519. DeepLPI: A Novel Drug Repurposing Model based on Ligand-Protein Interaction Using Deep Learning

BACKGROUND: Drug repurposing has gained increased attention because it proposes to find effective cures for new diseases from approved drugs to lower development costs and time, and computational prediction of protein-ligand interactions (PLI) can provide accurate and fast drug screening. The shortcomings of existing machine learning-based methods for PLI prediction include 1) using human-selected features leads to loss of information and therefore lower accuracy and 2) using limited 3D structure data for input leads to lower generalizability. METHODS: To address the shortcomings, I proposed DeepLPI, a novel deep learning-based model that takes as input the raw sequences of drug molecules and proteins. DeepLPI applied pre-trained embedding models to encode the raw sequences into dense vector representations, which were then fed into 1D-CNN and biLSTM to obtain predictions. BindingDB dataset was used for model training and performance evaluation that is compared with a start-of-the-art method DeepCDA. [Figure: see text] [Figure: see text] RESULTS: Results showed that the DeepLPI reached an overall AUROC of 0.79 based on the BindingDB internal test, 76% better than DeepCDA. DeepLPI also outperformed DeepCDA using the external Davis dataset (AUROC=0.53) and COVID-19 3CL Protease dataset (AUROC=0.61). [Figure: see text] [Figure: see text] [Figure: see text] CONCLUSION: The high performance of DeepLPI suggests that our model has the potential to identify new COVID-19 drugs when applied to approved drugs. The generalizability of the model also promises applications to diseases in a wider scope. DISCLOSURES: All Authors: No reported disclosures.