy to follow. I only have some minor comments:\n1. Transductive limitation: The authors acknowledge that the method design (e.g., the ratio node and the pretraining strategy) makes the approach kind of transductive. I think this is a major limitation of the work as it prevents direct application of the model to other polymers. I suggest that the authors more explicitly state this constraint in the manuscript so that readers clearly understand the current scope and applicability of the method.\n2. Greedy acquisition strategy (Page 8, line 234): The authors mention that a “greedy acquisition strategy was employed to further exploit the most promising candidates,” but the description is rather brief. Please elaborate a bit more about this.\n3. Ratio embedding: The manuscript does not clearly explain how the ratio embedding is implemented. Was it based on a learnable sinusoidal encoding, or another approach?\n4.Pretraining details: While the pretraining procedure has been described in the authors’ previous work, it would strengthen this manuscript if the authors could provide a bit more details about the self-supervision objective for pretraining."}, "author_response": "We thank the reviewer for the question. Poly(β-amino ester)s are random copolymers synthesized by alternating diacrylate and amine monomers. An additional description of the reaction is provided in the Methods section as follows:\nSupplementary Fig. S26 | Synthesis scheme for poly(β-amino ester)s. The polymer is formed by alternating diacrylate and amine units, with three types of amines randomly distributed along the backbone. The resulting product can be regarded as a random copolymer composed of three types of repeating units.\n“Chemical synthesis of poly(β-amino ester)s. The step-growth polymerization of Michael addition produces linear polymers with ester and tertiary amine structures in their skeleton. In this study, reaction occurred between diacrylates and three types of amines to produce random copolymers (Supplementary Fig. S26). The polymer is formed by alternating diacrylate and amine units, with three types of amines randomly distributed along the chains. The molar ratio of diacrylate to amine was set at 1.2:1. Initially, the amine monomer solution (3 M in DMF) was pipetted to a reaction vial containing 48 μL of diacrylate (3 M in DMF) according to the specified ratio. The reaction mixture was then heated to 90°C and stirred magnetically for 24 hours. For combinations with Boc-protected amine monomers, 80 μL of trifluoroacetic acid was added after polymerization. The solution was stirred overnight at room temperature to remove the protective group. Finally, the crude product was purified by precipitating in ether/dichloromethane (1:1 in vol%) solution three times.”\n2. Scope of the framework: PolyCLOVER appears extendable to polymer backbones other than poly(β-amino ester)s; if this is correct, is it also applicable to non-linear polymer architectures, such as branched, star-shaped, graft/bottlebrush polymers?\nResponse:\nWe appreciate the reviewer’s insightful comment regarding the applicability of PolyCLOVER. Indeed, PolyCLOVER is highly extensible and can be applied to a broad range of polymer backbones beyond poly(β-amino ester)s, as long as the polymers can be conveniently constructed via combinatorial chemistry—through polymerization reactions that are compatible with a wide range of monomers and proceed under relatively mild conditions suitable for high-throughput synthesis. Furthermore, PolyCLOVER also holds potential for handling polymer libraries with more complex topologies, enabled by our previously developed graph-based polymer representation method. In a separate ongoing applied study in our group, we are exploring the use of PolyCLOVER for screening branched polymers with specific biological activity. Preliminary results indicate that the model can be extended to such architectures by introducing virtual nodes to specify the fraction of branched units. However, this line of work lies beyond the scope of the current study. To clarify the applicability of PolyCLOVER, we have revised the Discussion section to include the following statement:\n“More importantly, we established PolyCLOVER as a generalizable and scalable platform not only applicable to poly(β-amino ester)s but also potentially extensible to a broad range of polymer backbones and complex topologies, including branched or star-shaped architectures.”\nSeparately, the following points require revision and/or clarification:\n3. For the combinatorial library of 220 candidates and the subsequent one of 60 candidates constructed experimentally, the details of the synthetic procedure(s) should be provided in supplementary information. These should also clarify the ratios of monomers used; in figure S16, the feeding ratios for x, y and z are provided, but are they for (preformed?) A-B, A-C and A-D blocks? Also, are these feeding ratios matching the actual ratios of the selected candidates in the library, or do they account for the different polymerisation kinetics of the diverse monomers? In other terms, how is the correspondence between the computed and experimental ratios ensured?\nResponse:\nWe thank the reviewer for this detailed and constructive comment. The monomers and feeding ratios for both the 220 candidates and the subsequently selected 60 candidates have been added to the Supplementary Information (Supplementary Data 1). All samples were synthesized using the same procedure, which is described in detail in the ‘Chemical synthesis of poly(β-amino ester)s’ section of the Methods. In response, we have included the following statement in the manuscript:\n“All samples involved in the screening process are summarized in Supplementary Data 1.”\nThe monomers were added directly into the reaction system without pre-forming any blocks.\nAs noted in the Response of Comment 1, the resulting products are random copolymers composed of diacrylate and three types of amines, where diacrylate and amine units alternate along the main chain, and the three amine monomers are randomly distributed. In our machine learning modeling, we conceptualized these copolymers as random sequences consisting of three possible repeating units (A–B, A–C, and A–D).\nFrom a machine learning perspective, we directly modeled the relationship between the input feeding ratios (combined with the chemical structures) and the antibacterial and hemolytic properties. This design enables the model to automatically learn the systematic biases or kinetic effects present in the experimental data. In other words, the model is trained to learn the correspondence between feeding ratios (combined with the chemical structures) and observed biological activity, and during inference, it predicts activity under the same type of inputs (feeding ratios)—ensuring that the training and prediction domains are aligned. Thus, the model effectively captures the relationship between input conditions (feeding ratios + structures) and measured properties, even if the actual ratios are not explicitly known.\nMoreover, even if the actual monomer ratios of the synthesized candidates were precisely characterized and used for training, such information would not be available for unlabeled samples during prediction. In practice, we only know the feeding ratios when selecting new candidates for synthesis, and there is no reliable inverse mapping from actual composition back to the feeding conditions. Therefore, modeling based on actual ratios—while potentially more accurate during training—would not translate into a practically useful tool for guiding optimization.\nMore importantly, the primary objective of this study is the high-throughput screening and optimization of antibacterial polymers, where it is essential to balance efficiency and accuracy. In this context, detailed structural characterization would not be cost-effective, compatible with a high-throughput workflow, or necessary for achieving the goals of this study. At the same time, considering the broad synthetic accessibility of poly(β-amino ester)s and the precedent set by previous studies that have employed them to construct libraries1,2, we accepted a certain degree of deviation between the actual ratio and the feeding ratio to accommodate the practical requirements.\nOur current structure + feeding ratio modeling approach provides a practical, consistent, and data-driven framework for predicting polymer activity under high-throughput experiment settings.\nIn response to the reviewer’s comment, we have added the following clarification to the Methods section: “Given the high-throughput nature of this study, direct characterization of actual monomer incorporation ratios for each candidate would be neither cost-effective nor necessary. Instead, feeding ratios—combined with chemical structures—are used as practical and consistent model inputs. This design enables efficient, data-driven prediction of biological activity, while maintaining alignment between training and inference conditions.”\n4. Figure 4c and lines 305-306: Streptomycin, used as a reference/control, is an aminoglycoside; why are H1-H3, as polymeric mimics of AMPs, not benchmarked to a representative AMP?\nResponse:\nWe thank the reviewer for this valuable comment. In Fig. 4c, we aimed to include a potent antibiotic as a positive control. Representative AMP-class antibiotics, such as polymyxins, have an antibacterial spectrum that is restricted to Gram-negative bacteria and are therefore not suitable for evaluating efficacy against MRSA. For this reason, we chose streptomycin (1 μg/mL), a widely used and broad-spectrum antibiotic, as the reference control in our study.\n5. Restored MRSA susceptibility to penicillin G: these are interesting results, that are difficult to understand at the molecular level, but which have been previously reported with another antimicrobial polymer mimic of AMPs (eg., DOI: 10.1021/acsinfecdis.0c00016). However, the interpretation of these results proposed by the authors, based on an increased affinity model, would benefit from additional details, proposed alternatives or evidences (experimental and/or from the literature). The hypothesis advanced is that the accumulation and integration of H2 nanoparticles in the bacterial membrane ‘leads to passive enrichment of PG at the membrane interface, thereby enhancing its affinity for penicillin-binding proteins (PBPs) and overcoming the low-affinity resistance mechanism’. But it has for example been proposed that the reduced binding of beta-lactam antibiotics to PBP2a is caused by a closed active site, so it is difficult to conciliate the effect of the polymer at the membrane level with an opening of the active site. On the other hand, a conformational change in this protein can induce this active sire opening, thereby restoring access to beta-lactam antibiotics (DOI: 10.1021/ja0434376). As the bacterial membrane can be involved in the PBP2a stabilisation, would a PBP2a conformational change induced by a membrane destabilisation and resulting in an opening of the PBP2a active site not be a potential alternative mechanism? On the other hand, have the authors some evidence that penicillin encapsulated in H2 remain stable, considering the presence of amino groups which could potentially react with the beta-lactam?\nResponse:\nWe sincerely thank the reviewer for this insightful comment and for stimulating discussion on the mechanisms underlying the restored susceptibility of MRSA to penicillin G. As the reviewer correctly notes, similar observations have been reported in the literature, where cationic polymers, peptides, or nanoparticles with membrane-disruptive properties were shown to potentiate the activity of conventional antibiotics. However, to the best of our knowledge, a definitive molecular-level explanation for this phenomenon remains elusive, and we hope the reviewer will appreciate the difficulty of establishing such a mechanism with certainty. For instance, in the study cited by the reviewer (DOI: 10.1021/acsinfecdis.0c00016)3, cationic nanoparticles were shown to restore MRSA susceptibility to oxacillin, and the authors observed mutations in the ebh gene encoding the hyperosmolarity resistance protein Ebh across multiple isolates. Likewise, Williams et al.4 (DOI: 10.1021/acscentsci.4c01973) demonstrated that polyacrylamide copolymers with membrane-disruptive effects enhanced the efficacy of rifampicin, penicillin, and vancomycin against E. coli, and their interpretation favored a model in which copolymer-induced membrane perturbation facilitated antibiotic penetration.\nWe also found the reviewer’s reference to Mobashery’s seminal work5 (DOI: 10.1021/ja0434376) particularly inspiring. In this study, the key acylation step (1 → 2) was shown to depend on both the substrate concentration and the acylation rate constant k (as illustrated in the 2 schematic pathway below). In light of this, we now appreciate that two complementary mechanisms may be possible in our system. On the one hand, SANP nanocomposites carrying penicillin G may enrich the antibiotic at the membrane interface, effectively increasing its local concentration and thereby driving the acylation reaction forward, which was the basis of our original hypothesis. On the other hand, membrane interactions, particularly those involving destabilization, could create an environment rich in NAM/NAG fragments from cell wall turnover, which may in turn promote conformational changes in PBP2a. Such changes could facilitate the opening of the active site and increase k , thereby enhancing access of β-lactam antibiotics. We have now incorporated this latter 2 possibility into the revised manuscript as a plausible alternative mechanism.\nAccordingly, we have revised the corresponding section in the main text to clarify this point: “Based on the above findings, we speculate that, due to the cationic and hydrophobic properties, the SANP nanocomposites can act as membrane-disruptive carriers that preferentially accumulate and integrate into bacterial membranes56. This process leads to passive enrichment of PG at the membrane interface, thereby increasing its local concentration. At the same time, the membrane-disruptive effect of SANPs may generate additional cell wall fragments in the surrounding environment, which could kinetically facilitate the acylation of the active-site serine of PBP2a by β-lactams57, ultimately overcoming the low-affinity resistance mechanism.”\nFinally, regarding the stability of penicillin G when encapsulated in SANP nanocomposites, we agree that this is an important consideration. The nucleophilic ring-opening of β-lactams by amines typically requires harsh catalytic conditions (strongly acidic or basic media)6. Under the relatively neutral conditions of our experiments (pH ≈ 7), such reactions are unlikely to proceed to a significant extent. To experimentally validate this, we mixed penicillin G with a model amine monomer structurally analogous to the repeating unit of the identified SANPs (N,N-dimethyl ethylenediamine) under neutral pH and analyzed the mixture by 1H NMR spectroscopy. The NMR spectra confirmed that no detectable reaction occurred under these conditions. In contrast, when pH = 11, the NMR spectrum exhibited multiple additional peaks with irregular integrations, indicating the presence of products other than the starting materials, consistent with possible β-lactam ring-opening or other side reactions. As H2 is a polymer and its spectrum is difficult to interpret directly, the monomeric analogue was used as a simplified model to validate the chemical stability of penicillin G.\n(a) 1H NMR spectrum of penicillin G\n(b) 1H NMR spectrum of N,N-dimethylethylenediamine\n(c) 1H NMR spectrum of the mixture of penicillin G and N,N-dimethylethylenediamine under neutral conditions (pH = 7)\n(d) 1H NMR spectrum of the mixture of penicillin G and N,N-dimethylethylenediamine under alkaline conditions (pH = 11)\n6. In would be interesting in the discussion to compare the properties of the copolymers identified by PolyCLOVER, with those of previously developed polymers (experimentally). For example, structurally, PolyCLOVER selected repeating units of the amino groups with short branching chains, while a number of antimicrobial peptides have been reported as benefiting from longer chains (eg., 4 methylene groups). The typical activities and selectivities could also be compared, although HC10 values are generally used, while HC50 values are reported here.\nResponse:\nWe sincerely thank the reviewer for this insightful comment. Generally, antimicrobial peptides (AMPs) are composed of 20 natural amino acids. As the reviewer correctly pointed out, AMPs often benefit from amino groups with four methylene groups (Lysine). This is primarily due to the positively charged side chain of lysine (amino group), which is considered to interact more strongly with the negatively charged bacterial membranes. However, since natural AMPs do not contain amino acids with systematically varied side chain lengths of amino-group side chain, there has been little evolutionary selection on chain length. In contrast, our study varied the length of the amino side chains to investigate their influence on antibacterial activity.\nFor antibacterial polymers, we found that our observation is consistent with some previously reported findings7,8, which suggest that antimicrobial activity increases as the number of carbon atoms in the pendant groups decreases. However, opposite trends have also been reported in other systems9,10. Overall, there does not appear to be a universal conclusion across different polymer systems and microbial species. We believe that the antibacterial performance of polymers results from the combined effects of multiple factors, including chain rigidity, hydrophobic/hydrophilic balance, cationic charge density, and others. Therefore, direct comparison between polymers that simultaneously differ in both backbone structures and side chain lengths may not yield mechanistically meaningful insights.\nTo address the reviewer’s suggestion, we have included the following observation in the main text: “A sharp increase in the de-protected primary amine of B10 suggested that the model favored a short chain length of positive charge (Fig. 3g), a tendency that has also been noted in some other polymer systems.41,51”\n7. Figure S16: are the MW calculated from the NMR spectra, or otherwise? These NMR spectra should be complemented by GPC chromatograms to provide sufficient characterization of the copolymers for publication. Also, without integration of the proton NMR spectra, the characteristics of the copolymers cannot be verified. The chemical shift for protonated primary amine appears to be higher than usual in proton NMR? It is also a sharp signal, while the one attributed to protons f, which is not bound to a heteroatom is broad. There appears to be some residual DCM and diethyl ether in H2 and H3. In this case, because of the presence of diethyl ether, the signal at ~3.5ppm cannot be assigned, to protons j. If the latter signal is masked by diethyl ether, this should be clarified in the legend.\nResponse:\nWe appreciate the reviewer’s valuable comments on the characterization of the identified samples. The molecular weights were determined using gel permeation chromatography (GPC) with DMF containing LiBr as the eluent, rather than by NMR. To avoid potential misunderstanding, this clarification has been explicitly stated in the revised figure legends: “The molecular weights were determined using gel permeation chromatography (GPC; Waters 1515/2414) with DMF containing LiBr as the eluent.”\nRegarding the NMR spectra, we have reanalyzed and annotated the NMR spectra of H1–H3. Residual solvent signals, including dichloromethane (DCM) and diethyl ether, have been clearly labeled, and integration values have been provided to verify copolymer composition.\nFor H1, the integrals of gh (4H total) = 2.34, e (6H total) = 1.23, and i (22H total) = 1.80 correspond to a ratio of x:y:z = 6.7:2.4:0.9. For H2 and H3, where the f signal could not be clearly resolved, integration allowed estimation of H2 x:z = 7.9:1.0 and H3 x:z = 8.2:0.9; the proportion of y was calculated by subtracting x and z from the backbone integration. These NMR-derived ratios show good correlation with the monomer feeding ratios. We agree with the reviewer that the protonated primary amine peak appeared sharper and more downfield than usual in CDCl . Therefore, the 3 ambiguous a signal has been removed from the spectra in CDCl . Because the solvent residual peak 3 at ~7.26 ppm interfered with the aromatic proton region (especially for H3), we remeasured the spectra in DMSO-d . The integrations obtained in this solvent confirmed the backbone composition 6 ratios (H1 and H2, k:d ≈ 2:3; H3, d:e ≈ 1:1). However, due to the strong solvent peak at ~2.50 ppm and the water signal at ~3.3–3.6 ppm in DMSO-d , the side-chain composition could not be precisely 6 quantified. In these spectra, the labile –NH protons were assigned to a broad resonance near ~7 2 ppm. Moreover, because the residual water peak overlapped with the ~3.5 ppm region, the protons labeled j could not be assigned. These revisions improve the accuracy and clarity of the NMR characterization.\nFig. S16 | Structures and nuclear magnetic resonance spectra (1H NMR, CDCl ) of the 3 identified SANPs. (a) H1, feeding ratio x:y:z = 6:3:1; (b) H2, feeding ratio x:y:z = 8:1:1; and (c) H3, feeding ratio x:y:z = 8:1:1. Residual solvent signals from dichloromethane (DCM) and diethyl ether are labeled in the spectra. The composition ratios were calculated based on the integrated areas of the NMR signals. The residual diethyl ether peak at ~3.5 ppm overlaps with the region of protons labeled j, which were therefore excluded from integration analysis. The molecular weights were determined using gel permeation chromatography (GPC; Waters 1515/2414) with DMF containing LiBr as the eluent.\nStructures and nuclear magnetic resonance spectra (1H NMR, DMSO-d ) of the identified 6 SANPs. (a) H1, feeding ratio x:y:z = 6:3:1; (b) H2, feeding ratio x:y:z = 8:1:1; and (c) H3, feeding ratio x:y:z = 8:1:1. Residual solvent (DMSO at ~2.50 ppm) and water signals (3.3–3.6 ppm) are labeled, as well as a minor ether impurity. Due to partial overlap between the water signal and the region of protons labeled j, these peaks could not be precisely integrated and were excluded from compositional analysis.\nPlease find here also some minor comments:\n8. Line 296: ‘The MIC of all three SANPs were 8 μg/mL’; is it against MRSA ATCC 43300, as the results in figure 4g for LAC and 779 show different MICs?\nResponse:\nWe thank the reviewer for pointing out this ambiguity. Throughout the AI-assisted screening workflow, we used MRSA ATCC 43300 as the model strain. Accordingly, the sentence “The MIC of all three SANPs was 8 μg/mL” specifically refers to MRSA ATCC 43300 under our MIC assay conditions. After the screening was completed, we further evaluated the efficacy of the identified samples (H1–H3) against more clinically isolated drug-resistant pathogens, including MRSA LAC, 779, and others (Fig. 4g). We consider the modest variations in MIC values observed across different strains to be within a reasonable range.\nTo avoid misunderstanding, we have clarified the sentence in the main text as follows:\n“The MIC of all three SANPs was 8 μg/mL against MRSA ATCC 43300.”\n9. In Figure 2a, it would be helpful to refer to table S1 for the codes A1, B1, …\nResponse:\nWe appreciate the reviewer’s careful reading of the manuscript. We have added the following statement to the legend of Fig. 2a for clarity: “The structures corresponding to the building block IDs can be found in Supplementary Table 1.”\n10. Figure 2b, it would be helpful to briefly describe the units of the x and y scales: how are they calculated; are higher values showing lower/higher antibacterial activities and higher/lower hemolytic toxicities?\nResponse:\nWe appreciate the reviewer’s careful reading of the manuscript. In Fig. 2b, the x-axis represents the optical density (OD) values measured at 595 nm, where lower values indicate stronger antibacterial activity. The y-axis represents the hemolytic ratio, with lower values indicating better hemocompatibility. Details of the experimental procedures and calculations can be found in ‘Antibacterial and hemolytic activity labelling’ section in the Methods. To improve clarity, we have added downward arrows (↓) to both axis labels to indicate that lower values correspond to better performance. We have also revised the figure legend to include the following explanation:\n“Lower values on axes indicate stronger antibacterial activity and better hemocompatibility.”\n11. Line 158 (and supplementary information figures): what is t-SNE?\nResponse:\nWe thank the reviewer for pointing this out. t-SNE stands for t-distributed stochastic neighbor embedding, a widely used dimensionality reduction algorithm that projects high-dimensional data into a low-dimensional space (typically 2D or 3D) while preserving local similarities among data points. It is commonly used for visualizing complex datasets such as chemical feature embeddings.\nTo address the reviewer’s comment, we have now added a brief explanation of t-SNE in the main text: “To visualize the learned polymer representations in two-dimensional space, we applied t-SNE—a widely used dimensionality reduction algorithm that preserves local similarities—to both the pretrained and unpretrained models.”\n12. Figure 2e,f and figures S2-5, what are the colour codes of the acrylates and amines?\nResponse:\nWe appreciate the reviewer’s careful reading and helpful comment. We have now added a legend of the color codes for the acrylates and amines in Fig. 2e,f and Supplementary Figs. S2–5.\nFig. 2e | t-SNE visualization of representations generated by the pre-trained and un-pretrained graph encoder. Each data point is colored according to the type of diacrylate used.\n13. Lines 250-251: ‘integration of polyfluorocarbon unit (C16) effectively preserved antibacterial efficacy while reduced toxicity’; this statement might need to be balanced by a quote that, according to the revised definition from the Economic Co-operation and Development (OECD), the compounds containing this moiety (including shorter analogues such as in H2 and H3) would qualify as PFAS.\nResponse:\nWe thank the reviewer for raising this important point. In response, we have revised the statement to provide a more balanced view: “We also found the unique integration of polyfluorocarbon unit (C16) effectively preserved antibacterial efficacy while reduced toxicity, which aligned with previous studies52. Nevertheless, this moiety falls under the category of per- and polyfluoroalkyl substances (PFAS), and warrants attention in practical applications.”\n14. Line 268: the hydrophobic portions of the copolymers are attributed to the amine of type C, but are the gem-dimethyl and phenyl groups of the diacrylates not also contributing?\nResponse:\nWe thank the reviewer for the question. The gem-dimethyl and phenyl groups of the diacrylates also contribute to the overall hydrophobicity. In this discussion, our intention was to specifically highlight the major source of hydrophobic variability, which in this study was systematically engineered through the selection of amines with differing alkyl chain lengths (Type C).\nTo clarify this point, we have revised the relevant sentence in the main text to explicitly refer to the amines as the variable component: “The positively charged amines (B) used in the formulation consisted of deprotected primary amines with short chains. The hydrophobic amines (C) featured either a relatively short tertiary alcohol or a short chain with fluorinated side groups, which likely mitigated the hemolytic toxicity associated with longer hydrophobic chains. The hydrophilic amines (D) included oligomeric polyethylene glycol (PEG) side groups in H1 and H3, while H2 contained a unique amide side group.”\n15. Figure 4i: is ‘control’, no treatment (ie., no antimicrobial agent)?\nResponse: The control group was only treated with PBS without antimicrobial agent. This has been clarified in the revised figure caption: “Control: PBS treatment only; no antimicrobial agent applied.”\n16. Line 297: these IC50 results are described in the supplementary information, but not introduced in the manuscript.\nResponse:\nWe thank the reviewer for the careful reading. The IC values were originally reported in the 50 main text (line 298) of the original manuscript. In the revised version, according to another reviewer’s suggestion, we additionally tested IC values using a cell line more relevant to the 50 pneumonia model (MLE-12) and updated the results accordingly. The corrected text now reads as follows: “Both the HC (H1 = 168 μg/mL, H2 > 512 μg/mL, H3 = 266 μg/mL) and the half- 50 maximal inhibitory concentration (IC ; H1 = 53 μg/mL, H2 = 162 μg/mL, H3 = 109 μg/mL, 50 assessed in L929 fibroblast cells) followed a consistent trend, which was also observed in the lung epithelial cell line MLE-12, with H2 exhibiting the lowest toxicity among the three (Supplementary Fig. S17).”\n17. Line 344: ‘SANPs H1-3 induced robust fluorescence signals, confirming their ability to disrupt bacterial membrane potential. SM, however, did not induce fluorescence’; apologies if I am misreading figure 4j, but H1 seems to show the same level of membrane potential disruption than the HEPES buffer, while SM shows a higher level of membrane potential disruption than Triton-X?\nResponse:\nWe sincerely thank the reviewer for the meticulous attention to detail and for catching this error in Fig. 4j. Upon careful review, we found that the inconsistency between the statement and Fig. 4j was caused by a mistake in the x-axis labels of the original figure. The figure was replaced accordingly.\n18. Figure 5e: is the colour code and values of the scale bar shown on the right-hand side matching the results shown for the different organs? Blue colours on the scale seems to be associated with low values of fluorescence\nResponse:\nWe thank the reviewer for their close attention to the imaging data. The color code is indeed correctly mapped to the values in the scale bar. Organs without polymer appear black, corresponding to the lowest end of the scale. The polymer tends to enrich in liver and show green color here in the organ center. And the fluorescence in lung still outcompetes other organs such as heart and kidney which present black colors.\n19. Figure 5d: what does CD68 show?\nResponse:\nWe thank the reviewer for the question. The CD68 staining in our study serves as a key marker to visualize the activity of macrophages in the tissues of our infected animal model. In the control group without treatment, CD68 staining shows a robust inflammatory response, while the antibacterial treated group presented relatively modulated results of the excessive inflammatory response. We have made the corresponding revision in the main text as follows: “The results of H&E staining revealed that the alveolar structure was collapsed in the PBS treatment group. In contrast, it remained porous in the SANPs-treated group, indicating the relief of lung infection. Additionally, Masson staining showed no significant blue fiber deposition, further suggesting that H1–3 exhibited a favorable therapeutic effect. And CD68 staining demonstrated that H1–3 modulated the excessive inflammatory response (Fig. 5d).”\n20. Line 430: PG crystals shown within the nanocomposites: the magnification required to view the particles' details make the images blurry. Some darker regions in the largest H1 particles, can be seen; are these crystals? Could images be included with higher resolutions and arrows pointing the crystals be added?\nResponse:\nWe thank the reviewer for this valuable suggestion. As correctly noted, the darker regions correspond to PG crystals. To improve clarity, we now include higher-resolution TEM images in the Supplementary Information with arrows indicating the regions where PG are evident:\nFig. S21 | TEM images of H1-, H2-, and H3-PG nanocomposites. Darker regions (indicated by white arrows) are observed within the nanoparticles, corresponding to PG crystalline domains. Scale bar: 1 μm.\nAccordingly, a reference to Supplementary Fig. S21 has been added in the main text:\n“Transmission electron microscopy (TEM) images obtained in the dry state clearly showed PG crystals encapsulated within the nanocomposites (Fig. 6b, Supplementary Fig. S21).”\n21. Are references 21 and 50 duplicated?\nResponse:\nWe thank the reviewer for the comment. We have removed the redundant entry and updated the in-text citations accordingly.\n22. Table S2: should it be Amines B, C and D, as per the above list?\nResponse:\nWe thank the reviewer for pointing this out. We have corrected the name in Supplementary Table S2 to ensure consistency with the main text, and the amines are now labeled as B, C, and D as follows.\nTable S2 | Feeding ratio of poly(β-amino ester)s combinatorial library.\nIndex Diacrylate (A) Amines (B) Amines (C) Amines (D)\n1 12 2 2 6\n2 12 2 4 4\n3 12 2 6 2\n4 12 4 2 4\n5 12 4 4 2\n6 12 6 1 3\n7 12 6 3 1\n8 12 8 1 1"}, {"round": 2, "reviewer_comments": {"reviewer_1": "Thank you to the authors for their comprehensive and thorough responses to the comments from the review of the original manuscript. These comments are well addressed, except for the characterization of the polymers; apologies if I misunderstood and/or missed some information:\n1. In the manuscript, it is written (page 4): ‘Next, we randomly selected 220 samples from the constructed library for laboratory synthesis and characterization to form the initial dataset.’, while in the rebuttal letter it is indicated that ‘In this context, detailed structural characterization would not be cost-effective, compatible with a high-throughput workflow, or necessary for achieving the goals of this study.’ I understand the latter comment and appreciate that the entire library cannot be characterized, but am not sure if the structural characterization was not undertaken as well for (some of the) selected samples, given the statement in the manuscript; or does this characterization refers to something else, such as testing? If the selected samples (apart from H1-H3, see below) were characterized, have their analytical data been deposited in a repository?\n2. More specifically, for H1, H2 and H3, the integrations of the proton NMR spectra can now be used to understand how their compositions were determined, but these could be subject to some errors, as some signals used are not well defined (for example, signal i in H3, for 14 protons). This is just an observation, and this point does not need to be answered, but it would be recommended to revert to the representation of the PEG chains in H1 and H3 used of the original SI, as otherwise there would be 10 or 6 protons f in these PEG chains for H1 and H3, respectively, as now represented.\n3. The GPC chromatograms of H1, H2 and H3 appear to be still missing, and this comment should be addressed.", "reviewer_2": "The authors have addressed my previous concerns, and I have no additional feedback on the revised manuscript."}, "author_response": "We sincerely thank the reviewer for the careful reading. As mentioned in our previous response, the monomer feeding ratios (along with the monomer chemical structures) were used directly as input features to model the antibacterial and hemolytic properties. The actual monomer incorporation ratios in the resulting copolymers were not meaningful in our model, as they are unavailable for unsynthesized candidates during inference and are irrelevant to the modeling framework.\nAccordingly, we did not perform structural characterization for the 220 synthesized samples used in the initial dataset. Structural characterization was conducted only for the three final candidates (H1, H2, and H3) that were selected based on model predictions and experimental validation. In the manuscript, the sentence “Next, we randomly selected 220 samples from the constructed library for laboratory synthesis and characterization to form the initial dataset” refers specifically to biological characterization, including antimicrobial activity and hemolysis testing. We have revised this sentence in the manuscript to read: “Next, we randomly selected 220 samples from the constructed library for laboratory synthesis and testing of their antibacterial and hemolytic properties to form the initial dataset.”\n2. More specifically, for H1, H2 and H3, the integrations of the proton NMR spectra can now be used to understand how their compositions were determined, but these could be subject to some errors, as some signals used are not well defined (for example, signal i in H3, for 14 protons). This is just an observation, and this point does not need to be answered, but it would be recommended to revert to the representation of the PEG chains in H1 and H3 used of the original SI, as otherwise there would be 10 or 6 protons f in these PEG chains for H1 and H3, respectively, as now represented.\nResponse:\nWe thank the reviewer for this careful observation. As suggested, we have reverted the representation of the PEG chains in H1 and H3 to the expanded form used in the original Supplementary Information.\n3. The GPC chromatograms of H1, H2 and H3 appear to be still missing, and this comment should be addressed.\nResponse:\nWe appreciate the reviewer’s continued attention to the GPC characterization. The GPC chromatograms are provided as follows. We would like to clarify that accurate GPC analysis of these copolymers is intrinsically challenging due to their chemical characteristics. Specifically, the polymers carry a high density of cationic amine groups, which significantly restricts the choice of compatible mobile phases and increases susceptibility to secondary interactions in size-exclusion chromatography.\nIn combination with their relatively low molecular weights, this leads to polymer elution near the solvent front when DMF containing LiBr is used as the eluent, resulting in limited chromatographic resolution between the polymer and solvent-associated signals. We performed multiple GPC measurements under various conditions, including repeated injections and parameter optimization; however, complete baseline separation of the polymer peaks was not achieved for any of the samples (H1, H2, or H3). Considering these inherent limitations, molecular weight values were estimated from the first discernible polymer elution peak, which appeared in the retention time window of approximately 28–31 min.\nImportantly, this study was not designed to optimize molecular weight or to evaluate its role in antibacterial activity systematically. Instead, our modeling framework directly relates monomer chemical structures and feeding ratios to the resulting biological activities, without introducing other variables. In this design, molecular weight is reported solely as descriptive characterization information, rather than as a determining parameter in the observed structure–activity relationships."}]

Decision letter