MAC-AMP: A Closed-Loop Multi-Agent Collaboration System for Multi-Objective Antimicrobial Peptide Design
Authors: Gen Zhou
Sugitha Janarthanan
Lianghong Chen
Pingzhao Hu
Venue: ICLR 2026 Poster (2026)
Keywords: Multi-agent system
Antimicrobial peptides
Multi-objective optimization
AI-simulated peer review
Reinforcement learning
Primary area: applications to physical sciences (physics, chemistry, biology, etc.)

Abstract: To address the global health threat of antimicrobial resistance, antimicrobial peptides (AMP) are being explored for their potent and promising ability to fight resistant pathogens. While artificial intelligence (AI) is being employed to advance AMP discovery and design, most AMP design models struggle to balance key goals like activity, toxicity, and novelty, using rigid or unclear scoring methods that make results hard to interpret and optimize. As the capabilities of Large Language Models (LLM) advance and evolve swiftly, we turn to AI multi-agent collaboration based on such models (multi-agent LLMs), which show rapidly rising potential in complex scientific design scenarios. Based on this, we introduce $\textbf{MAC-AMP}$, a closed-loop multi-agent collaboration (MAC) system for multi-objective AMP design. The system implements a fully autonomous simulated peer review-adaptive reinforcement learning framework that requires only a task description and example dataset to design novel AMPs. The novelty of our work lies in introducing a closed-loop multi-agent system for AMP design, with cross-domain transferability, that supports multi-objective optimization while remaining explainable rather than a 'black box'. Experiments show that MAC-AMP outperforms other AMP generative models by effectively optimizing AMP generation for multiple key molecular properties, demonstrating exceptional results in antibacterial activity, AMP likeliness, toxicity compliance, and structural reliability.

Published as a conference paper at ICLR 2026
MAC-AMP: A CLOSED-LOOP MULTI-AGENT COL- LABORATION SYSTEM FOR MULTI-OBIECTIVE AN- TIMICROBIAL PEPTIDE DESIGN
Gen Zhou', Sugitha Janarthanan"; Lianghong Chen', Pingzhao Hu1 'Department of Computer Science, Western University, London, ON, Canada *Department of Biochemistry, Western University, London, ON, Canada
ABSTRACT
To address the global health threat of antimicrobial resistance, antimicrobial pep- tides (AMPs) are being explored for their potent and promising ability to fight resistant pathogens. While artificial intelligence (AI) being employed to ad- vance AMP discovery and design, most AMP design models struggle to balance key goals like activity, toxicity, and novelty, using rigid or unclear scoring meth- ods that make results hard to interpret and optimize. As the capabilities of Large Language Models (LLMs) advance and evolve swiftly, we turn to Al multi-agent collaboration based such models (multi-agent LLMs), which show rapidly ris- ing potential in complex scientific design scenarios. Based on this, we introduce MAC-AMP, closed-loop multi-agent collaboration (MAC) system for multi- objective AMP design. The system implements a fully autonomous simulated peer review-adaptive reinforcement learning framework that requires only a task description and example dataset to design novel AMPs. The novelty of our work lies in introducing a closed-loop multi-agent system for AMP design, with cross- domain transferability, that supports multi-objective optimization while remaining explainable rather than a 'black box'. Experiments show that MAC-AMP outper- forms other AMP generative models by effectively optimizing its AMPs for mul- tiple key molecular properties, demonstrating exceptional results in antibacterial activity, AMP likeliness, toxicity compliance, and structural reliability.
INTRODUCTION
Although new antibiotics are still being developed to treat bacterial infections, antimicrobial resis- tance (AMR) remains a critical challenge, one that has caused systemic crisis in global public health. AMR occurs when bacteria evolve mechanisms that reduce or eliminate the effectiveness of drugs designed kill them, making bacterial infections harder treat. In 2021 alone, bacte- rial AMR directly caused approximately 1.14 million deaths and was associated with approximately 4.71 million deaths. In addition, is estimated that between 2025 and 2050. AMR will directly lead to over 39 million deaths across all ages and will be associated with 169 million deaths (Naghavi jet al.] 2024). Antimicrobial peptides (AMPs) are naturally occurring short chains of amino acids that part of the innate immune system and serve the natural defence against a broad range of microbes in many living organisms. They are being explored to combat AMR due to their im- pressive properties, including broad-spectrum activity, diverse mechanisms of action, and higher resistance barriers compared to traditional antibiotics. However, they still face bottlenecks such as risk of toxicity and hemolysis, insufficient stability and bioavailability in vivo, and limitations in manufacturability and cost (Bucataru & Ciobanasu 2024 Min et al., 2024).
Recently, scientists have turned to artificial intelligence (AI) models to design AMPs. Over the past couple of years, Al-driven AMP discovery has expanded from retrieval and screening to include generation and optimization (Pirtskhalava al. (202I). Recent AMP generative and discriminative models have achieved encouraging progress on public benchmarks, and some studies have validated
"Equally Contributed
1 Contact Author: phu49@uwo.cr

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several in vitro active candidates. However, most AI-driven AMP design models face limitations. First, most optimize solely for activity, which tends to produce AMPs with undesirable molecular properties, limited synthesizability, and restricted novelty (Van Oort et al. 2021: Tucs et al. 2020 Those that employ multi-objective optimization are often unstable, as static weighting or threshold- ing can cause reward hacking or diversity collapse (Abels et al.) 2019). In addition, their outputs usually scattered scores or text, which are hard to convert into clear, reproducible learning signals for stable reinforcement-styyle optimization (Van Kempen al. 2024 Wu et al. 2022 Guan al. 2025). To combat these gaps, multi-agent collaboration (MAC) systems are being explored. MAC systems emphasize solving complex tasks through division of labour, communication, and collabo- ration among interacting autonomous agents. Large Language Models (LLMs) are now being used as flexible interfaces and reasoning agents within MAC systems, making LLM-based MAC systems very popular. A review published in late 2024 on LLM-based MAC systems highlighted signifi- cant advances in complex problem-solving and world simulation, demonstrating the effectiveness of research frameworks in which collaborative agents interact to execute sophisticated scientific and engineering workflows across diverse domains (Guo et al.] 2024). However, a critical limitation of current MAC systems is that their outputs are typically the form of natural language or het- erogeneous scores, lacking reproducible training signals suitable for model optimization. A recent model, Eureka, has shown that LLMs that generate and self-improve their own reward code can sig- nificantly boost reinforcement learning (RL) performance, but it has yet to be integrated into MAC systems (Ma et al., 2023). In addition, recent LLM-baseed MAC systems are largely open-loop, as they converse. call tools, and often involve human-in iattaa--i-thh-looo the-loop operation (Wu et al. (024).. As re- sult, downstream optimization often relies on trial-and-erm prompt iteration or ad-hoc fine-tuning rather than principled closed-loop learning (Song et al. 2024).
To address these gaps, we introduce MAC-AMP, the first closed-loop MAC system for AMP design. Unlike prior AMP generators that treat design as single-model sequence optimization task, we recast AMP design as a coordinated multi-agent problem and propose a general, end-to-end path- way that translates user's design request into novel, caatti-obeectiv-oppiiiiei integrates four modules: (I) a Property Prediction module that applics specialized scoring tools to evaluate AMPs activity, safety, stability, and novelty: (2) an Al-Simulated Peer Review module, in which specialized agents synthesize these evaluations into structured, multi-criteria consensus rather than relying isolated scalar scores; (3) an RL Refinement module that translates agent consensus into machine-actionable reward functions, replacing free-text or ad hoc weighting heuris- ties; and (4) Peptide Generation module that closes the loop by dynamically and transparently adapting the training objective during peptide design, enabling stable optimization under conflicting biological constraints. The key architectural novelties of MAC-AMP are:
AMPs. MAC-AMP
1. A fully autonomous, closed-lood multi-agent system that converts AMP-specific evaluations into executable RL reward signals, which establishes real-time feedback cycle for continuous design, critique, and optimization.
2. Stepwise explainability and auditability via transparent logs, replay traces, and consensus- aware decision tracking across all agents, overcoming black-box limitations of Al models, intro- ducing explainability, and enabling error localization and systematic correction.
3. Native support for multi-objective AMP design, balancing antihacterial activity, structural stability, toxicity, and other constraints through structured agent consensus rather than manual or static weighting schemes.
4. A domain-agnostic framework that supports transfcrability beyond AMP generation.
Overall, MAC-AMP sets new benchmark for generative AMP design, surpassing existing models in antibacterial activity, toxicity reduction, and structural reliability while maintaining comparable AMP-likeness. These results demonstrate that closed- reward-driven MAC provides scalable and principled foundation for next-generation molecular design.
2 RELATED WORK
AMP Design Approaches. Traditional generative pipelines for AMP design have used adversar- ial or diffusion models. Early Generative Adversarial Network (GAN) based systems demonstrated feasibility for activity-based AMP generation, such AMPGAN v2, which proposed bidirectional
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conditional GAN to steer peptide properties and showed diverse, novel sequences under conditional control (Van Oort et al., 2021). Recently, a diffusion model, Diff-AMP, unified diffusion based generation with identification, attribute prediction, and iterative optimization in single framework (Wang et al. 2024a). LLLM-based AMP design has also been gaining traction, such as AMP De- signer, foundation model which was able design de novo AMPs with broad-spectrum Gram- negative activity with 94.4% success rate in vitro (Wang et al., (2025.. However, most of these models rely on ad hoc filters or single model scores for selection, making multi-objective optimiza- tion hard to implement, and leaving gap between evaluation outputs and trainable optimization signals that can robustly drive learning.
LLM Multi-Agent Collaboration Systems. LLM agents are being increasingly used in scientific discovery and evaluation. For example, the Virtual Lab employed I.LM-coordinated Al agents to integrate computational protein structure prediction and modelling tools to design 92 novel SARS- CoV-2 nanobodies (Swanson et al. 2025). In materials chemistry, the OSDA (organic structure- directing agent) Agent combines LLM with domain tools for the design of zeolite OSDAs (Hu ctt al, 2025). Seneral-purpose multi-agent frameworks, such as CAMEL for role-playing-ban cooperation Li et al., 2023) and AutoGen for multi-agent dialogue and orchestration (Wu et al., 2024). have laid the groundwork for how agents collaborate and communicate to achieve common goals, demonstrating that such interactive methods can enhance model performance and outcomes. Multi-agent systems have also been explored for expert-like reviewing. For example, Review ReviewAgents Agents coordinates multiple LLM reviewer roles using a structured chain-of-thought dataset to generate comments aligned with human judgments (Gao et al.) 2025). However, while these multi-agent pipelines show strong coordination internally, their outputs are mostly natural language narratives, language-bass based sequences, or heterogeneous scores. There lacks a bridge between multi-agent con- sensus and executable, auditable training signals for downstream optimization. MAC systems are open-loop, relying human-in-the-loow orchestration and trial-and-error prompt- ing or fine-tuning, which often yields lot of non-executable outputs that are difficult to compile into reusable training signals.
addition, many
LLM-enhanced Reinforcement Learning and Automated Reward Design. At the same time, there are studies exploring LLM use to guide RL. For example, RL from AI feedback (RLAIF) can replace or supplement RL from human feedback (RLHF) and actually reports performance on par with RLHF on PaLM 2 (Lee et al. 2024). Another example is Eureka, which uses coding-capable LLMs to generate and iteratively improve reward code, outperforming expert-engineered rewards on 83% of tested tasks (Ma et al. 2023). A recent survey on RL-enhanced LLMs summarizes RL's impressive performance in improving LLM capabilities (Wang et al. (024b). However, existing approaches largely fail integrate automated reward design with multi aalti--agetten consensus or domain- specific evidence.
PROPOSED APPROACH
3.1 MAC-AMP FRAMEWORK
We created MAC-AMP, a closed-loop multi-agent collaboration (MAC) system, that is designed to create novel AMPs optimized for multiple molecular properties. MAC-AMP executes the workflow through six interconnected modules, beginning with an input module and concluding with an output module. The only input required from the user is the target bacterium name and example dataset containing AMPs and their associated minimum inhibitory concentration (MIC) values, which re- flects antibacterial activity. Figure outlines the entire workflow, and it is explained in detail below.
3.1.1 PROPERTY PREDICTION MODULE
This module's role is to predict various AMP properties and aggregate the results into a structured record. To predict antibacterial activity, we developed a target-specific MIC predictor model. It an LLM-based regressor adapted from BERT AmPEP60 that fine-tunes ProtBERT via transfer learning on the input dataset (8:1:1 traintaaliattionttest split). For the remaining molecular properties, we use existing property prediction tools. AMP likelihood is predicted by Macrel 1.5 (Santos-Júmior jet al. 2020), toxicity scores by ToxinPred 3.0 (Rathore et al. (024). structural reliability scores by OmegaFold vI (Wu et al. 2022) (EMBL-EBI AlphaFold Team 2025). physicochemical summaries
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by ProtParam in Biopython 1.85 (Cock et al. 2009), and template similarity scores by Foldseek 10 (Van Kempen et al. 2024). Foldseek 10 selects the 10 best-performing example AMPs from the user-provided dataset (defined by the lowest MIC values) as structural templates and quantifies the similarity between generated AMP candidates and these reference peptides. Each of the property prediction tools is explained in more detail Appendix A Outputs of this module are partitioned into two categories: S, the explicit reward signal for activity; and V, the auxiliary evidence that constrains generation. S includes two items: the antibacterial activity score provided by the target- specific MIC predictor (Sa), and the AMP likelihood score (S,). V includes four items: toxicity scores (Va), structural reliability scores (V)). physicochemical summaries (V.), and template sim- ilarity scores (V2). In the subsequent modules, different agents have different access privileges to these results. To validate the components of this module, we performed ablation studies to assess the necessity of each property prediction tool (Appendix L..I). substitution analyses to validate the ToxinPred 3.0 and MIC predictor (Appendix M.4 M.5] respectively), and Molecular Dynamics (MD) simulations structural reliability proxy (Appendix !.
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AMPs confirm using OmegaFold
3.1.2 AI-SIMULATED PEER REVIEW MODULE
Motivated by committee-style deliberation in academic peer review, this module is multi-agent system that reviews the AMPs. The overall workflow is illustrated in Figureeland described below.
Reviewer Agents. The review committee consists of three independent Reviewer agents (GPT-5, Gemini 2.5, and Perplexity), each with distinct background knowledge to ensure diverse evalua- tions. Inspired by multi-criteria score panels in journal peer review, each Reviewer agent evaluates candidates along four task-specific dimensions using a set of key criteria. This establishes shared evaluation space where opinions from different Reviewer agents can be aligned and aggregated into structured consensus. For AMP design, the four dimensions are efficiency (EFF), safety (SAFE), developmental sequence structure (DevStruct), and originality (Orig). For different tasks, these dimensions and criteria can be customized during preparatory meeting, which is a one-on-one session where human expert and the agent define task-specific requirements, and register these requirements injectable knowledge (see Appendix C.3)
To make free-text reviewing quantifiable, each dimension associated with a weighted lexicon subtable composed of Tags in the format D(State, Weight). Here, ID denotes a key evaluation criterion, and State is a discrete value. The determination of all Tags and their weights is designed by the Reviewer agent and then reviewed and finalized under the supervision of experienced human specialists during a preparatory meeting. The Tags are used to label Reviewer comments structurally by encoding each State numerically (e.g., Low -1, Medium O, High 1). For each dimension,
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Reviewer agent first provides free-text comment and then self-annotates by selecting up to four IDs from the lexicon subtable, creating Tag for each, and assigning a confidence score, The overall score for an AMP is computed by summing the weighted Tags scaled by the confidence score. Fach Reviewer agent outputs comments, Tags, and scores for all dimensions. To ensure that the lexicon weights are decided appropriately by the agent, we performed substitution analysis to test differing lexicon weights, detailed in Appendix M.66
Area Chair Agent. The Area Chair agent processes and combines the outputs of the Reviewer agents. First, it aggregates the results and drafts meta-comment for each dimension based on the individual reviews. It then groups Tags by the same ID and solves semantic conflicts (different States assigned by Reviewer agents). For each dimension, it computes the mean Reviewer agent score, estimates a divergence penalty based on discrepancies in State, and applies this penalty to obtain a final dimension-level meta score. The module produces two outputs: four-line delimited meta-review text (T) and the average meta score (S.). Algorithm 1, detailed in Appendix [B.]] pro- vides the full specification of the module. To validate the components of this module, performed ablation studies to assess the necessity each Reviewer agent (Appendix L.2) and substitution analyses validate the Reviewer and Area Chair agents (Appendix M.3
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Figure 2: Overview of the Artificial Inteligence- Peer Review module. Green text indi- cates the injectable section, while the green boxes denote content obtained from the separate prepara- tory meeting.
3.3.3 RL REFINEMENT AND PEPTIDE GENERATION MODULE
The outputs of the Property Prediction and Al-simulated Peer Review modules are fed into the RL Refinement module, which converts them into trainable optimization signals to automatically shape and adapt the reward function. The overall workflow illustrated in Figure 3 and described in detail below.
Candidate Reward Design. The RL Refinement module begins by cold-starting reward design using evaluation results and consensus from a batch of example AMPs. At each stage, it reads logs from the previous stage, including the current stage index, the reward function F, batch-level explicit signals S (Sa. Sy. Sc). and meta-review text T. This information is first processed by the Computer Science -CS)- Reward Design agent, an AI expert focused on observable signals and their mathematical properties. Guided by stage-specific prompts and constraints, this agent refines
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the reward function based the CS-relevant data (signals S, previous reward function F, stage information), intentionally ignoring the meta-review texts to maintain focus. Operating together, the Biomedical-Base Reward Alignment agent, expert in biomedical and AMP design, analyzes the meta-review texts and integrates domain knowledge to propose alignment-oriented revisions to the CS-based Reward Design agent's candidate reward functions. This agent accesses only the meta- review texts, avoiding distraction by non-biomedical signals, Candidate rewards are then filtered by rule-based validator for executability and constraint compliance, yielding a candidate set of three reward functions.
Next, the module clones the Peptide Generation module into sandbox, runs short simulated training for each candidate reward function, and the RL Reward Decision agent selects the option with the best overall performance via Pareto optimization. The chosen reward function and sandbox logs are returned to the eS-Biomedicaa Reward Design agent team for further refinement. This inner loop iterates three times.
To validate the components of this module, we performed ablation studies to assess the necessity of each component, detailed in Appendix L.3
Peptide Generation Module. After the inner loop, the selected reward function is compiled into a Proximal Policy Optimization (PPO) objective, which guides the Peptide Generation module through AMP design. The generator. inherited from the
architecture (Wang et al. 2025). is a GPT-2 auto-regressive model with a trainable soft prompt that injects domain prior knowl- edge. Generated AMPs are evaluated using the Property Prediction and Al-simulated Peer Review modules, producing batch-level meta-review texts and scores. These evaluations are incorporated into a reward-based PPO strategy to calculate the loss and update generator parameters every epoch.
Stage-Based Adaptive Optimization. A stage is defined as 15 epochs under the same reward function and PPO strategy. At the end of each stage, the RL module aggregates all evaluation logs and adaptively redesigns the reward, yielding an optimized PPO strategy for the next stage. This adaptive redesign repeated three times, allowing the reward function co-evolve with real- time feedback and multi-agent consensus, Algorithm 2, detailed in Appendix B.2, provides the full specification of the module. Although the number of epochs defining stage can be adjusted, we performed substitution analyses to evaluate the effects of increasing or decreasing the default (15 epochs), detailed in Appendix M.I
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Communication Logs. Through every batch and training stage, communication among modules primarily relies on reading and writing structured system logs. Each log contains six fields: Stage Number (0, 1, 2, or JN, where IN denotes inner-loop sandbox logs), reward function (F), S., Sb S, and T. This structured logging ensures end-to-end auditability.
3.2 PROXIMAL POLICY OPTIMIZATION
In MAC-AMP, PPO strategy is instantiated from the stage-selected reward and applied to update the generation module a sequence-leve decision setting. Conditioned on a trainable soft prompt and partial prefix, the policy ma auto-regressively generates a complete sequence 0 (a1,...; (T

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The value network produces token-wise estimates, which are averaged across positions to yield the sequence baseline Va.
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A = norm(R- -
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Lpolicy (0) = ()min(((O)A, clip(((0), -, +
where r(f) is the probability ratio between the current policy and the reference policy for the sam- pled action, > 0 is the PPO clipping hyperparameter, clip(r(0), 1 e, 11+66 clamps this ratio to the interval [1 - e, 1 e], and E[|] denotes expectation over sampled trajectories and time steps.
The value regression term is:
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L= Lppotic + Cv Lyaatt Ge Leett
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The training is conducted in rounds. batch of sequences is sampled, Rt V., and A are computed, and single gradient update is performed on that batch.
In addition, schema-driven prompting framework incorporated to enable multi-agent collabo- ration that is generalizable, transferable, and reusable across wide range of applications and is inspired by human organizational practices. The structural design of this collaboration framework is described in Appendix C
To assess whether stage-wist wise RL MAC-AMP remains stable during training and verify that the Reviewer agents do not induce feedback collapse, overfitting to internal Reviewer agent biases, or reward hacking, additional analyses were conducted and are detailed in Appendix
EXPERIMENTS
4.1 AMP GENERATION TESTING
Bacterial Targets. AMP design performance was evaluated against five bacterial targets. First, Escherichia coli (E. coli). which has been associated with multiple infections and diseases (c.g., urinary tract infections (Totsika al., 2012), neonatal meningitis (Bonacorsi & Bingen| 2005 )-
Four other ESKAPE pathogenic bacterial strains were also analyzed: Staphylococcus aureus (S. aureus), Pseudomonas aeruginosa (P. aeruginosa). Klebsiella pneumoniae (K. pneumoniae), and Enterococcus faecium (E. faecium). ESKAPE pathogens are group of bacteria that the World Health Organization (WHO) and U.S. Centers for Disease Control and Prevention (CDC) flag as major threats because they are leading causes of hoopital-acquiree infections and often display AMR Miller & Arias 2024). Together, these targets also reflect diverse Gram staining profiles, as S. aureus and F., faecium are Gram-positive, while the remaining Gram-negative.
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Dataset Preparation& & Pre-Processing. For each bacterial target, AMP sequences were collected along with their corresponding MIC values. MIC represents the lowest concentration of a compound, or in this case, AMP, that inhibits bacterial growth. This data soureed from two public databases: DBAASP v3 (Pirtskhalava et al. 2021) and dbAMP 3.0 (Yao et al., 2025). AMP sequences were standardized by converting to uppercase, removing whitespace, and retaining only canonical IUPAC letters, and entries with non-standard residues were excluded. Duplicate sequences were removed, and replicate MIC measurements for the same sequence were aggregated using the geometric mean to obtain single value. AMP sequences were represented in IUPAC single-letter codes, and their MIC values (ug//LL) were log10-transee (O-transformed serve as labels. The final datasets contained 3,818 AMP examples for E. coli, 2,644 for S. aureus, 2,458 for P. aeruginosu, 838 for K. pneumoniae, and 352 for E. faecium.
Target-Specific AMP Testing, E. coli, S. awreus, and P. aeruginosa were used to test target-specific design tasks. In each test, one of the bacterial target datasets was provided by the user and passed via the input module. During generation, the generation head produced 1,000 AMP candidates, of which only the top 30 (ranked by predicted MIC) were retained for downstream performance analysis. This procedure repeated three times, resulting in total of 90 AMPs.
Broad-Spectrum Activity Testing. To test the broad-spectrum activity of the generated AMPs, a separate MIC predictor was trained for each bacterial strain, which was then used to evaluate the E. coli-designed AMPs and assessed their generalization across species. Further analyses, including evaluation with external MIC predictor (APEX 1.1) and motif analysis of broad-spectrum AMPs, are detailed in Appendix F
Baseline Models. MAC-AMP was compared against two categories of generative baselines: LLM- based and non-LLM traditional, as well as a real-world AMP dataset. The two LLM-based baselines are AMP Designer and BroadAMP GPT. AMP-Designer is a comprehensive framework for AMP design that integrates GPT, prompt tuning, contrastive learning, knowledge distillation, and RL (Wang et al., 2025). BroadAMP GPT employs transformer-basee
generation and deep learning- guided screening for AMP design (Li et al.) (025). The two non-L.LM-tradition baselines are PepGAN and Diff-AMP. PepGAN is a GAN-based model based on LeakGAN, tfateoof-the-an sequence generator, but incorporates
activity predictor that is trained separately with positive and negative examples together (Tucs et al. 2020). Diff-AMP is a diffusion-based model that, alongside diffusion, employs pre-training and iterative optimization technologies to advance AMP design (Wang et al. (2024)). Details on how baseline model testing was performed, and the real- world AMP dataset was chosen, can be found in Appendix E
4.2 ENVIRONMENT DETAILS AND COMPUTATIONAL COSTS
Experiments were run in PyTorch on NVIDIA A100 GPUs. The AMP generator is GPT-2 small (12 layers, 12 heads, hidden size 768) augmented with a 10-token soft prompt, using a BERT-style amino-acid tokenizer. During PPO, the policy is optimized and GPT-2 value head with Adam (Ir = =5e-5) and gradient-norm clipping at 1.0. The Peptide Generation module uses top-k = 50 /op-pp 0.95 with temperature 1.0.
To train MAC-AMP for AMP prediction on this environment, it took 47.61 GPU hours, 853 API calls, 9106 MB of peak memory, and incurred a total API token cost of $36.56 USD.
4.3 RESULTS
Target-Specifiic AMP Testing, Across the three single-task bacterial targets (E. coli, S. aureus, and P. aeruginosa), MAC-AMP consistently achieves the best antibacterial activity, toxicity, and struc- tural reliability scores, as shown in Table [] This proves that MAC-AMP enhances target-specific efficacy, while imposing effective safety constraints that suppress potential toxicity and implement effective assessments of structural and physicochemical properties that are leveraged to steer AMP generation toward more stably foldable sequences with fewer structural hallucinations. Although BroadAMP-GPP shows slight advantage on AMP likelihood, it performs notably worse on toxicity and structural reliability. This indicates that, without heavily sacrificing AMP discriminability, our approach allocates optimization capacity to multiple objectives, ultimately delivering performance that better aligns with real-world research and development needs.
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Table 1: Property scores of antimicrobial peptides (AMP) generated by MAC-AMP compared to baseline models and real-world AMP datasets across bacterial species
Antibacterial Activity (1)
AMP Likelihood (f)
Structural
Model
Toxicity (L)
Reliability (1)
Escherichia coli (E. coli)
MAC-AMP
0.943 + 0.008
0.797 = 0.012
0.154 = 0.008
0.873 = 0.009
AMP-Designer
0.807 0.021
0.811 + 0.011
0.251 = 0.024
0.817 = 0.017
BroadAMP-GPT
0.831 0.025
=
0.246 = 0.033
0.763 = 0.023
PepGAN
0.823 0.023
0.247 = 0.064
0.637 = 0.026
Diff-AMP
0.822 0.006
0.554 0.036
0.235 = 0.072
0.752 + 0.020
top K
0.894 + 0.014
0.807 + 0.030
0.558 + 0.068
0.846 + 0.022
Staphylococeus aureus (S. aureus)
MAC-AMP
0.931 + 0.007
0.849 = 0.008
0.137 = 0.011
0.837 = 0.009
AMP-Designer
0.809 0.023
0.807 + 0.012
0,225 + 0.022
0.801 + 0.017
BroadAMP-GPT
0.823 0.025
0.858 + 0.014
0.448 # 0.062
0.763 # 0.025
PepGAN
0.901 0.019
0.231 = 0.059
0.644 + 0.021 0.021 0.764 0.023
Diff-AMP
0.926 0.013
0.535 0.023
0.281 = 0.130
Real-World- top K
0.742 0.031
0.543 = 0.070
0.769 = 0.023
Pseudomonas
aeruginosa aeruginosa)
MAC-AMP
0.917 = 0.008
0.851 = 0.006
0.110 = 0.014
0.850 = 0.010
AMP-Des MMP-Designer
0.839 0.018
0,816 + 0.011
0.243 # 0.024
0.799 = 0.019
BroadAMP-GRI
0.842 0.031
0.858 + 0.014
0.449 = 0.067
0.772 = 0.022
PepGAN
0.912 +
0.747 0.013
0.248 + 0.056
0.664 + 0.027
Diff-AMP
0.907 + 0.006
0.594 + 0.022
0.201 # 0.072
0.766 + 0.021
Rorl-World- top K
0.802 0.023
0.785 + 0.031
0.568 + 0.064
0.820 + 0.024
Broad-Spectrum Activity Testing. When evaluating the broad-spectrum potential of the anti-E. coli AMPs, overall, MAC-AMP peptides showed the strongest generalization, achieving the high- est antibacterial activity scores for more non-E. coli species than any other model (Table D E. coli-specific AMPs show excellent generalization in other Gram -egganeeetive species (P. aeruginosa, K. pneumoniae), indicating that the learned physicochemical patterns (e.g., cationic charge density, hydrophobicity, appropriate length) transfer well across Gram-negati negative bacterial strains, likely due to their shared outer membrane architecture. Interestingly, we also observe strong generalization to E. faecium, a Gram-positive species, while S. aureus shows somewhat reduced activity. This sug- gests that while cell envelope structure influences AMP susceptibility, our results demonstrate that effective generalization is achievable across both Gram-negative and Gram-positive species. The strong activity against E. faecium shows that Gram-positive classification doesn't 't preclude broad- spectrum efficacy. Species-specifie factors may modulate activity levels (as with S. aureus), but this simply indicates that some additional optimization or validation may be beneficial for certain targets.
Table 2: Antibacterial activity scores of Escherichia coli-targeted antimicrobial peptides against other bacterial strains
Model
E. coli
S. aureus
P. aeruginosa
K. pneumoniae
E. faecium
MAC-AMP
0.94 0.01
0.81 0.03
0.94 0.00
0.98 + 0.00
0.95 0.01
AMP-Designer
0.81 0.02
0.81 + 0.02
0.85 + 0.01
0.96 # 0.01
0.96 # 0.01
BroAdAMP-GPT
0.83 0.02
0.82 0.03
0.87 2 0.02
0.96 0.01
0.97 0.01
PepGAN
0.82 + 0.02
0.89 + 0.02
0.01
0.98 + 0.00
0.96 # 0.01
DMP
0.82 0.01
0.91 0.01
0.94
0.98 + 0.00
0.93 0.01
Comparison of MAC-AMP and Real-World AMPs. In addition, using Uniform Manifold Ap- proximation and Projection (UMAP), MAC-AMP candidate peptides for E. coli were projected alongside the E. coli AMP training dataset (real-world AMPs) and a UniProt background set

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sisting of peptide sequences extracted from UniProtKB reference proteomes, used represent the broader space of naturally occurring peptides (Consortium 2025). Figure 4 shows that MAC-AMP candidates cluster within or along the edges of the blue density wells defined by real-workd AMPs, rather than being scattered across the broader UniProt space. This spatial alignment indicates that MAC-AMP has learned AMP-like features while also populating multiple subregions of the "AMP manifold", balancing fidelity with exploration of underrepresented neighbourhoods that may yield novel activity. Figure 4 and Figure 4 illustrate two MAC-AMM- -ggnerrred AMPs for E. coli, both of which display canonical AMP chemistries (e.g., Lys/Arg enrichment, aromatic/hydrophoohe residues such as Trp, Leu, Ile, and Val) consistent with electrostatic membrane association and am- phipathic disruption. This supports our finding that MAC-AMP concentrates sampling in biophys- ically plausible, E. coli-relevant regions of sequence space while maintaining chemotype diversity. To further assess similarity, 2000 AM--generated E. coli AMPs were compared against base- line models and real-world AMPs in Appendix K
(b)
(o)
Figure 4: (a) UMAP of the peptide chemical space for Escherichia coli (E. coli) inhibition. Gray dots show UniProt peptides, orange dots show real-world E. coli AMPs, and red dots are MAC-AMP candidate AMPs. Nested blue contours indicate increasing kernel density from these AMPs. (b,c) Sequence diagrams of two example MAC-AMP designed E. coli AMPs.
The 90 generated anti-E. coli AMPs were evaluated for novelty, antibacterial motifs, and structural stability in Appendices G H and respectively. Also, The sequences and biophysical properties of six MAC-AMP-generatte E. coli AMPs are analyzed in Appendix [J] Finally, evaluate MAC- AMP's cross-domain transferability, we tested it on an English tablett-ttext generation task, where the model generates a one-sentence description from table and highlighted cells. The details and results are detailed in Appendix N
5 CONCLUSION
In conclusion, MAC-AMP is the first fully autonomous, closed-loop multi-agent system for AMP design, reframing AMP generation as coordinated multi-agent optimization problem. By trans- lating structured consensus on activity, safety, stability, and novelty into executable reward sig- nals, it enables stable, multi-objective optimization with full auditability. MAC-AMP surpasses existing models in antibacterial potency, toxicity, and structural reliability while maintaining AMP- likeness, exploring underrepresented yet biophysically plausible regions of sequence space. More broadly, it provides scalable, interpretable framework for next-generation molecular design. Lim- itations and future directions are discussed in Appendix O All code and data can be found at
https: thub. com/ CCom/CIMFAP MAC- AMP v1/
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6 ACKNOWLEDGMENTS
This work was supported in part by the Canada Research Chairs Tier Program (CRC-2021- 00482), the Canadian Institutes of Health Research (PLL 185683, PJT 190272, PJT204042, CFA 205059), the Natural Sciences, Engineering Research Council of Canada (RGPIN-2021- 04072.ALLRP 602759-24) and The Canada Foundation for Innovation (CFI) John Evans Leaders Fund (JELF) program (#43481).
7 ETHICS STATEMENT
This paper presents MAC-AMP, a novel model for the design and generation of AMPs. While its pri- mary focus accelerating AMP discovery. MAC-AMP could also be applied in broader biomedical research and be adapted for other domains. At present, we do not anticipate any direct or immedi- ate ethical concerns associated with the development or use of this model. In particular, we have considered the potential societal impacts of MAC-AMP and do not believe there any concerns at this time. MAC-AMP users must apply the model responsibly, ensure transparency regarding its capabilities and limitations, and verify that any outputs are used safely and ethically. Finally, emphasize that MAC-AMP is intended as a research tool to augment human expertise, not to replace critical scientific judgment. We encourage continued evaluation of potential risks and responsible deployment.
8 REPRODUCIBILITY STATEMENT
We have made multiple efforts to ensure reproducibility throughout our study. We have included extensive details in the main manuscript and additional sections in our Appendix that provide algo- rithms and more in-depth explanations of the theoretical concepts.
The complete description of the data processing steps and sources of data is detailed in Sec- tion 4.1 The exact tools used in our Property Prediction module are detailed in Appendix A The algorithms for our Al-simulated Peer Review and RL Refinements modules are detailed in Appendix [ The theory behind our proximal policy optimization and structural design of MAC detailed Section 3..2 and Appendix C
respectively. We have included the exact details of our baseline model training steps in Appendix [ We have also released all code and data at www/CLMMMS / /MAC- AMP vl/ By providing source code, comprehensive details on the methodology, algorithms, training environment, hyperparameters, and data processing, we commit to fully transparent and reproducible research.
hottps://githue

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APPENDIX MAC-AMP: A CLOSED-LOOP MULTI-AGENT COLLABORATION SYSTEM FOR MULTI-OB/ECTIVE ANTIMICROBIAL PEPTIDE DESIGN
APPENDIX TABLE OF CONTENTS
Property Prediction Module Tools
18
B
Algorithms
19
B.1 Al-Simulated Peer Review Module Algorithm
19
B.2
Reinforcement Learning Refinement Module Algorithm
20
Details Regarding Structural Design of Multi-Agent Collaboration
21
Role-based Agent Profile
Sciven System Prompts
22
Knowledge Injection via Preparatory Meetings
22
D
Empirical Analysis of Reward Variance and Closed-Loop Stability
23
Baseline Models Training
23
Independent Validation of MAC-AMP-Gennrated E. coli Peptides
24
F1
Independent Testing of MAC-AMP Generated Anti-E. coli AMP Activity
24
Independent Testing of MAC-AMP Generated Anti-E. coli AMP Sroad-SSpettre
Activity ................................
24
F3
Broad-Spectrum Motif Analysis of MAC-AMP Generated Anti-E. coli AMPs
25
Novelty of coli AMPs Generated by MAC-AMP
26
H
Presence of Antibacterial Activity Related Motif's in coli AMPs generated by MAC-
AMP
26
General Structural Stability of coli AMPs generated by MAC-AMP
27
Sequence-level and Biophysical Properties of E. coli AMPs Generated by MAC-AMP
28
K Comparative Analysis of Sequence and Biophysical Properties of E. coli AMPs Gener- ated by MAC-AMP vs. Baseline Models
29
Ablation Studies
30
L.1
Áblation Studies on Property Prediction Module
30
Ablation Studies Simulated Peer Review Module
Ablation Studies on Reinforcement Learning Refinement Module
32
M Substitution Analyses
33
M. Substitution Analysis Number of External RI. Training Epochs
33
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M.2
Substitution Analysis Maximum Generated Peptide Length
33
M.
Substitution Analysis on Al-simulated Peer Review Module Agents
34
M.4
Substitution Analysis on Toxicity Property Prediction Model
35
M.5
Substitution Analysis on MIC Property Prediction Model
35
M.6
Substitution Analysis on Lexicon Weights
36
Cross-Domain Transferability
36
Limitations and Future Work
37
Use of Large Language Models
37
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A PROPERTY PREDICTION MODULE TOOLS
ToxinPred 3.0. ToxinPred 3.0 is a sequence-based general toxicity predictor for peptides that out- puts probability between 0 and where higher values indicate higher toxicity risk (Rathore al. 2024).
OmegaFold vl. OmegaFold computational tool that predicts the 3D structure of protein directly from its amino acid sequence. Along with the predicted structure, it provides pLDDT score for each residue, which ranges from 0 to as a structural reliability indicator, where higher values indicate higher confidence (Wu et al. 2022 EMBL-EBI AlphaFold Team 2025).
Biopython 1.85. Biopython contains a collection of Python tools for bioinformatics, from which the ProtParam module is used for physicochemical profiling. This module outputs a concise text summary of sequence properties such as length, molecular weight, theoretical isoelectric point, and Grand Average of Hydropathy (GRAVY) (Cock et al. 2009).
Foldseek 10. Foldseek is structure and sequence alignment tool that computes similarity be- tween protein structure sets (including peptide sequences), and reports normalized similarity score between and I, where higher score indicates greater similarity (Van Kempen et al. 2024).
Macrel 1.5. Macrel 1.5 is sequence-based AMP classifier that outputs an AMP likelihood score between and 1, where higher values indicate a higher predicted probability of being an AMP (Santos-Jnnior et al. 2020).
Target -Specific MIC Predictor: We designed an LLM-bassd regressor, adapted from BERT Am-
PEP60 (Cai et al. 2025). that fine-tunes ProtBERT via transfer learning (Elnaggar al. 2022) to predict target-specific MIC values for AMP sequences. In our system, it is automatically trained for each target using the user-provided, real-world AMP dataset. The raw output is the MIC (ug/mL., representing the minimum concentration that inhibits visible growth Wicher 2021). To make it compatible with higher-is-better RL reward, the MIC is transformed using a sigmoid-shaped function into an antibacterial activity score between and 1, inclusive. Higher scores correspond lower MIC values, which reflect stronger target-specific activity.
& Dudek- Kowallsa-KKrchhee
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B ALGORITHMS
B.1 AI-SIMULATED PEER REVIEW MODULE ALGORITHM
Algorithm 1 Al-Simulated Peer Review module Algorithm
1: function REVIEWMODULE((ater lexicons, signmap)
2: for E {RI, }R3 do
3: R[r] RUNREVIEWER(r, batch, lericons)
4: end for
5: AC < AGGREGATEBYACCCR signmap)
6: S COMPUTESCORFS( AC)
7: return AC,S
8:
end function
9:
function RUNREVIEWER(r, batch, (ezicons)
10:
for E {Eff. Safe, DevStruct, Orig) } do
comment a] < generate I 1500 chars based analysis of (S, V) 12: lags (a] + select < 4 (id, state, p) via lexicons; p E {1.00, 0.85, 0.60, 0.40}
11:
13:
score[a] + Σ w(d,, state) p over lags[a]
14: end for
return (comment, tags, score}
16:
end function
17:
function AGGREGATEBYAC(RR signmap) for E Safe,, DevStruct, Orig) do
18:
19:
mela(a] < concise summary of agreements based on all comment
20:
G group all reviewers' tags by id
21:
Dist(a] + sorted signs from signmap for ids with > hits; keep all-zero
22:
Num[a] + [ist[a]]
> number of ids with overlapped states
23:
end for
24: return {meta, G, Dist, Num}
end function
function COMPUTESCORESCRR AC)
27:
for a E (Eff, Safe, DevStruct, Orig) do
28: S(a) < -mean(score(a) over reviewers) list []
29:
for each (id, in Dist [a] do
is tuple of signs, e.g. (-1,-1, or (0, ()
31:
if max([S]) 0 then
all-zero special case
32:
d'id
33:
else
34:
d_id +- mSea
35:
end if
36:
d_id= = then continue
38:
end if append(d list, d id) end for
40:
D[a] t mean(d Jist) if d Jist + / else Taa +- clip/o...1.0 0.6 D(a))
42:
metaja] + >|a]] S[a]]
44:
end for
overall <- mean meean(mtaaa)) over aspects)
46:
return {S, D, 7, meta], overall
47: end function
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B.2 REINFORCEMENT LEARNING REFINEMENT MODULE ALGORITHM
Algorithm Reinforcement Learning Refinement module Algorithm
1: candidates f-
> number of candidate reward functions per inner loop sandbox optimize rounds > agent ++ critical review turns sandbox updates per candidate D outer training updates per stage exploration balance - convergence
2: inner_rounds f- 3
3: dialog max f-
4: sandbox {- 5
5: u_outer <- 15
6: stages (1, 2,
7: function RLMoDULE soodddstatrreriieer..n
8: out t mooedlddelemmm model, moddule)
9: for E stages do
10: f* t- run inner loop(gen model, out, 8)
out +- www.iteerrainnnnnnnnnn..
rewiewer out)
12: end for
13: return out
14:
end function
15:
function RUN LONNENNNRRRRRR LOOP(gen model, out, stage_id id)
16:
snap <- snapshol moode))l model)
for E (1.inn)er_rrumnss] do
18:
c [
for l E {1..candidates) do
20:
f + co no-desigm (origggmottttooo stage.id) id)
f * None then
22:
c.append(f)
23:
end if end for
24:
25:
logs +- run_sandbool (snap, c, u_sandbor)
26:
é rl decision select (logs)
27:
best, I best t- c(m), logs[m]
28:
feedback_to. Btaccktt__ooa wtw.ttoogggntsfffeeeeeeeee best, i_best)
29:
snap f restore(snap)
end for
31:
return febstt
end function
33: function Co DESIGN(Out, stage id)
34:
<- prneel_prrpooxeoon, slage_id)
35:
for E (1..dialog_maz do
36:
pass, cmts t- eritical.revieuup. out, stage_id)
37:
pass = true then break
38:
39:
end
40:
agent_ emts)
41:
end for
if -rule_validdte()) then
43:
return None
end
45: return p
end function
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function RUN SAnDBOxssSaa.. send u)
48:
logs t {
49:
for each f cand
50:
m <- clone(snap)
51:
for u4 E {1..u} do
52:
+ train_step(m,,f)
53:
t -ー reviewer module (m)
54:
append inside log(lossff stage="in", f,
55:
end for 56: end for
57: return logs
end function
function RI. DECISION SELFCT((OgS)
60:
winner <- argmaz..yyrules((png return winner
61:
62:
end function
63:
function RUN JONTORTER..R
TRAIN(gen.model) WWw.OUUTE.TRAINigee....
moviewer out)
64:
for u, E (1..u outer) do Irain serriiislepggen.nnee
65:
f)
66:
{ reviewer reriewrer_module mooddeeeeeeee (gee[gemmmdde)] stage=s, t,
68: end for
return
70:
end function
71:
function WRITE WRITE_OUTSIDE. LONSSot,, stage, r, t)
72:
append(out.agen, record(stage, f, r.sa, r.sb, r.sc)) crtenddout.critical. record(stage. t))
73:
74:
end function
C DETAILS REGARDING STRUCTURAL DESIGN OF MULTI-AGENT COLLABORATION
This section outlines the overarching design principles that govern agent-based modules and the agents within them. Adherence to these structured rules ensures that each agent consistently fulfills its designated role and that inter-agent communication and collaboration remain accurate, stable, and effective.
Inspired by human organizational practices, the MAC framework formalizes roles, operating pro- cedures, and human onboarding practices into unified structure, enabling agents coordinate reliably without altering team composition or core methodology. It comprises three components: (1) role-based agent profile (representing role establishment) that anchors identity and responsi- bility; (2) role-bound operating contract (representing operating manual) specifying standardized input/output formats, workflow steps, startup commands, and reserved slots for additional infor- mation; and (3) an knowledge injection (representing human onboarding practice) via preparatory- meeting decisions. When pivoting to a new task, only the injectable content is updated while profiles and contracts remain unchanged, and inter-agent communication occurs via role-dependent access local and global logs, ensuring structured, stable collaboration.
C.I ROLE-BASED AGENT PROFILE
We define the role-based agent profile the long-lived professional identity of single agent that specifies who the agent is, what competencies commands, what outcomes pursues, and where its responsibility boundaries lie. The profile remains invariant across tasks and domains, enabling the accumulation of role experience. For this part, we adopt the four-anchor specification: Title,
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Expertise, Goal, and Role as introduced by the AI Virtual Lab (Swanson et al. 2025 explained below:
Title: A concise, professional, and task agnostic position name that preciscly denotes the agent's occupational identity.
Expertise: A brief description of the core disciplinary knowledge and methodological capabilities the position relies on, emphasizing a stable competence scope and toolbox.
Goal: A results-oriented statement of the agent's success criteria and optimization target while avoiding overlap with other positions.
Role: A clear charter of responsibility boundaries and decision authority within the collaboration pipeline, together with the obligations and principles that govern interaction with other positions.
C.2 SCHEMA-DRIVER SYSTEM PROMPTS
For single agent, the core elements consist of four parts: Base Model, Parameter Settings, User Prompt, and System Prompt. Base Model refers to the API service on which the agent relies and determines differences in its default knowledge background and working style. Parameter Settings used to adjust the agent's behaviour. User Prompt exists as dialogue and is typically used for intra-team communications the framework. Inter-team communications are performed by reading logs via injecting local or global log entries into the corresponding section in the System Prompt of the agent under access control. System Prompt the primary design target and implements the three-part abstraction: role-based agent profile, role-bound operating contract, and injectable section. We adopt a schema-driven approach to modularize the System Prompt and refine it into the following sections:
Agent Definition: Insert the agent's 's role-based agent profile here.
The first part of the role-bound operating contract. declaring the input structures that the agent may receive. When multiple inputs are required simultaneously. list different input types on separate lines. Common inputs include dialogue history from the User Prompt and log entries injected into the System Prompt.
Input Format:
Workflow: The second part of the role-bound operating contract, providing role-based general workflow guideline.
The third part of the role-bound operating contract, declaring the agent's output format so that free-text responses are constrained to fixed structure and can be written to logs.
Output Format:
Startup Command: The standard command that requests the agent to start working.
Injectable Section: Used for task-specific knowledge customization and injection as functional unit. When needed, inter-team meeting logs or other multi-source inputs can also be injected here.
C.3 KNOWLEDGE INJECTION VIA PREPARATORY MEETINGS
We introduce general, transferable tnowledge-injectoon design that treats task intent as an inter- changeable specification, decoupling domain guidance from the core role to enable rapid cross-task adaptation while preserving reproducibility. The two main parts of the ceowledge-injection design are described below:
For task in new domain, preparatory meeting is held between a human expert and the corresponding agent before deployment. During the meeting, the agent retains only its role-based profile, with the role-bound operating contract temporarily left empty. The human expert describes the new task's 's specific process requirements and, together with the agent, drafts a detailed plan as the meeting output.
Preparatory Meeting:
Knowledge Injection: The finalized content from the preparatory meeting is written into the agent's System Prompt under the Injectable Section the task-specific knowledge and preferences required for subsequent execution. The role-based profile and the bole-bound operating contract remain un- changed.
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D EMPIRICAL ANALYSIS OF REWARD VARIANCE AND CLOSED-LOOP STABILITY
To assess whether stage-wise RI. in MAC-AMP remains stable throughout training and verify that the multi-agent evaluators do not induce feedback collapse, overfitting to internal Reviewer agent biases, or reward hacking, the reward-episode trajectories were analyzed across all three RL stages. This analysis tracked the total reward as well as its components (S., antibacterial activity score; Sp, AMP likelihood score; and S., average meta score) over training episodes and examined how each term evolved relative to the others. In addition, the variance band of the total reward was evaluated over time and compared against the component-wei
trajectories. This allowed detection of potential reward-hacking signatures, such as one sub-term being pushed to an extreme while others degrade, as well as signs of Reviewer agent-signal collapse or instability in the closed-loop feedback.
In Figure Al] the trajectories of Sa. So, and Se display the intended stage-specific behaviour. In the early stage, after an initial drop when the reward is reset, the policy primarily increases S. reflecting an exploration-oriented focus AMP-likeness. In the mid stage, once Sa and Sy have stabilized at reasonable levels, the system increases Se, which aggregates multi-agent assessments involving toxicity, structural reliability, and other constraints. In the final stage, after constraint-relater signals plateau, optimization shifts toward further elevating S, to enhance predicted antibacterial activity.
Across all stages, the total reward rises without any indication of reward hacking. There are tra- jectories in which total reward increases by sharply degrading one component while over-optimizing another. Instead, the three reward components co-evolve stage-wise objectives. Likewise, the variance band of the total reward does not collapse, indicating that the stage- stage-wise reward structure acts as an implicit regularizer that prevents any single objective from being disproportionately exploited within the current three-stage stage training horizon.
manner consistent with the intended
3.25
3.3
-335
20
100 122 340 20
100 120 342
20
302
120
140
Episodes
Episodes
Epicodss
Figure A1: Three-stage reward reward-episode learning curves for MAC-AMP. The curves illustrate how the total reward and its components (S., antibacterial activity score; S,, AMP likelihood score; and Se. average meta score) evolve during (a) the early stage, (b) the middle stage, and (c) the late stage. The light blue area indicates the moving variance of the total reward.
E BASELINE MODELS TRAINING
For all generative baselines, unified protocol used to generate AMPs for each target organism: each model independently generates 1,000 candidate AMPs, evaluates them with the same target- specific MIC predictor to obtain an antibacterial activity prediction score, where a higher score indicates lower predicted MIC value, and then selects the top-k (k=30) from that batch the rep- resentative set for the run. Each model run three times with different random seeds, yielding 3x30 representative sequences for evaluation and statistical analysis. De-duplication is performed across runs and across models using 100% sequence identity. For the real-world dataset, for each target, the data de-duplicated, randomly split into three equal parts, and within each part, sequences are ranked by experimentally measured MIC values from low to high, selecting the top-k (k=30) as that part's 's representative set. This produces 3x30 real-world sequences per target, matching the batch structure of the generative baselines and enabling fair comparison.
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F INDEPENDENT VALIDATION OF MAC-AMP-GENERATHI E. coli PEPTIDES
F.1 INDEPENDENT TESTING OF MAC-AMP GENERATED ANTI-E. coli AMP ACTIVITY
To further validate the 90 BAC-AMM-geeneran anti-E. coli AMPs, an external MIC predictor, APEX 1.1, was used. APEX is an ensemble deep-learning model that combines peptide-sequence encoder with neural predictors of antimicrobial activity (Wan et al. 2024). It was first applied to assess whether each peptide is predicted to be active against at least one of three E. coli strains: E. coli ATCC 11775, E. coli AIC221, and coli AIC222. Of 90 AMPs, 85 were predicted to be active against all three E. coli strains and 5 were predicted to be active against two of the three E. coli strains. Being active was defined as having an MIC < 128 pmoll" as defined in Wan et al. 2024..
F.2 INDEPENDENT TESTING MAC-AMP GENERATED ANTI-E. coli AMP BROAD-SPECTRUM ACTIVITY
APEX 1.1 was then used to evaluate broad-spectrum activity across additional clinically rele- vant pathogens, including Acinetobacter baumannii (ATCC 19606), Klebsiella pneumoniae (ATCC 13883), Pseudomonas aeruginosa (PA01 and PA14), Staphylococeus aureus (ATCC 12600 and BAA-15561 and vancomycin-resise Enterococcus faecium (ATCC 700221). These strains cover major ESKAPE pathogens. As shown Figure A2 many of the generated AMPs exhibit low MIC values across multiple species, indicating broad-spectrun spectrum antimicrobial potential.
500
ALLIWE
An
VEHILL
203
190
1177
ec222
Figure A2: Predicted Minimum Inhibitory Concentrations (MICs) the top 90 MAC-AMP- generated Aath-rschericcia coli (E. coli) peptides against E. coli strains and ESKAPE pathogens using APEX 1.1.
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F.3 BROAD-SPECTRES MOTIF ANALYSIS OF MAC-AMP GENERATED ANTI-E. coli AMPs
Using a MIC cutoff of S 128 pmol - to define activity (aan et al. 2024), the results from broad- spectrum testing with APEX were binarized into active versus inactive for each strain. Among the top 90 MAC-AMP-generater anti-E. coli AMPs, 36 (40%) were classified as broad-spectrum, defined exhibiting activity against at least one strain of each of the five additional ESKAPE pathogens (A. baumannii, K. pneumoniae, P. aeruginosa, S. aureus, E. faecium).
For these broad-spectrum AMPs, motif analysis was subsequently performed to investigate poten- tial sequence features underlying their proad-spectrum activity. Motif analysis was performed us- (ZOOPS)
ing MEME 5.5.8 (Bailey et al. 2009) protein mode under the cero-or-one-oceurene model, searching for up to 10 motifs with widths ranging from 3-10 amino acids, using dataset- derived background amino-acid frequencies, Dirichlet mixture priors, and default EM optimization parameters to identify statistically enriched motifs across the AMP set. FIMO 5.5.8 (Grant et al. 2011) was then used to scan all AMP sequences for motif occurrences using the MEME-generatte motif file, with p-value threshold of 1 x 10 and default settings.
The top ten most frequent motifs identified across the broad-spectrum AMPs are shown in Figure A3 From this, two particularly notable conserved motifs
identified: KFLKGA and WLLGKW. The KFLKGA motif, although not experimentally validated the literature exactly, exhibits an alternating pattern of cationic (K) and hydrophobic (F, L,, A) residues characteristic of amphipathic AMPs and fits the cationic sationicchydrophobbic pattern typical of cationic AMPs. Similarly, the WLLGKW motif follows the same fundamental design principles, featuring a central lysine (K) residue flanked by hydrophobic tryptophan (W) and leucine (L) residues that create an amphipathic structure. The two tryptophan residues are particularly significant, as their large aromatic structures preferentially position at the membrane-ward water interface, potentially enhancing membrane membrane-disruping -isruptiig activity. Both motifs are expected to assist the AMPs in targeting bacteria through the characteristic two-step mechanism: initial electrostatic attraction between the cationic lysine residues of the AMP and the negatively charged bacterial membrane surface, followed by hydrophobic insertion of tryptophan and leucine residues into the lipid bilayer. The resulting membrane perturbation and disruption lead to bacterial cell death (Hollmann et al.) 2016)) Yeaman & Yount 2003). Overall, this goes to show that there is the potential that the alternating cationic and hydrophobic pattern contributes to the broad-spectrum activity of the generated broad-spectrum AMPs.
KFUKGA
32
WILGON
RX1
Motif
MRWWWRW
8G-
DRL
LGE
5
10
30
Number of Occurrences
Figure A3: Frequency of the top ten motifs identified in broad-spectrum MAC-AMP- -ennerte anti-Escherichia coli antimicrobial peptides.
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G NOVELTY OF E. coli AMPS GENERATED BY MAC-AMP
To ensure novelty, sequence similarity was quantified between the MAC-AMP-generacte E. coli AMPs and the E. coli AMPs in our training dataset (3,818 experimentally validated AMPs from DBAASP v3 and dbAMP 3.0). The Needleman-Wine
global alignment algorithm implemented Biopython 1.8.6 was used with default parameters. For each pairwise comparison, similarity was calculated by normalizing the alignment score to the length of the longer peptide in the pair.
Because each generated peptide required comparison against thousands of database sequences, we report, for each generated AMP, the highest and average similarity score observed across all compar- isons. As shown Figure A4 the generated AMPs only show a maximum similarity score of 84.6% to existing AMPs, indicating that even the most similar generated AMPs retain approximately 15% sequence divergence. On average, their similarity to the training dataset sits around 27%, indicat- ing substantial sequence-level differences from known AMPs. Thus, the generated AMPs exhibit consistently low similarity to known AMPs, indicating high sequence-lever novelty.
(a)
(b)
+
17.5
15.0
Fecuaacy
10
1.5
3.0
2.5
0.20
0,22
0.24
0.26
0.28
0.30
0.50
0.55
0.60
0:55
0.70
0.75
0.80
0.85
Average Normalzzed Similrtty Score
Highest Normalzed Similartty Score
Figure A4: Distribution of (a) average and (b) highest normalized Needleman-Winn alignment similarity scores for the top 90 MAC-AMP MMC-AMP--enerrated aati-Escherichaa coli (E. coli) an- timicrobial peptides (AMPs), evaluated against external dataset of real-world coli AMPs.
global-
The internal similarity among the 90 MAC-AMP-ggenertted generated E. coli AMPs are also assessed using the same alignment procedure described above. Pairwise global alignments were computed for all combinations of generated AMPs, and similarity scores were normalized by division of the maxi- mum sequence length. As shown in Figure A5 internal similarity across the generated set remains low, indicating that MAC-AMP produces diverse set of AMP sequences rather than minor varia- tions of a few amino acids.
H PRESENCE OF ANTIBACTERIAL ACTIVITY RELATED MOTIFS IN E. coli AMPS GENERATED BY MAC-AMP
To further validate the MAC-AMP results in silico, the top 90 anti-E. coli AMPs generated by MAC- AMP were analyzed for the presence of motifs experimentally associated with antibacterial activity, particularly against E. coli.
search was conducted for the Cholesterol- -eeeoggiol-RReoogiiiingn Amino-Acid Consensus (CRAC) motif and its reverse (CARC), which interact with cholesterol in cell membranes. influence cholesterol- dependent cellular processes, and modulate membrane permeability and stability. CRAC motifs may contribute antimicrobial activity by sequestering cholesterol sterol-like lipids, disrupting mem- brane organization, and forming pores in bacterial membranes. CRAC-containing peptides have demonstrated activity against E. coli in previous studies, with CRAC motifs appearing necessary for this effect (Koksharova et al. (2022). Within the generated AMPs, 8 of 90 contained CRAC or CARC motifs, providing additional evidence for activity against E. coli.
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KKILHFEJRRL
RILRWUIN
VULL LAB
RILIVE
KNW
0.8
SUKWIC EwNNC
ACRWINN
VEHRILA
ALFRKLAKLOKI
Pogitdes
KAAAKWAK AKIR
KIKGWI
FLAKEE FSL SEA
VARAEPR
WE
ALARL
RGIL BOILBRRLAAC
NOOLL HOLLIMPEE PULLA
RILAWER VULLLA
SUKWIER
VSFU VSFURACLL 5640
ALFRCLAKLOR
HhiS
FLAUC
Si COLPEE
nu koo
Peptides
Figure A5: Normalized Needleman- Wunsch giobal-alignment-bane basse similarity scores between top 90 anti-Escherichia coli generated antimicrobial peptides by MAC-AMP.
Another example searched for was proline-rich AMPs (PR-AMPs), which are AMPs with an unusu- ally high content of proline residues and a net cationic charge derived mainly from arginine residues. These peptides have been shown in the literature to demonstrate antibacterial activity. Two AMPs proline-riche rich: KRRWEPLPPPPRPPEER and RPREPPRYP.
from the generated sequence PRLYPPVRP. Based on the literature, these peptides likely exhibit anti-E. coli activity due to their characteristic proline-rich and arginine-rich composition, hallmark of PR-AMPs that selectively target Gram-negative bacteria. The first AMP KKREWRPPLPPPRREFRR)) contains an N-terminal arginine-rich region, which has been shown to influence antimicrobial activity, along with multiple proline residues arranged in patterns similar to functional PR-AMP fragments. The second AMP RPPEPPRYYPRLYPPVRR displays repeating Pro-Arg motifs (particularly PRP sequences) that are characteristic of active PR-AMPs (Scocchi et al., 2011).
Overall, this provides some evidence into the mechanisms and validity of the AMPs designed by MAC-AMP to target E. coli in silico.
T GENERAL STRUCTURAL STABILITY OF E. coli AMPS GENERATED BY MAC-AMP
OmegaFold pLDDT is used as structural reliability proxy to guide MAC-AMP during peptide generation. To confirm that MAC-AMP produces generally stable peptides and that OmegaFold PLDDT is a reliable proxy, molecular dynamies (MD) simulations were performed on subset
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of randomly chosen E. coli AMPs from the top 90 candidates generated by MAC-AMP to assess general structural stability.
MD simulations were performed using OpenMM 8.4.0 (Eastman et al. 2024) to assess the structural stability of the generated AMP structures. Input Protein Data Bank (PDB) files were first processed using PDBFixer 1.12 to add missing residues, missing heavy atoms, and hydrogens pH 7.0. Each prepared structure was then solvated in a TIP3P water box with 0.8 nm padding and 0.15 ionic energy mnergy-minimized, followed by of
strength using the AMBERI4 force field. The systems NPT equilibration at 300 K and I bar using a Langevin integrator with a fs timestep and Monte Carlo barostat. Production MD simulations were conducted for 100 ns with coordinates saved every ps. Backbone Root Mean Square Deviation (RMSD) values were calculated using MDTraj 1.11 McGibbon et al., 2015), which measures the average distance between the backbone atoms of peptide structure over time compared to the initial structure. General conditions were used to simply assess the reliability of OmegaFold to guide the design of AMPs with structural reliability.
Table Al shows that the mean RMSD lies around 2-4 Åfor the AMPs generated by MAC-AMP. This shows that the AMPs designed by MAC-AMPs demonstrate general structural stability and also that using Omegaloodd pLDDT proxy to guide the generation of AMPs is acceptable.
Table Al: Backbone root mean square deviation (RMSD) of molecular dynamics simulations of MAC-AMP-generateed anti-Escherichia coli antimicrohial peptides (AMPs) (mean + standard devi-
ation)
AMP
RMSD (Å)
AMP
RMSD (Å)
AMP
RMSD (Å)
AMP
RMSD (Á)
1
2.22 # 0.79
17
3.18 = 1,49
33
2.50 0.79
49
2.57 = 0.67
2
+
18
2.25 +
34
3.66 = 0.64
50
2.09 + 0.72
3
2.16
19
1.31
35
2.68 # 1.00 1.00
51
2.61 0.70
4
3.40 1.85
20
0.86
36
1.88 0.48
52
4.68 1.20
5
2.17
21
2.17
37
3.25
53
3.63 1.84
6
2.53
22
+.52
38
3.70
2.49
7
4.04
23
0.62
39
3.07
55
3
8
2.91
0.55
24
0.57
40
1.00 3.53 1.00
56
2.54
9
0.51
25
1.65
2.23
57
4.59
10
0.72
26
0.60
42
3.26
58
1.68 0.70
11
0.88
27
3.75
2.67
2.81
12
0.65
28
2.70
44
+
60
1.91 +
13
3.84
29
1.80
45
2.23 2.65
2.65 0.69
14
2.87
30
1.79
46
4.00
62
1.63
15
4.96 + 1.19
31
1.79
47
3.79
+.80
16
2.91 # 0.99
32
3.21 + 1.31
48
2.32 # 1.15
64
3.36 + 0.84
J SEQUENCF-LEVEL AND BIOPHYSICAL PROPERTIES OF E. coli AMPS GENERATED BY MAC-AMP
Here, sequence-level (Table A2) and biophysical (Table A3 features summarized for six gen- crated AMPs by MAC-AMP for E. coli inhibition. In Table A2 the Length column indicates the number residues in the peptide sequence. The K/R Fraction column reports the fraction of lysine (K) and arginine (R) residues. The K/R Fraction Positions column shows the positions of K and R residues within each sequence. The Identity Fraction gives the proportion of residues identical to the reference template sequence used during generation. In Table A3 GRAVY represents the Grand Average of Hydropathy, quantifying the overall hydrophobicity the peptide sequence. Hydropho- bic Moment (a-helix) measures the a-helical amphipathicity, indicating the degree of segregation between hydrophobic and hydrophilic faces when the peptide adopts an a-helical conformation. Net Charge pH 7 indicates the total charge of the peptide under physiological conditions, pl gives the isoelectric point, which the pH at which the peptide carries no net charge.
The generated peptides exhibit key characteristics associated with antimicrobial activity: moderate to high cationic character (K/R fractions of 0.32-0.41, net charges of +6 to +9), balanced to mod- erately high hydrophobicity (GRAVY values of 0.10575, and moderate to strong amphipathicity
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(hydrophobic moments of 0.38-0.73). Notably, all sequences maintain high pl values (11.25-12.97), ensuring they remain positively charged under physiological conditions, which facilitates electro- static interaction with negatively charged bacterial membranes. The identity fractions (0.50-0.60) indicate that MAC-AMP modified 40-50% of residues relative to template sequences to optimize antimicrobial properties.
Table A2:
Summary sequence-level features for six generated antimicrobial peptides by MAC-
AMP for Escherichia coli
K/R
K/R Fraction
Identity
Sequence
Length
Fraction
Positions
Fraction
FRVEGFIAKKVKKLVKKI
18
0.389
1.8.9.1..2.15,16
0.556
VRGGAIKKAKKLAKKLLA
19
0.316
1,6,7,10,14,18
0.579
VGLVKKKFFKSVIKKKAKKS
18
0.333
1,5,8.12,13,16
0.500
RIFKFLKKRAFFIIIGLFRKRRRR
22
0.364
0,3,677.16,17,18,20
0.500
KIWKLLKKVLAKVAK
15
0.400
0,3,6,7.11,14
0.600
IGKLVLAKVGKIKKKIIKKK
22
0.409
3778.1,,14,1,111192200
0.500
Table A3: Summary of biophysical properties for six generated antimicrobial peptides by MAC-
AMP for Escherichia coli
Hydrophobic Moment
Sequence
GRAVY
Net Chargepir pH17
pl
FRVEGHAAKKVKKIYKKI
0.406
0.734
7
11.95
VRGGAIRKIAKKLAKKLLAR
0.574
0,375
6
12.52
VGLVKKWFRSVIKKKAKS
0.189
0,546
6
11.25
RIFKELKRAAFGIIGLIFRRRKKS
0.155
0.578
8
12.97
KINKLLKKVLAKVAK
0,240
0.721
11.25
IICK.VIKKVGKIKKIIIKKS
0.373
0.427
9
11.45
K COMPARATIVE ANALYSIS OF SEQUENCE AND BIOPHYSICAL PROPERTIES OF E. coli AMPS GENERATED BY MAC-AMP vs. BASELINE MODELS
To evaluate sequence and biophysical properties, 2,000 random AMPs were generated by MAC- AMP for E. coli and compared with equal-sized sets from baseline models and the training dataset of experimentally validated E. coli AMPs (Figure A6). Overall, MAC-AMP generates AMPs with distributions similar to experimentally validated anti-E. coli AMPs while showing enhanced op- timization for key properties associated with antimicrobial activity. In Figure A6μ, MAC-AMP's amino acid composition shows elevated K/R and L/I, consistent with cationic, amphipathic helices, while remaining broadly similar to real AMPs. Figure (Λb illustrates that MAC-AMP peptides peak global charge of +5-7 pH 7.4, reflecting a design preference for increased membrane interaction. GRAVY distributions (Figure A6c) indicate moderate hydrophobicity (me- dian ~0 +0.5), slightly more hydrophobic than natural AMPs, which may enhance membrane insertion potential while maintaining solubility. Predicted helix fractions (Figure A6lJ) are slightly elevated (~-0.45-0.5), supporting formation stable amphipathic helices. Sequence lengths (Fig- ure A6e) are predominantly 17-20 aa, well within the typical 10-40 aa AMP range. Also, Eisen- berg hydrophobic moments (Figure A6F are highest among all models (~0..5 median), producing well-defined amphipathic faces without extreme hydrophobic clustering, which could lead to aggre- gation or reduced solubility. Finally, the instability index (Figure A6g) and global hydrophilicity FFigureA6hh distributions comparable experimentally validated AMPs, indicating that MAC- AMP maintains stability and solubility profiles consistent with functional peptides. Collectively, this indicates that MAC-AMP maintains the statistical fidelity of natural AMPs while optimizing charge, amphipathicity, and helicity to favour membrane-active designs.
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(8)
(b)
0.200
Oeaigeer DillAM
Nossa Mic 20ar
0.355
0.30
0..33
0.25
Teeddon
0380
0.23
0.33
8525
0.93
2.00
185 215
Amino azids
Global charge at 7.4
(c)
(d)
Model
Model
(0)
1.4
Sequerce Lerdn
0.4
4FPO000000 Braa OF
Pardtn
Heil Workd APP
MAC AMP
Dift Cnl
100
bybabiiyy
2 2-
140 Deiiyee Broedable OPP
Prybara
Weat Worid 201 WM AMP
L0MP Broadbie
Year War
MAd 4PP
Model
Figure A6: Comparative distributions of sequence and biophysical properties across 2000 random antimicrobial peptides (AMPs) generated by MAC-AMP, five baseline models, and a real-world AMP dataset targeting Escherichia coli. (a) Amino-acid composition AMPs, normalized to per- model fractions.
(b) Global charge at pH 7.4 for all AMPs. (c) Grand Average of Hydropathy (GRAVY) of AMPs, representing overall hydrophobicity. (d) Predicted helix fraction of AMPs, in- dicating their propensity to adopt a-helical structure. (e) AMPs length in amino acids. (f) Eisenberg hydrophobic moment of AMPs, reflecting amphipathicity. (g) Instability index of AMPs, estimating their predicted stability. (h) Global hydrophobicity of AMPs.
L ABLATION STUDIES
L.1 ABLATION STUDIES ON PROPERTY PREDICTION MODULE
Ablation studies were conducted on the Property Prediction module by comparing MAC-AMP to versions where each of the following additional objectives removed in the Property Prediction module: Toxicity (V.). Structural Reliability (V,,, and AMP Likelihood (S.).
The performance metrics of the generated AMPs by each variant compared to MAC-AMP are sum- marized in Table A4 The computational costs were also measured via GPU hours, total number of API calls, peak memory usage in MB, and API costs, shown in Figure A7 As expected, although computational costs drop when multiple objectives are removed, it comes at the cost of performance. When each combination of objectives is removed, the model can optimize for the remaining ob-
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jectives (instead of having to balance all three), so it produces slightly higher performance metrics for those optimized remaining objectives at the cost of the other objectives. The slight increase noted in the optimized objectives when others are dropped is not worth the trade-off of the multi-objective optimization. Overall, MAC-AMP can effectively optimize AMP generation for multiple objectives once when all components included.
Table A4: Ablation studies of the Property Prediction module: property scores of antimicrobial pep- tides generated by MAC-AMP and its variants. Here, Va denotes the toxicity predictor (ToxinPred 3.0), Vi, the srriabiil-reliiailltyy property predictor (OmegaFold vl), and So the AMP-likelihood predictor (Macrel 1.5).
Antibacterial Activity (1)
AMP
Structural
Model
Toxicity ())
Likelihood (*)
Reliability (f)
MAC-AMP
0.943 + 0.008
0.797 + 0.012
0.154 + 0.008
0.873 + 0.009
Drop_V,
0.904 = 0.028
0.799 = 0.009
0.157 + 0.021
0.818 + 0.012
Drop_Va
0.946 + 0.045
0.782 = 0.016
0.022
0.799 + 0.017
Drop_S,
0.923 = 0.037
0.742 = 0.006
0.164
1.800
Drop_Va-1
0.904 = 0.043
0.764 = 0.023
0.758
Drop V, Sb.
0.889 = 0.040
0.745 = 0.035
0.087
0.763
Drop Vn Sb
0.933 + 0.021
0.735 + 0.010
0.255 + 0.024
0.836 0.019
Drop_ V.
0.866 + 0.044
0.729 + 0.024
0.212 + 0.045
0.742 + 0.031
60)
(0)
029
13 ledurd
Sgp
Figure A7: Ablation studies of the Property Prediction module. (a) API costs for each ablation model, broken down by the percentage contribution of each Reviewer Agent: GPT-5, Perplexity, and Gemini 2.5. (b) GPU hours, total number of API calls (further separated into percentages from the Reinforcement Learning Refinement module versus the Artificial Intelligence-simulated Peer Reviewer module), and peak memory usage (MB) for each ablation model. Here, V, denotes the toxicity predictor (ToxinPred 3.0), Vo the structural-reiiabiliy property predictor (OmegaFold vI), and Sy the AMP-likelihood predictor (Macrel 1.5).
L.2 ABLATION STUDIES ON AI-SIMULATED PEER REVIEW MODULE
Ablation studies were conducted on the contribution of the Al-simulated Peer Review module by comparing MAC-AMP to versions where each of the Reviewer agents was removed in the Ai- simulated Peer Review module. As shown in Table A5, the Al-Simulated Peer Review module is key to achieving a balanced multi-objective outcome. When all Reviewer agents are removed, and only RL with handcrafted rewards is retained, all four metrics deteriorate. The three Reviewer agents have distinct roles and counterbalance one another. GPT-5 (RA1) functions as the gatekeeper of safety and discriminability. When GPT-5 is included, toxicity scores decrease and AMP scores remain high, although antibacterial activity and structural reliability scores are slightly reduced. Conversely, removing GPT-5 produces candidates that appear more potent but exhibit increased toxicity and reduced stability. Perplexity (RA2) acts as the primary driver of potency. Removing Perplexity reduces all four metrics, whereas using Perplexity alone increases antibacterial activity scores but leads to lower AMP scores, higher toxicity, and weaker structural stability, indicating unilateral bias when applied in isolation. Gemini 2.5 (RA3) primarily mediates the synergy between structural reliability and potency. Removing Gemini 2.5 results in pronounced decline in structural
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reliability, while using Gemini 2.5 alone fails to balance safety and overall performance. Together, the three Reviewer agents form a closed loop: Perplexity pushes sequences into the effective region, GPT-5 pulls risk back into controllable range, and Gemini 2.5 improves foldability, ultimately delivering the most balanced results across all four metrics.
Table A5: Ablation studies of the Al-simulated Peer Review module: property scores of antimi- crobial peptides generated by MAC-AMP and its variants. Here, RAI denotes Reviewer agent (GPT-5), RA2 the Reviewer agent 2 (Perplexity), and RA3 the Reviewer agent 3 (Gemini 2.5)
Antibacterial Activity (t)
AMP Likelihood (t)
Structural
Model
Toxicity (L)
Reliability (1)
MAC-AMP
0.943 + 0.008
0.797 + 0.012
0.154 + 0.008
0.873 + 0.009
w/o RA3
0.857 0.016
0.840 0.008
0.133 0.023
0.731 0.017
w/o RA2
0.884 + 0.015
0.780 = 0.008
0.164 = 0.023
0.734 + 0.021
w/o RAI
0.953 0.010
0.742 = 0.016
0.181 = 0.019
0.783 + 0.017
w/o RA2 RA3
0.870 0.033
0.806 + 0.006
0.130 0.015
0.740 = 0.011
w/o RAI RA3
0.979 0.010
0.700 0.019
0.185 = 0.019
0.758 = 0.015
w/o RAI RA2
0.938 0.011
0.795 = 0.008
0.195 = 0.013
0.862 = 0.013
w/o All Review
0.825 + 0.016
0.627 + 0.014
0.448 + 0.051
0.831 + 0.012
L.3 ABLATION STUDIES ON REINFORCEMENT LEARNING REFINEMENT MODULE
Ablation studies were conducted on the contribution of the RL Refinement module by comparing MAC-AMP to versions where the RL Reward Decision agent and/or Adaptive Optimization were removed or replaced with human-designed RI., where the reward and PPO code were designed by a human expert. In this set of RL module ablations, MAC-AMP, with both the RL Reward Decision agent and Adaptive Optimization, achieves the most balanced performance across all metrics (Ta- ble A6). In more detail, without the RL Reward Decision agent, all property metrics drop except for structural reliability, which slightly increases, leading to a clear safety degradation. Without adaptive optimization, antibacterial activity superficially increases while all other properties drop, reflecting overexploitation under a reward and PPO strategy. As such, it is clear that the stagewise up- dates act as regularization and calibration, preventing the search from being pulled too hard by one specific objective. Without either, performance degrades across the board except for toxicity, likely because the static reward places heavy penalty on toxicity. In short, the RL Reward Decision agent determines whether the right trade-off was chosen, and Adaptive Optimization determines whether it is stable. Both are indispensable for reaching balanced multi-objective optimum.
Table A6: Ablation studies of the Reinforcement Learning Refinement module: property scores of antimicrobial peptides generated by MAC-AMP and its variants
Antibacterial
AMP Likelihood (t)
Structural
Model
Toxicity (4)
Activity (1)
Reliability (t)
MAC-AMP
0.943 + 0.008
0.797 + 0.012
0.154 + 0.008
0.873 0.009
Without RL Decision
0.935 + 0.011
0.791 = 0.013
0.260 + 0.022
0.879 0.010
Agent
Without Adaptive
0.955 + 0.003
0.752 - 0.005
0.279 = 0.006
0.869 = 0.013
Optimization
Without RL Decision
Agent & Adaptive
0.818 + 0.021
0.737 - 0.009
0.200 + 0.022
0.768 = 0.020
Optimization
Replaced by
0.900 + 0.014
0.776 + 0.009
0.175 # 0.018
0.015
Hluman-Designed RL
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M SUBSTITUTION ANALYSES
To validate our model's performance, substitution analyses were conducted where different com- ponents of our model framework were swapped, and computational costs and/or performance were evaluated in comparison to the original framework. For each analysis, downstream performance was measured via the generated AMPss antibacterial activity, AMP likelihood, toxicity and structural re- liability. When computational costs were calculated, GPU hours, total number of API calls (broken down by the percentage coming from the RL Refinement module versus the Al-simulated Peer Re- view module), peak memory usage, and API costs (broken down by the percentage contributed by each Reviewer agent) were noted.
M.1 SUBSTITUTION ANALYSIS ON NUMBER OF EXTERNAL RL TRAINING EPOCHS
In MAC-AMP, the RL Refinement module is trained for 15 epochs by default. To evaluate the effect of training duration, the number of epochs was reduced to 10 and increased to 20, and computational costs are reported in Figure A8 Training for 15 epochs incurs only an additional $0.46 USD com- pared to 10 epochs, whereas increasing to 20 epochs raises API costs by S5.62 USD. GPU hours increase with the number of epochs, while peak memory usage remains largely unchanged. To note, MAC-AMP allows users to choose their desired number of external RL training epochs depending on their preferred balance between accuracy and computational cost.
16)
(6)
Co
20
Confountie
Figure A8: Substitution analyses on the number of Reinforcement I Learning module training epochs. (a) API costs for each substitution variant, broken down by the percentage contribution of each Re- viewer Agent: GPT-5, Perplexity, and Gemini 2.5. (b) GPU hours, total number of API calls (further separated into percentages from the Reinforcement Learning module versus the Al-simulated Peer Review module), and peak memory usage (MB) for each substitution variant.
M.2 SUBSTITUTION ANALYSIS ON MAXIMUM GENERATED PEPTIDE LENGTH
In MAC-AMP, the default maximum peptide length is 32, reflecting focus designing short AMPs. The effects of lowering the maximum length to 20 and 26 or increasing to 39 were explored, and shown in Figure A9 the computational costs due to differences in peptide length are negligible, so sequence length does not have a significant impact on computational costs.
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809
Model
Figure A9: Substitution analyses on the maximum length of generated peptides. (a) API costs for each substitution variant, broken down by the percentage contribution of each Reviewer Agent: GPT-5, Perplexity, and Gemini 2.5. (b) GPU hours, total number of API calls (further separated into percentages from the Reinforcement Learning module versus the Artificial Peer Review module), and peak memory usage (MB) for each substitution variant.
M.3 SUBSTITUTION ANALYSIS ON AI-SIMULATED PEER REVIEW MODULE AGENTS
MAC-AMP currently uses GPT-5, Perplexity, and Gemini 2.5 as Reviewer agents and the Area Chair agent. To evaluate cost differences, the agents were replaced with locally deployed small models in four configurations: RA-AC.Llama, in which all Reviewer agents and the Area Chair agent in the Peer Review module were replaced with Llama 3.1 8B (Grattafiri & Dubey 2024); RA AC Qwen, in which all Reviewer agents and the Area Chair agent were replaced with Qwen 2.5 7B-instruct Qwen al.) 2024]; RA.APLAC.OO Awen,en, in which only the Area Chair agent was replaced with Qwen; and RA Qwen_AC_An ACIAPI, in which only the Reviewer agents were replaced with Qwen. As shown in Table A7 MAC-AMP achieves the most favourable balance across all evaluated metrics, particularly excelling in toxicity (0.154) and AMP likelihood (0.797), while maintaining competitive antibacterial activity and structural reliability. Variants that obtain higher antibacterial activity or structural reliability do so at the cost of substantially increased toxicity (2-3x worse) and reduced AMP likelihood. Because all objectives are jointly prioritized, the MAC-AMP agents represent the most effective choice overall. Although MAC-AMP incurs the highest API and computational costs (with the exception peak memory), as shown in Figure (A0, performance is prioritized over computational efficiency in this work, and the resulting costs remain acceptable for the intended use. Therefore, MAC-AMP remains the preferred configuration.
Table A7: Substitution analyses the Artificial Intelligence-simulace Peer Review Module Agents: property scores of antimicrohial peptides generated by MAC-AMP and its variants. RA all Reviewer agents (RA) and the Area Chair (AC) agent are replaced with Llama 3.1 8B, RA AC. Qwen: all RA and the AC agent are replaced with Qwen 2.5 7B-instruct, W.L.PLAC..eert only the AC agent replaced with Qwen, AC only the RA are replaced with Qwen
Antibacterial
AMP
Structural
Model
Toxicity (L)
Activity (t)
Likelihood (t)
Reliability (1)
MAC-AMP
0.943 = 0.008
0.797 = 0.012
0.154 = 0.008
0.873 = 0.009
RA AC Llama
0.944 + 0.007
0.713 + 0.012
0.201 0.020
0.896 + 0.012
RALAC.Qwen
0.949 + 0.009
0.683 + 0.010
0.308 + 0.013
0.913 + 0.016
RA_APIAC Awen
0.865 + 0.014
0.783 + 0.011
0.206 + 0.013
0.863 + 0.007
RALQwen_Aee API
0.835 + 0.023
0.734 + 0.017
0.211 + 0.030
0.854 + 0.015
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Pertorr
Anto Lik Fo000
Can 1000
Figure A10: Substitution analyses on the Al-simulated Peer Review Module Agents. (a) API costs for each comparative model, broken down by the percentage contribution of each Reviewer Agent: GPT-5, Perplexity, and Gemini 2.5. (b) GPU hours, total number of API calls (further separated into percentages from the Reinforcement Learning module versus the Artificial Intelligence-smmllated Peer Review module), and peak memory usage (MB) for each comparative model.
M.4 SUBSTITUTION ANALYSIS ON TOXICITY PROPERTY PREDICTION MODEL
For MAC-AMP, ToxinPred 3.0 is used as the toxicity property prediction model. At the time of ToxinPred 3.0's release, it remained one of the top general toxicity prediction models. CAPTP was tested as an alternative to determine whether ToxinPred 3.0 is the superior choice. CAPTP is a deep learning model that combines convolutional and self-attention mechanisms, using convolutional lay- ers to extract local sequence motifs, self-attention layers to capture long-range dependencies across the peptide, and fully connected layers integrate these learned features for highly accurate toxicity prediction (Jiao et al. 2024).
ToxinPred 3.0 and CAPTP were both evaluated on an independent dataset originally used test ToxinPred 3.0, ensuring that none of the compounds overlapped with the training sets of either model. As detailed in Table A8 ToxinPred 3.0 outperforms CAPTP.
Table A8: Performance comparison between ToxinPred 3.0 and CAPTP
Predictor ToxinPred 3.0
Accuracy
Weighted Precision
Weighted Recall
Weighted Fl-score
0.828
0.849
0.828
0.828 0.770
CAPTP
0.770
0.787
0.77
M.5 SUBSTITUTION ANALYSIS ON MIC PROPERTY PREDICTION MODEL
For MAC-AMP, designed an LL.d-based regressor (MAC-AMP MIC Predictor). adapted from BERT AmPEP60 (Cai et al.] 2025). that fine-tunes ProtBERT via transfer learning (Elnaggar et al.) 2022 predict species-specific MIC values for AMP sequences. The designed MIC prediction model was compared to APEX 1.1, an ensemble of deep-learning networks that uses a peptide- sequence encoder coupled with neural predictors of antimicrobial activity (Wan et al. 2024).
Both models were evaluated on a collection of 157 AMPs targeting E. coli ATCC 11775, E. coli AIC221, and/or coli AIC222 from DBAASP v3, after removing duplicates and any overlap with our training dataset. As shown in Table A9 our predictor outperforms APEX. Therefore, using our self-trained MIC predictor within the framework is the optimal choice.
Table A9: Performance comparison between MAC-AMP MIC Predictor and APEX 1.1
Predictor
R2
Pearson r
Spearman
MAC-AMP MIC Predictor
0.572
0.758 0.728
0.724
APEX 1.1
0.546
0.607
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M.6 SUBSTITUTION ANALYSIS ON LEXICON WEIGHTS
The lexicon weights in the weight-tagging map of the Al-simulated Peer Review module are deter- mined by the agent itself during preparatory meeting with a human expert, subject constraints such as restricting the possible distribution of each dimensional score (summary of weights) to the range [-1, 1]. This process occurred prior training and established the injectable knowledge for the model.
Substitution analyses were performed to evaluate the change in performance metrics when the lexi- con weights decided by the model are offset by 0.1. As shown in Table (A10 decreasing the lexicon weights by 0.1 resulted in slight negative impact on performance, whereas increasing the weights by 0.1 caused a steep decline. Overall, the weights determined during the human expert-agent preparatory meeting remain reliable and efficient.
Table A10: Substitution analyses on the lexicon weights: property scores of antimicrobial peptides generated by MAC-AMP and its variants
Antibacterial Activity (1)
AMP
Structural
Model
Toxicity (L)
Likelihood (1)
Reliability (t)
MAC-AMP
0.943 0.008
0.797 + 0.012
0.154 + 0.008
0.873 + 0.009
Lexicon Weight + 0.1
0.878 = 0.023
0.794 = 0.009
0.156 = 0.012
0.836 + 0.013
Lexicon Weight 0.1
0.938 = 0.019
0.804 = 0.006
0.871 = 0.013
N CROSS-DOMAIN TRANSFERABILITY
One of MAC-AMP's biggest novelties is its setup for cross-domain transferability. To test its po- tential beyond AMP design, the MAC-AMP framework was applied for an English table-to-text generation task, where, given a table and a set of highlighted table cells, the model has to produce a one-sentence description. To test this task, subset of the ToTTo dataset was used, an open-domain English table-to-text dataset with training examples of Wikipedia tables, highlighted table cells, and one-sentence descriptions (Parikh al. 2020). For this preliminary study, a randomly sampled sub- set of 30,000 training examples (approximately 25% of the full dataset of 120,761 sequences) was used.
In transferring MAC-AMP to the ToTTo table-to-text benchmark, the multi-agent architecture, log- ging scheme, and PPO training code remain unchanged, and only minimal domain-specific substitu- tions are applied. The Property Prediction module no longer queries molecular predictors; instead, each candidate description was evaluated using the official ToTTo scripts to obtain PARENT and BLEU scores, which are linearly normalized and used as S. and S, within the RL pipeline. Ad- ditional statistics, such as table coverage, proportion of unsupported tokens, and length ratio, are computed and exposed to the Al-simulated Peer Review module V-style auxiliary evidence. The module continues to operate over the four dimensions (Eff) / Safe / DevStruct Orig), but their semantics redefined for text generation. Eff denotes task effectiveness and content adequacy (coverage of highlighted cells and correctness of expressed facts). Safe denotes factual safety (ab- sence of hallucinations contradictions relative the table). DevStruct denotes linguistic quality and structural coherence (grammaticality, fluency, and Wikipedia-lin discourse organization). Orig denotes stylistic diversity and generalization (faithful yet varied paraphrasing rather than verbatim copying). Based on these redefinitions, a new weighted lexicon is constructed for each dimension, while the original tagging mechanism and scoring procedure remain unmodified.
In the RL. Refinement and Peptide Generation modules, the peptide generator is replaced by a T5- small encoder-decoder model. The reward-design and alignment agents receive updated prompts describing PARENT/BLEU and the redefined four dimensions, but still emit TorchScript reward functions, F(S., Sb S.) E [0, 1]. which are consumed by the same PPO strategy to update the generator.
Due to time limitations, only a minimal evaluation was conducted in which both the T5-based base- line and the multi-agent agent-optimized generator were under identical default settings. This pro- vides an initial assessment of cross-domain applicability, while more extensive experiments and task-specific optimizations are left for future work.
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For this experiment, results were analyzed from three perspectives. "Overall" refers the average score over the entire test set. The "Overlap" subset consists of examples whose source tables also appear in the training set but with different subsets of highlighted cells, reflecting performance tables the model has seen before. The "Non-overlap" subset contains examples whose source tables never appear in the training data, testing generalization completely unseen tables.
Three automatic evaluation metrics were used: BLEU, PARENT, and BLEURT. BLEU is a tradi- tional n-gram overlap metric that measures similarity between the generated sentence and the refer- ence at the word level(Papineni et al., 2002). PARENT is specifically designed for table- or data- to-text generation, assessing how well the generated it hallucinates content not present in the table (Dhingra et al. 2019). BLEURT is a learned metric based on a finetuned pre-trained language model that leverages semantic representations and hu- man ratings to evaluate adequacy and fluency, and is more tolerant of paraphrases than pure n-gram overlap metrics (Sellam et al. 2020).
covers the correct table cells and whether
The results, shown in Table All demonstrate cross-domain applicability of the closed-loop multi- agent plus adaptive reward design framework while restricting modifications to lightweight tool- and lexicon-level adaptations for the ToTTo task.
Table A11: Comparison of the T5 baseline and T5-based MAC-AMP agent framework for the English table-to-text generation task
Data Subset
Model
BLEU
PARENT
BLEURT
T3 Baseline
44.6
56.0
0.179
Overall
T5-based MAC-AMP
46.2
58.0
0.208
Baseline
52.6
60.7
0.311
Overlap
T5- based MAC-AMP
54.1
62.4
0.338
T5 Baseline
36.8
51.4
0.051
Non-Overlap
T5-based MAC-AMP
38.5
53.7
0.083
LIMITATIONS AND FUTURE WORK
Our work demonstrates significant advances in AMP drug design and discovery, well as in LLM- based MAC systems. However, certain limitations remain. First, sensitivity to evaluators and out-of- distribution (OOD) drift persists evaluators and by distributional shifts. The generator can explore sequence space beyond the train- ing distribution, and multi-agent consensus can unintentionally imprint systematic biases onto the reward landscape. Future work will investigate OOD-aware reward specification and adaptively cal- ibrated lexicon weights enhance robustness. A second limitation concerns closed-loop incentives and exploration diversity. By compiling multi-agent consensus into executable rewards and updating them in a closed loop, multi-objeccive progress stabilized, but over long horizons, this approach may favour signals that are easiest to optimize and agree upon, thereby narrowing exploratory di- versity. To address this, future work will explore cross-module calibration and constraints to mitigate incentive drift, preserve exploration breadth, and improve cross-scenario con- sistency.
framework is still influenced by the robustness of upstream
P USE OF LARGE LANGUAGE MODELS
We acknowledge the use of large language models as a writing assistant in the preparation this manuscript. Specifically, it was used to help polish the language and writing for flow and clarity. All research idcation, code development and exccution, as well as initial drafting and section formatting, carried out by the authors.
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Reviews:
[{"review_id": "uUjF60hBGp", "reviewer": "Reviewer_vtM8", "summary": "The paper introduces MAC-AMP, a closed-loop multi-agent collaboration (MAC) system for multi-objective antimicrobial peptide (AMP) design. The system integrates property prediction, AI-simulated peer review, reinforcement learning (RL) refinement, and peptide generation modules in a fully autonomous loop. A key novelty is the conversion of multi-agent textual consensus into executable reward signals, allowing reinforcement-based optimization without manual prompt tuning. Experiments on five bacterial targets show that MAC-AMP outperforms existing generative models in antibacterial activity, toxicity reduction, and structural reliability. Ablation studies confirm that both the AI-simulated peer review and RL refinement modules contribute critically to balanced multi-objective optimization.", "questions": "1. How sensitive is MAC-AMP’s performance to the specific LLMs used as reviewer agents (GPT-5, Gemini 2.5, Perplexity)? Would substituting smaller open models maintain performance trends?\n\n2. The AI-simulated peer review uses a lexicon-tagging mechanism, how were the weights and tag mappings validated for interpretability or bias?\n\n3. Does the system ever experience feedback collapse or overfitting to its own reviewers’ biases after multiple closed-loop iterations?\n\n4. How is novelty quantitatively ensured beyond low Foldseek similarity, are there checks for redundancy or motif-level overlap with training data?\n\n5. Could the authors provide empirical measures of reward variance or entropy across RL stages to confirm that adaptive optimization prevents reward hacking?\n\n6. What are the computational costs (GPU hours, number of agent calls per iteration) and how do they scale with peptide length or number of objectives?", "limitations": "", "rating": 6, "confidence": 2, "soundness": 3, "presentation": 3, "contribution": 3, "strengths": "- The paper introduces a credible pipeline that operationalizes “multi-agent collaboration” beyond conversational coordination, producing quantitative, auditable training signals.\n- Transparent logging and role-based agent structure allow reproducibility and traceability uncommon in LLM-based systems.\n- The system’s iterative RL refinement aligns multi-agent consensus with executable objectives, avoiding reward hacking typical in static scoring systems.\n- Results across multiple bacterial targets and comparisons to several baselines demonstrate consistent gains in activity and safety metrics.\n- Includes ablation analyses, sequence-level property comparisons, and visualization of learned chemical space.\n- The framework is described as transferable to other molecule or material design problems with minimal adaptation.", "weaknesses": "- The quality of generated AMPs is constrained by the accuracy of property predictors (e.g., ToxinPred 3.0, OmegaFold). The paper acknowledges this but does not quantify its impact.\n- While broad-spectrum testing is reported, the reasoning behind cross-species generalization (beyond physicochemical similarity) could be more rigorously analyzed.\n- Although individual module ablations are provided, it remains unclear whether a simpler reward design or fewer agents would achieve similar performance.", "quality": null, "clarity": null, "significance": null, "originality": null, "strengths_and_weaknesses": "", "was_revised": false, "final_justification": "", "rebuttal": "", "claims_and_evidence": "", "methods_and_evaluation_criteria": "", "theoretical_claims": "", "experimental_designs_or_analyses": "", "relation_to_broader_scientific_literature": "", "essential_references_not_discussed": "", "other_strengths_and_weaknesses": "", "other_comments_or_suggestions": "", "questions_for_authors": "", "summary_of_paper": "", "summary_of_recommendation": "", "technical_quality": "", "clarity_of_presentation": "", "potential_impact": "", "robotics_focus": "", "quality_of_limitations": "", "questions_for_rebuttal": "", "reasons_to_accept": "", "reasons_to_reject": "", "questions_to_authors": "", "extra_scores": {}}, {"review_id": "lsS2kargEW", "reviewer": "Reviewer_yaxY", "summary": "The paper introduces MAC-AMP, a closed-loop multi-agent collaboration framework for multi-objective antimicrobial peptide design, tackling the complex trade-offs between activity, toxicity, and stability. The system integrates LLM-based agents (e.g., GPT-5, Gemini 2.5, Perplexity) within an AI-driven peer-review loop that evaluates candidate peptides along multiple criteria and transforms the structured consensus into executable reinforcement learning reward signals. A biomedical–computer science dual-agent team iteratively refines these rewards, guiding a PPO-based generator to optimize AMP sequences under continuous feedback—achieving interpretable and auditable improvements. Across E. coli, S. aureus, and P. aeruginosa, MAC-AMP consistently surpasses baselines such as AMP-Designer, BroadAMP-GPT, PepGAN, and Diff-AMP in antibacterial efficacy, toxicity regulation, and structural robustness, while preserving AMP-like properties and strong cross-species generalization. Collectively, MAC-AMP establishes a generalizable and interpretable paradigm for autonomous molecular design.", "questions": "See weakness.", "limitations": "", "rating": 4, "confidence": 3, "soundness": 3, "presentation": 3, "contribution": 2, "strengths": "The paper introduces a closed-loop multi-agent collaboration framework that transforms AMP design into an autonomous, explainable reinforcement learning process. Its integration of AI-simulated peer review to generate executable reward signals is both novel and technically elegant. It is clearly written, with transparent modular design and reproducible details. Overall, the paper is significant, offering a transferable paradigm for multi-objective molecular design and broader autonomous scientific discovery.", "weaknesses": "While the framework is conceptually strong, several areas could be improved. \n1. The novelty is quite limited. Using existing PPO and peptide generation model (Wang et al. 2025)\n2. Experimental validation is limited to in silico analyses—no wet-lab or biophysical confirmation is provided to verify that the generated AMPs exhibit real-world antimicrobial activity, which would strengthen the biological significance. \n3. Although the AI-simulated peer review concept is creative, the paper could include quantitative ablations demonstrating how each reviewer agent or the Area Chair aggregation contributes to performance, beyond qualitative explanation. \n4. The computational cost and scalability of running multi-agent deliberation and RL loops are not clearly analyzed, raising concerns about feasibility for large-scale or real-time applications. \n5. While the framework claims cross-domain generalizability, no transfer experiments to non-AMP domains are shown, leaving this claim largely theoretical. Addressing these limitations would substantially enhance the rigor and impact of the work.", "quality": null, "clarity": null, "significance": null, "originality": null, "strengths_and_weaknesses": "", "was_revised": false, "final_justification": "", "rebuttal": "", "claims_and_evidence": "", "methods_and_evaluation_criteria": "", "theoretical_claims": "", "experimental_designs_or_analyses": "", "relation_to_broader_scientific_literature": "", "essential_references_not_discussed": "", "other_strengths_and_weaknesses": "", "other_comments_or_suggestions": "", "questions_for_authors": "", "summary_of_paper": "", "summary_of_recommendation": "", "technical_quality": "", "clarity_of_presentation": "", "potential_impact": "", "robotics_focus": "", "quality_of_limitations": "", "questions_for_rebuttal": "", "reasons_to_accept": "", "reasons_to_reject": "", "questions_to_authors": "", "extra_scores": {}}, {"review_id": "mDmaT0p2tW", "reviewer": "Reviewer_xSXc", "summary": "The paper proposes MAC-AMP, a closed-loop multi-agent framework for multi-objective antimicrobial peptide design. It compiles structured “peer review” consensus into PPO-executable rewards to jointly optimize activity, toxicity, and structural reliability, and reports computational improvements over several baselines.\n1) The work is difficult to reproduce at review time. Code and data are not publicly available; while the authors commit to releasing them upon acceptance, independent replication and verification cannot be performed during evaluation.\n2) Core evaluation and constraints depend on external tools (e.g., ToxinPred 3.0, OmegaFold, Macrel, Foldseek). The reward and selection are tightly coupled to their outputs, yet there is no systematic robustness or bias analysis to assess how these components influence optimization outcomes.\n3) The use of OmegaFold pLDDT as a structural reliability proxy for short peptides is insufficiently validated. The paper lacks cross-checks against experimental structures or more rigorous dynamical simulations to establish suitability in the short-peptide regime.\n4)The comparison uses a uniform protocol of generating 1,000 candidates and selecting the top-30, which can amplify scorer preferences and ranking bias. The fairness and implications of this selection strategy are not discussed in depth.\n5) The paper does not provide rigorous alignment of hyperparameters, training budgets, and screening strategies across baselines, nor does it include sensitivity analyses to quantify how these choices affect reported performance.", "questions": "1)  Please specify a concrete timeline and scope for releasing code, data, and pretrained models, including licenses, repository URL, and whether an anonymized artifact will be available during rebuttal.\n2) Provide an exact, machine-readable environment (Docker/Conda) with pinned versions for all dependencies, especially third-party predictors (e.g., ToxinPred, OmegaFold, Macrel, Foldseek) and their model checkpoints.", "limitations": "", "rating": 2, "confidence": 3, "soundness": 1, "presentation": 2, "contribution": 2, "strengths": "The paper introduces a novel closed-loop multi-agent framework that compiles structured peer-review consensus into PPO-executable rewards, enabling interpretable and auditable multi-objective optimization for antimicrobial peptide design. Empirically, it reports consistent computational improvements on activity, toxicity, and structural reliability metrics over several baselines, supported by a clearly described system architecture and informative ablation studies.", "weaknesses": "1) The work is difficult to reproduce at review time. Code and data are not publicly available; while the authors commit to releasing them upon acceptance, independent replication and verification cannot be performed during evaluation.\n2) Core evaluation and constraints depend on external tools (e.g., ToxinPred 3.0, OmegaFold, Macrel, Foldseek). The reward and selection are tightly coupled to their outputs, yet there is no systematic robustness or bias analysis to assess how these components influence optimization outcomes.\n3) The use of OmegaFold pLDDT as a structural reliability proxy for short peptides is insufficiently validated. The paper lacks cross-checks against experimental structures or more rigorous dynamical simulations to establish suitability in the short-peptide regime.\n4)The comparison uses a uniform protocol of generating 1,000 candidates and selecting the top-30, which can amplify scorer preferences and ranking bias. The fairness and implications of this selection strategy are not discussed in depth.\n5) The paper does not provide rigorous alignment of hyperparameters, training budgets, and screening strategies across baselines, nor does it include sensitivity analyses to quantify how these choices affect reported performance.", "quality": null, "clarity": null, "significance": null, "originality": null, "strengths_and_weaknesses": "", "was_revised": false, "final_justification": "", "rebuttal": "", "claims_and_evidence": "", "methods_and_evaluation_criteria": "", "theoretical_claims": "", "experimental_designs_or_analyses": "", "relation_to_broader_scientific_literature": "", "essential_references_not_discussed": "", "other_strengths_and_weaknesses": "", "other_comments_or_suggestions": "", "questions_for_authors": "", "summary_of_paper": "", "summary_of_recommendation": "", "technical_quality": "", "clarity_of_presentation": "", "potential_impact": "", "robotics_focus": "", "quality_of_limitations": "", "questions_for_rebuttal": "", "reasons_to_accept": "", "reasons_to_reject": "", "questions_to_authors": "", "extra_scores": {}}, {"review_id": "PXVY2pBp4k", "reviewer": "Reviewer_yJGN", "summary": "MAC-AMP introduces a closed-loop, multi-agent LLM system for multi-objective antimicrobial peptide (AMP) design. The framework integrates three stages: a property prediction module using bioinformatics tools, an AI-simulated peer review system where multiple LLMs collaboratively evaluate peptides along activity, safety, and originality dimensions, and a reinforcement learning refinement stage that converts their structured consensus into executable reward functions for the generator.", "questions": "see weakness", "limitations": "", "rating": 6, "confidence": 4, "soundness": 3, "presentation": 3, "contribution": 3, "strengths": "1. The integration of multi-agent collaboration and reinforcement learning is innovative and potentially impactful for scientific design workflows.\n2. The structured peer-review mechanism and log-based audit trail improve transparency relative to typical black-box AMP generators.\n3. The modular design could be adapted to other molecule or protein design domains.", "weaknesses": "The framework relies on large proprietary models (GPT-5, Perplexity, and Gemini 2.5), which are not easily accessible or affordable for most users. It would be helpful if the authors could provide an estimation of the computational cost for a single complete run, or discuss how the system performs when these agents are replaced with smaller open-source models such as Qwen. Additionally, reporting the total training time compared to a standard non-agent GRPO baseline would help clarify the efficiency and scalability of the proposed approach.", "quality": null, "clarity": null, "significance": null, "originality": null, "strengths_and_weaknesses": "", "was_revised": false, "final_justification": "", "rebuttal": "", "claims_and_evidence": "", "methods_and_evaluation_criteria": "", "theoretical_claims": "", "experimental_designs_or_analyses": "", "relation_to_broader_scientific_literature": "", "essential_references_not_discussed": "", "other_strengths_and_weaknesses": "", "other_comments_or_suggestions": "", "questions_for_authors": "", "summary_of_paper": "", "summary_of_recommendation": "", "technical_quality": "", "clarity_of_presentation": "", "potential_impact": "", "robotics_focus": "", "quality_of_limitations": "", "questions_for_rebuttal": "", "reasons_to_accept": "", "reasons_to_reject": "", "questions_to_authors": "", "extra_scores": {}}]

Author rebuttal:

Decision: Accept (Poster)