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Updated: Jul 6, 2025

Author Spotlight: Streamlining Protein Target Prediction and Validation via Molecular Docking and CETSA
Published on: February 23, 2024
Harnessing deep learning for enhanced ligand docking
Xujun Zhang1, Chao Shen1, Chang-Yu Hsieh2
1College of Pharmaceutical Sciences, Zhejiang University, Hangzhou 310058, Zhejiang, China; Hangzhou Carbonsilicon AI Technology Co., Ltd, Hangzhou 310018, Zhejiang, China.
Abstract:
Ligand docking (LD), a technology for predicting protein-ligand (PL)-binding conformations and strengths, plays key roles in virtual screening (VS). However, the accuracy and speed of current LD methodologies remain suboptimal. Here, we discuss how deep learning (DL) could help to bridge this gap by examining recent advancements and projecting future trends.
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