druglikeFilter 1.0:An AI powered filter for collectively measuring the drug-likeness of compounds
10.1016/j.jpha.2025.101298
- Author:
Minjie MOU
1
;
Yintao ZHANG
;
Yuntao QIAN
;
Zhimeng ZHOU
;
Yang LIAO
;
Tianle NIU
;
Wei HU
;
Yuanhao CHEN
;
Ruoyu JIANG
;
Hongping ZHAO
;
Haibin DAI
;
Yang ZHANG
;
Tingting FU
Author Information
1. College of Pharmaceutical Sciences,Zhejiang University,Hangzhou,310058,China
- Publication Type:Journal Article
- Keywords:
Drug-likeness;
Virtual screening;
Deep learning;
Drug discovery
- From:
Journal of Pharmaceutical Analysis
2025;15(6):1370-1377
- CountryChina
- Language:English
-
Abstract:
Advancements in artificial intelligence(AI)and emerging technologies are rapidly expanding the exploration of chemical space,facilitating innovative drug discovery.However,the transformation of novel compounds into safe and effective drugs remains a lengthy,high-risk,and costly process.Comprehensive early-stage evaluation is essential for reducing costs and improving the success rate of drug development.Despite this need,no comprehensive tool currently supports systematic evaluation and efficient screening.Here,we present druglikeFilter,a deep learning-based framework designed to assess drug-likeness across four critical dimensions:1)physicochemical rule evaluated by systematic determination,2)toxicity alert investigated from multiple perspectives,3)binding affinity measured by dual-path analysis,and 4)compound synthesizability assessed by retro-route prediction.By enabling automated,multidimensional filtering of compound libraries,druglikeFilter not only streamlines the drug development process but also plays a crucial role in advancing research efforts towards viable drug candidates,which can be freely accessed at https://idrblab.org/drugfilter/.
