Evidence mapPaperPMID 41584335Full record

ReviewActa pharmaceutica Sinica. B2026

Neg-entropy is the true drug target for chronic diseases.

Rui Li, Tian-Le Gao, Gang Ren, Lu-Lu Wang, Jian-Dong Jiang

Abstract readReview
In one paragraph

Review in Acta pharmaceutica Sinica. B, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

0numbers the graph read from it
0cells of the map it votes in
0citing 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

0 citing papers in PubMed.

No citing paper in PubMed yet.

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

5 authors.

Rui LiInstitute of Medicinal Biotechnology, Chinese Academy of Medical Sciences, Peking Union Medical College, Beijing 100050, China.
Tian-Le GaoInstitute of Materia Medica, Chinese Academy of Medical Sciences, Peking Union Medical College, Beijing 100050, China.
Gang RenInstitute of Medicinal Biotechnology, Chinese Academy of Medical Sciences, Peking Union Medical College, Beijing 100050, China.
Lu-Lu WangInstitute of Medicinal Biotechnology, Chinese Academy of Medical Sciences, Peking Union Medical College, Beijing 100050, China.
Jian-Dong JiangInstitute of Medicinal Biotechnology, Chinese Academy of Medical Sciences, Peking Union Medical College, Beijing 100050, China.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Molecular mechanisms of chronic diseases are complicated, and it impedes drug target identification and subsequent drug discovery. We consider entropy increase in human body the root causes of chronic diseases. Accordingly, the inherent neg-entropic mechanisms, for instance the homeostatic mechanisms for metabolism, immunity, self-healing, etc., are true drug targets. Only very few molecules (such as proteins) are decisive for neg-entropy related functions, thus they are termed "head goose molecules" (HGMs) here. Identification of HGMs is key to activating neg-entropic mechanism(s), and drug intervention of the HGMs' functions might reprogram the disease process through a neg-entropy mediated drug cloud (dCloud) effect, resulting in a treatment of both symptoms and root causes of the diseases. Thus, we recommend, for the first time, the "HGMs-neg-entropy-dCloud" axis as an important strategy for discovering new drugs. Clinically proven effective drugs that target HGMs are given as examples to illustrate the concept. Different from most of the single-target drugs that interrupt disease signal pathway(s), neg-entropy drugs treat chronic diseases through converting disorderliness to orderliness in the body of patients. We hope it to be helpful in future drug discovery for chronic diseases.

Indexed as

Drug cloudEntropyHead goose moleculesNeg-entropy

Identifiers

PMID41584335
PMCPMC12828147

What Socratic holds

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Registered trials

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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.