publications

2024

1. EJMC

   

   PepExplainer: An Explainable Deep Learning Model for Selection-Based Macrocyclic Peptide Bioactivity Prediction and Optimization

   Silong Zhai, Yahong Tan, Cheng Zhu, and 6 more authors

   European Journal of Medicinal Chemistry. GitHub , Sep 2024

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   Macrocyclic peptides possess unique features, making them highly promising as a drug modality. However, evaluating their bioactivity through wet lab experiments is generally resource-intensive and time-consuming. Despite advancements in artificial intelligence (AI) for bioactivity prediction, challenges remain due to limited data availability and the interpretability issues in deep learning models, often leading to less-than-ideal predictions. To address these challenges, we developed PepExplainer, an explainable graph neural network based on substructure mask explanation (SME). This model excels at deciphering amino acid substructures, translating macrocyclic peptides into detailed molecular graphs at the atomic level, and efficiently handling non-canonical amino acids and complex macrocyclic peptide structures. PepExplainer’s effectiveness is enhanced by utilizing the correlation between peptide enrichment data from selection-based focused library and bioactivity data, and employing transfer learning to improve bioactivity predictions of macrocyclic peptides against IL-17C/IL-17 RE interaction. Additionally, PepExplainer underwent further validation for bioactivity prediction using an additional set of thirteen newly synthesized macrocyclic peptides. Moreover, it enabled the optimization of the IC50 of a macrocyclic peptide, reducing it from 15 nM to 5.6 nM based on the contribution score provided by PepExplainer. This achievement underscores PepExplainer’s skill in deciphering complex molecular patterns, highlighting its potential to accelerate the discovery and optimization of macrocyclic peptides.

   @article{zhaiPepExplainerExplainableDeep2024,
     title = {{{PepExplainer}}: {{An}} Explainable Deep Learning Model for Selection-Based Macrocyclic Peptide Bioactivity Prediction and Optimization},
     author = {Zhai, Silong and Tan, Yahong and Zhu, Cheng and Zhang, Chengyun and Gao, Yan and Mao, Qingyi and Zhang, Youming and Duan, Hongliang and Yin, Yizhen},
     year = {2024},
     month = sep,
     journal = {European Journal of Medicinal Chemistry},
     volume = {275},
     pages = {116628},
     issn = {0223-5234},
     doi = {10.1016/j.ejmech.2024.116628},
     keywords = {Bioactivity prediction,Graph neural network (GNN),Machine learning (ML),Macrocyclic peptide,Optimization,Structure-activity relationship (SAR)}
   }

2023

1. JMC

   

   PepScaf: Harnessing Machine Learning with In Vitro Selection toward De Novo Macrocyclic Peptides against IL-17C/IL-17RE Interaction

   Silong Zhai, Yahong Tan, Chengyun Zhang, and 6 more authors

   Journal of Medicinal Chemistry. GitHub , Aug 2023

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   The combination of library-based screening and artificial intelligence (AI) has been accelerating the discovery and optimization of hit ligands. However, the potential of AI to assist in de novo macrocyclic peptide ligand discovery has yet to be fully explored. In thisstudy, an integrated AI framework called PepScaf was developed to extract the critical scaffold relative to bioactivity based on a vast dataset from an initial invitro selection campaign against a model protein target, interleukin-17C (IL-17C). Taking the generated scaffold, a focused macrocyclic peptide library was rationally constructed totarget IL-17C, yielding over 20 potent peptides that effectively inhibited IL-17C/IL-17RE interaction. Notably, the top two peptides displayed exceptional potency with IC50 values of 1.4 nM. This approach presents aviable methodology formore efficient macrocyclic peptide discovery, offering potential time and cost savings. Additionally, this is also the first report regarding the discovery of macrocyclic peptides against IL17C/IL-17RE interaction.

   @article{zhaiPepScafHarnessingMachine2023,
     dimensions = {true},
     title = {{{PepScaf}}: {{Harnessing Machine Learning}} with {{In Vitro Selection}} toward {{De Novo Macrocyclic Peptides}} against {{IL-17C}}/{{IL-17RE Interaction}}},
     author = {Zhai, Silong and Tan, Yahong and Zhang, Chengyun and Hipolito, Christopher John and Song, Lulu and Zhu, Cheng and Zhang, Youming and Duan, Hongliang and Yin, Yizhen},
     year = {2023},
     month = aug,
     journal = {Journal of Medicinal Chemistry},
     volume = {66},
     number = {16},
     pages = {11187--11200},
     publisher = {American Chemical Society},
     issn = {0022-2623},
     doi = {https://doi.org/10.1021/acs.jmedchem.3c00627}
   }