Evidence map›Paper›PMID 42453407›Full record

ArticleActa pharmaceutica Sinica. B2026

Unveiling the bioactive landscape of drug inactive ingredients (DIGs) using deep transfer learning.

Minjie Mou, Jinsong Zhang, Xingang Liu, Hao Yang, Tingting Fu, Hengbin Zhang, Yimiao Zhu, Tianle Niu, Xuedong Li, Yichao Ge and 7 more

Abstract read
PubMed Full text at PMC Publisher
In one paragraph

Article in Acta pharmaceutica Sinica. B, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.

0numbers the graph read from it
0cells of the map it votes in
1citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

1 citing paper in PubMed.

  1. Article
4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

17 authors.

Minjie MouCollege of Pharmaceutical Sciences, State Key Laboratory of Advanced Drug Delivery and Release Systems, Zhejiang University, Hangzhou 310058, China.
Jinsong ZhangCollege of Pharmaceutical Sciences, State Key Laboratory of Advanced Drug Delivery and Release Systems, Zhejiang University, Hangzhou 310058, China.
Xingang LiuSchool of Pharmacy, Hebei Medical University, Shijiazhuang 050017, China.
Hao YangSchool of Pharmacy, Hebei Medical University, Shijiazhuang 050017, China.
Tingting FuCollege of Pharmaceutical Sciences, State Key Laboratory of Advanced Drug Delivery and Release Systems, Zhejiang University, Hangzhou 310058, China.
Hengbin ZhangCollege of Pharmaceutical Sciences, State Key Laboratory of Advanced Drug Delivery and Release Systems, Zhejiang University, Hangzhou 310058, China.
Yimiao ZhuCollege of Pharmaceutical Sciences, State Key Laboratory of Advanced Drug Delivery and Release Systems, Zhejiang University, Hangzhou 310058, China.
Tianle NiuSchool of Pharmacy, Hebei Medical University, Shijiazhuang 050017, China.
Xuedong LiSchool of Pharmacy, Hebei Medical University, Shijiazhuang 050017, China.
Yichao GeCollege of Pharmaceutical Sciences, State Key Laboratory of Advanced Drug Delivery and Release Systems, Zhejiang University, Hangzhou 310058, China.
Ziqi PanCollege of Pharmaceutical Sciences, State Key Laboratory of Advanced Drug Delivery and Release Systems, Zhejiang University, Hangzhou 310058, China.
Xinyu LiuSchool of Pharmacy, Hebei Medical University, Shijiazhuang 050017, China.
Huaicheng SunCollege of Pharmaceutical Sciences, State Key Laboratory of Advanced Drug Delivery and Release Systems, Zhejiang University, Hangzhou 310058, China.
Tianyuan ZhangCollege of Pharmaceutical Sciences, State Key Laboratory of Advanced Drug Delivery and Release Systems, Zhejiang University, Hangzhou 310058, China.
Yang ZhangSchool of Pharmacy, Hebei Medical University, Shijiazhuang 050017, China.
Feng ZhuCollege of Pharmaceutical Sciences, State Key Laboratory of Advanced Drug Delivery and Release Systems, Zhejiang University, Hangzhou 310058, China.
Jianqing GaoCollege of Pharmaceutical Sciences, State Key Laboratory of Advanced Drug Delivery and Release Systems, Zhejiang University, Hangzhou 310058, China.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

In a drug product, the major components by mass are the drug inactive ingredients (DIGs), which raises great concerns about their unwanted effects and clinical toxicities. It is demanded to unveil their proteome-wide bioactive landscape using computational methods. However, existing methods are impeded by either incapability to scan human proteome or inaccuracy in DIGs' bioactivity prediction. Here, a cross-attention transformer model, titled

Indexed as

Bioactive landscapeDeep transfer learningDrug formulationDrug inactive ingredientsDrug safetyExcipient–protein interactionsExcipientsTransformer

Identifiers

PMID42453407
PMCPMC13366294

What Socratic holds

Textmetadata
LicenceCC BY-NC-ND
Read underepoch 390

Registered trials

None linked

Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the Socratic graph.