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Workshop: Machine Learning in Structural Biology Workshop

Predicting Ligand – RNA Binding Using E3-Equivariant Network and Pretraining

Zhenfeng Deng · Ruichu Gu · Hangrui Bi · Xinyan Wang · Zhaolei Zhang · Han Wen


It is becoming increasingly appreciated that small molecules hold great promise in targeting therapeutically relevant RNAs, such as viral RNAs or splicing junctions. Yet predicting ligand targeting RNA is particularly difficult since limited data are available. To overcome this, we fine-tuned a pretrained small molecule representation model, Uni-Mol, to predict the RNA-binding propensity of ligands and the RNA binding QSAR model. In addition, we develop an E3-equivariant model to predict possible ligands given the RNA pocket geometry. To the best of our knowledge, this is the first E3-equivariant model for predicting RNA-ligand binding. We demonstrated the great potential of Uni-Mol pretraining in the RNA-ligand tasks towards efficient and rational RNA drug discovery.

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