A conditional denoising VAE-based framework for antimicrobial peptides generation with preserving desirable properties.

{"sci_version": "1.0", "schema": "https://corpus.bntrg.co.uk/sci/1.0/sci.schema.json", "id": "pmid:39932977", "kind": "science-record", "revision": 2, "content_hash": "sha256:3604dcaab2f055e900934880e1bbe5e3d1c0f5e404558e294bb085178a5dde7c", "created_at": "2026-11-01T20:30:12Z", "updated_at": "2026-11-01T21:34:48Z", "generator": {"name": "corpus-assay-engine", "run_id": "reading-sci-2026-11-01"}, "paper": {"state": "complete", "title": "A conditional denoising VAE-based framework for antimicrobial peptides generation with preserving desirable properties.", "doi": "10.1093/bioinformatics/btaf069", "pmid": "39932977", "authors": ["Weizhong Zhao", "Kaijieyi Hou", "Yiting Shen", "Xiaohua Hu"], "journal": "Bioinformatics (Oxford, England)", "year": 2025, "abstract": "MOTIVATION: The widespread use of antibiotics has led to the emergence of resistant pathogens. Antimicrobial peptides (AMPs) combat bacterial infections by disrupting the integrity of cell membranes, making it challenging for bacteria to develop resistance. Consequently, AMPs offer a promising solution to addressing antibiotic resistance. However, the limited availability of natural AMPs cannot meet the growing demand. While deep learning technologies have advanced AMP generation, conventional models often lack stability and may introduce unforeseen side effects.\nRESULTS: This study presents a novel denoising VAE-based model guided by desirable physicochemical properties for AMP generation. The model integrates key features (e.g. molecular weight, isoelectric point, hydrophobicity, etc.), and employs position encoding along with a Transformer architecture to enhance generation accuracy. A customized loss function, combining reconstruction loss, KL divergence, and property preserving loss ensure effective model training. Additionally, the model incorporates a denoising mechanism, enabling it to learn from perturbed inputs, thus maintaining performance under limited training data. Experimental results demonstrate that the proposed model can generate AMPs with desirable functional properties, offering a viable approach for AMP design and analysis, which ultimately contributes to the fight against antibiotic resistance.\nAVAILABILITY AND IMPLEMENTATION: The data and source codes are available both in GitHub (https://github.com/David-WZhao/PPGC-DVAE) and Zenodo (DOI 10.5281/zenodo.14730711).", "claims": [{"id": "claim-method", "statement": "To better demonstrate the difference between generated samples with different input properties, we employ t-SNE (Van der Maaten and Hinton 2008) to reduce the 10 properties into two dimensions, and plot the generated samples with different input properties by dots in different colors.", "kind": "methodological", "target": null, "evidence": [{"asset": "source:pmid:39932977", "locator": "sections[20] \"3.5 Conditional generation results of different conditions\" paragraph 2", "quote": "To better demonstrate the difference between generated samples with different input properties, we employ t-SNE (Van der Maaten and Hinton 2008) to reduce the 10 properties into two dimensions, and plot the generated samples with different input properties by dots in different colors."}], "state": "complete"}, {"id": "claim-data-access", "statement": "The data and source codes are available both in GitHub (https://github.com/David-WZhao/PPGC-DVAE) and Zenodo (DOI 10.5281/zenodo.14730711).", "kind": "methodological", "target": null, "evidence": [{"asset": "source:pmid:39932977", "locator": "sections[0] \"Abstract\" paragraph 3", "quote": "The data and source codes are available both in GitHub (https://github.com/David-WZhao/PPGC-DVAE) and Zenodo (DOI 10.5281/zenodo.14730711)."}], "state": "complete"}], "gaps": []}, "experiment": {"state": "partial", "objective": "", "steps": [{"id": "read_published_estimate", "order": 1, "name": "Read the published estimate from the source", "kind": "other", "state": "complete", "blocked_on": null, "description": "The claim, method and evidence span published with the source article.", "duration_hours": null, "parameters": {}, "inputs": ["source:pmid:39932977"], "outputs": ["the published estimate and its evidence"], "instruments": [], "reagents": []}, {"id": "obtain_source_data", "order": 2, "name": "Obtain the source data from the named repository", "kind": "data-preparation", "state": "blocked", "blocked_on": "data:accession_absent", "description": "The GEO series accession resolves and its sample accessions are listed. 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