AVP-GPT2: A Transformer-Powered Platform for De Novo Generation, Screening, and Explanation of Antiviral Peptides
Human respiratory syncytial virus (RSV) remains a significant global health threat, particularly for vulnerable populations. Despite extensive research, effective antiviral therapies are still limited. To address this urgent need, we present AVP-GPT2, a deep-learning model that significantly outperf...
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| Format: | Article |
| Language: | English |
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MDPI AG
2024-12-01
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| Series: | Viruses |
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| Online Access: | https://www.mdpi.com/1999-4915/17/1/14 |
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| author | Huajian Zhao Gengshen Song |
| author_facet | Huajian Zhao Gengshen Song |
| author_sort | Huajian Zhao |
| collection | DOAJ |
| description | Human respiratory syncytial virus (RSV) remains a significant global health threat, particularly for vulnerable populations. Despite extensive research, effective antiviral therapies are still limited. To address this urgent need, we present AVP-GPT2, a deep-learning model that significantly outperforms its predecessor, AVP-GPT, in designing and screening antiviral peptides. Trained on a significantly expanded dataset, AVP-GPT2 employs a transformer-based architecture to generate diverse peptide sequences. A multi-modal screening approach, incorporating Star-Transformer and Vision Transformer, enables accurate prediction of antiviral activity and toxicity, leading to the identification of potent and safe candidates. SHAP analysis further enhances interpretability by explaining the underlying mechanisms of peptide activity. Our in vitro experiments confirmed the antiviral efficacy of peptides generated by AVP-GPT2, with some exhibiting EC50 values as low as 0.01 μM and CC50 values > 30 μM. This represents a substantial improvement over AVP-GPT and traditional methods. AVP-GPT2 has the potential to significantly impact antiviral drug discovery by accelerating the identification of novel therapeutic agents. Future research will explore its application to other viral targets and its integration into existing drug development pipelines. |
| format | Article |
| id | doaj-art-cc96d09535214baeb482fa7b88e748a8 |
| institution | Kabale University |
| issn | 1999-4915 |
| language | English |
| publishDate | 2024-12-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Viruses |
| spelling | doaj-art-cc96d09535214baeb482fa7b88e748a82025-01-24T13:52:15ZengMDPI AGViruses1999-49152024-12-011711410.3390/v17010014AVP-GPT2: A Transformer-Powered Platform for De Novo Generation, Screening, and Explanation of Antiviral PeptidesHuajian Zhao0Gengshen Song1Beijing Youcare Kechuang Pharmaceutical Technology Co., Ltd., Beijing 100176, ChinaBeijing Youcare Kechuang Pharmaceutical Technology Co., Ltd., Beijing 100176, ChinaHuman respiratory syncytial virus (RSV) remains a significant global health threat, particularly for vulnerable populations. Despite extensive research, effective antiviral therapies are still limited. To address this urgent need, we present AVP-GPT2, a deep-learning model that significantly outperforms its predecessor, AVP-GPT, in designing and screening antiviral peptides. Trained on a significantly expanded dataset, AVP-GPT2 employs a transformer-based architecture to generate diverse peptide sequences. A multi-modal screening approach, incorporating Star-Transformer and Vision Transformer, enables accurate prediction of antiviral activity and toxicity, leading to the identification of potent and safe candidates. SHAP analysis further enhances interpretability by explaining the underlying mechanisms of peptide activity. Our in vitro experiments confirmed the antiviral efficacy of peptides generated by AVP-GPT2, with some exhibiting EC50 values as low as 0.01 μM and CC50 values > 30 μM. This represents a substantial improvement over AVP-GPT and traditional methods. AVP-GPT2 has the potential to significantly impact antiviral drug discovery by accelerating the identification of novel therapeutic agents. Future research will explore its application to other viral targets and its integration into existing drug development pipelines.https://www.mdpi.com/1999-4915/17/1/14antiviral peptidestar-transformervision-transformergeneration modelantiviral activity screening modeltoxicity screening model |
| spellingShingle | Huajian Zhao Gengshen Song AVP-GPT2: A Transformer-Powered Platform for De Novo Generation, Screening, and Explanation of Antiviral Peptides Viruses antiviral peptide star-transformer vision-transformer generation model antiviral activity screening model toxicity screening model |
| title | AVP-GPT2: A Transformer-Powered Platform for De Novo Generation, Screening, and Explanation of Antiviral Peptides |
| title_full | AVP-GPT2: A Transformer-Powered Platform for De Novo Generation, Screening, and Explanation of Antiviral Peptides |
| title_fullStr | AVP-GPT2: A Transformer-Powered Platform for De Novo Generation, Screening, and Explanation of Antiviral Peptides |
| title_full_unstemmed | AVP-GPT2: A Transformer-Powered Platform for De Novo Generation, Screening, and Explanation of Antiviral Peptides |
| title_short | AVP-GPT2: A Transformer-Powered Platform for De Novo Generation, Screening, and Explanation of Antiviral Peptides |
| title_sort | avp gpt2 a transformer powered platform for de novo generation screening and explanation of antiviral peptides |
| topic | antiviral peptide star-transformer vision-transformer generation model antiviral activity screening model toxicity screening model |
| url | https://www.mdpi.com/1999-4915/17/1/14 |
| work_keys_str_mv | AT huajianzhao avpgpt2atransformerpoweredplatformfordenovogenerationscreeningandexplanationofantiviralpeptides AT gengshensong avpgpt2atransformerpoweredplatformfordenovogenerationscreeningandexplanationofantiviralpeptides |