Exploration of Drug Science

Table 6. Examples of peptide generation tools.

Predictor	Core methodology	Key features	Reference
HydrAMP	cVAE	By constructing a low-dimensional continuous latent space representation of peptide sequences, analogue generation and unconstrained generation can be achieved	[112]
deepAMP	Protein language model	Data augmentation, multi-round iterative optimization, and experimental verification are highly efficient	[109]
Multi-CGAN	CGAN	Adopt a three-stage generation strategy, and introduce the multi-label embedding mechanism and the automatic integration module	[113]
LSTM_Pep	Long short-term memory	After pre-training with large-scale general peptide data, then fine-tuning with specific active peptides	[135]
BroadAMP-GPT	CNN + LSTM	AI-driven design, multi-model fusion screening, high experimental hit rate	[110]

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AI: artificial intelligence; CNN: convolutional neural network; cVAE: conditional variational autoencoder.

Declarations

Author contributions

YZ: Conceptualization, Investigation, Writing—original draft, Writing—review & editing. ZW: Investigation, Writing—original draft, Writing—review & editing. HZ: Validation, Writing—review & editing, Supervision, Funding acquisition. All authors read and approved the submitted version.

Conflicts of interest

The authors declare that they have no conflicts of interest.

Ethical approval

Not applicable.

Consent to participate

Not applicable.

Consent to publication

Not applicable.

Availability of data and materials

Not applicable.

Funding

National Natural Science Foundation of China [82073767]; Priority Academic Program Development of Jiangsu Higher Education Institutions [PAPD 2023007]; 2026 College Students’ Innovation and Entrepreneurship Training Program [2026303]. The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript.

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