druglikeFilter 1.0: An AI powered filter for collectively measuring the drug-likeness of compounds.

Minjie MOU; Yintao ZHANG; Yuntao QIAN; Zhimeng ZHOU; Yang LIAO; Tianle NIU; Wei HU; Yuanhao CHEN; Ruoyu JIANG; Hongping ZHAO; Haibin DAI; Yang ZHANG; Tingting FU

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druglikeFilter 1.0: An AI powered filter for collectively measuring the drug-likeness of compounds.

Author: Minjie MOU ¹ ; Yintao ZHANG ¹ ; Yuntao QIAN ¹ ; Zhimeng ZHOU ¹ ; Yang LIAO ¹ ; Tianle NIU ² ; Wei HU ³ ; Yuanhao CHEN ¹ ; Ruoyu JIANG ¹ ; Hongping ZHAO ⁴ ; Haibin DAI ³ ; Yang ZHANG ² ; Tingting FU ¹
Author Information

1. College of Pharmaceutical Sciences, Zhejiang University, Hangzhou, 310058, China.
2. School of Pharmacy, Hebei Medical University, Shijiazhuang, 050017, China.
3. Department of Pharmacy, Second Affiliated Hospital, Zhejiang University School of Medicine, Hangzhou, 310009, China.
4. School of Science, China Pharmaceutical University, Nanjing, 210009, China.
Publication Type:Journal Article
Keywords: Deep learning; Drug discovery; Drug-likeness; Virtual screening
From: Journal of Pharmaceutical Analysis 2025;15(6):101298-101298
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/.