Can I read CBGBench: Fill in the Blank of Protein-Molecule Complex Binding Graph on EtoBox?

CBGBench: Fill in the Blank of Protein-Molecule Complex Binding Graph by Lin, Haitao; Zhao, Guojiang; Zhang, Odin; Huang, Yufei; Wu, Lirong; Liu, Zicheng; Li, Siyuan; Tan, Cheng; Gao, Zhifeng; Li, Stan Z. is a scholarly article available to read on EtoBox.

What is CBGBench: Fill in the Blank of Protein-Molecule Complex Binding Graph about?

Structure-based drug design (SBDD) aims to generate potential drugs that can bind to a target protein and is greatly expedited by the aid of AI techniques in generative models. However, a lack of systematic understanding persists due to the diverse settings, complex implementation, difficult reproducibility, and task singularity. Firstly, the absence of standardization can lead to unfair comparisons and inconclusive insights. To address this dilemma, we propose CBGBench, a comprehensive benchmark for SBDD, that unifies the task as a generative heterogeneous graph completion, analogous to fill-in-the-blank of the 3D complex binding graph. By categorizing existing methods based on their attributes, CBGBench facilitates a modular and extensible framework that implements various cutting-edge methods. Secondly, a single task on \textit{de novo} molecule generation can hardly reflect their capabilities. To broaden the scope, we have adapted these models to a range of tasks essential in drug design, which are considered sub-tasks within the graph fill-in-the-blank tasks. These tasks include the generative designation of \textit{de novo} molecules, linkers, fragments, scaffolds, and sidech

Author

Lin, Haitao; Zhao, Guojiang; Zhang, Odin; Huang, Yufei; Wu, Lirong; Liu, Zicheng; Li, Siyuan; Tan, Cheng; Gao, Zhifeng; Li, Stan Z.

Published

2024

Language

EN