RNA is identified as a critical therapeutic target due to its involvement in various cellular functions. Accurate prediction of RNA-small molecule affinity (RSMA) is prioritized for accelerating drug discovery. However, existing deep learning models predominantly treat RNA and small molecules as static entities. The dy...
Jin-Miao Song, Qi-Lin Feng, Lei Deng et al.· IEEE transactions on computa...· 0 citations
Experimental results demonstrate that MI_NPDAE consistently outperforms baseline methods across various missing mechanisms and ratios, maintaining lower imputation errors, and the imputed data significantly improves performance in downstream predictive tasks, highlighting the practical value of the approach in clinical...
Huamei Qi, Chen Cao, Wenhui Yang et al.· IEEE Transactions on Nanobio...· 0 citations
Pocket-PROTACs is proposed, a pocket-aware attention-based framework for predicting PROTAC-induced protein degradation from a triplet of POI, E3 ligase, and PROTAC, which consistently outperforms fingerprint-based baselines and recent deep learning methods.
Kai Chen, Zhijian Huang, Yinbo Wang et al.· Bioinformatics· 0 citations
SimSiam‐MuTF is introduced, a novel fine‐tuning framework to enhance the detection of resistance variants by explicitly aligning latent embedding distances with the corresponding shifts in binding affinity between WT and MT targets, which deepen the understanding of mutation‐induced resistance.
Xiaowen Hu, Pan Zhang, Shangqian Wu et al.· Advancement of science· 0 citations
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