Exploring Fragment Adding Strategies to Enhance Molecule Pretraining in AI-Driven Drug Discovery  

作　　者：Zhaoxu Meng Cheng Chen Xuan Zhang Wei Zhao Xuefeng Cui 

机构地区：[1]School of Life Sciences,Shandong University,Qingdao 266237,China [2]School of Computer Science and Technology,Shandong University,Qingdao 266237,China [3]State Key Laboratory of Microbiology Technology,Shandong University,Qingdao 266237,China

出　　处：《Big Data Mining and Analytics》2024年第3期565-576,共12页大数据挖掘与分析（英文）

基　　金：supported by the National Key R&D Program of China(Nos.2019YFA0905700 and 2021YFC2101500);the National Natural Science Foundation of China(No.62072283).

摘　　要：The effectiveness of Al-driven drug discovery can be enhanced by pretraining on small molecules.However,the conventional masked language model pretraining techniques are not suitable for molecule pretraining due to the limited vocabulary size and the non-sequential structure of molecules.To overcome these challenges,we propose FragAdd,a strategy that involves adding a chemically implausible molecular fragment to the input molecule.This approach allows for the incorporation of rich local information and the generation of a high-quality graph representation,which is advantageous for tasks like virtual screening.Consequently,we have developed a virtual screening protocol that focuses on identifying estrogen receptor alpha binders on a nucleus receptor.Our results demonstrate a significant improvement in the binding capacity of the retrieved molecules.Additionally,we demonstrate that the FragAdd strategy can be combined with other self-supervised methods to further expedite the drug discovery process.

关 键 词：pretraining information retrieval drug discovery virtual screening molecule property prediction 

分 类 号：TP39[自动化与计算机技术—计算机应用技术] R91[自动化与计算机技术—计算机科学与技术]