Evidence map›Paper›PMID 40008135›Full record

ReviewFrontiers in pharmacology2025

New strategies to enhance the efficiency and precision of drug discovery.

Qi An, Liang Huang, Chuan Wang, Dongmei Wang, Yalan Tu

Abstract readReview
In one paragraph

Review in Frontiers in pharmacology, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 16 papers.

0numbers the graph read from it
0cells of the map it votes in
16citing 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

16 citing papers in PubMed.

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  6. Target discovery and drug design in the era of artificial intelligence.Medicinal chemistry research : an international journal for rapid communications on design and mechanisms of action of biologically active agents · 2026
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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.

Qi AnScientific Research and Teaching Department, Public Health Clinical Center of Chengdu, Chengdu, Sichuan, China.
Liang HuangScientific Research and Teaching Department, Public Health Clinical Center of Chengdu, Chengdu, Sichuan, China.
Chuan WangScientific Research and Teaching Department, Public Health Clinical Center of Chengdu, Chengdu, Sichuan, China.
Dongmei WangScientific Research and Teaching Department, Public Health Clinical Center of Chengdu, Chengdu, Sichuan, China.
Yalan TuScientific Research and Teaching Department, Public Health Clinical Center of Chengdu, Chengdu, Sichuan, China.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Drug discovery plays a crucial role in medicinal chemistry, serving as the cornerstone for developing new treatments to address a wide range of diseases. This review emphasizes the significance of advanced strategies, such as Click Chemistry, Targeted Protein Degradation (TPD), DNA-Encoded Libraries (DELs), and Computer-Aided Drug Design (CADD), in boosting the drug discovery process. Click Chemistry streamlines the synthesis of diverse compound libraries, facilitating efficient hit discovery and lead optimization. TPD harnesses natural degradation pathways to target previously undruggable proteins, while DELs enable high-throughput screening of millions of compounds. CADD employs computational methods to refine candidate selection and reduce resource expenditure. To demonstrate the utility of these methodologies, we highlight exemplary small molecules discovered in the past decade, along with a summary of marketed drugs and investigational new drugs that exemplify their clinical impact. These examples illustrate how these techniques directly contribute to advancing medicinal chemistry from the bench to bedside. Looking ahead, Artificial Intelligence (AI) technologies and interdisciplinary collaboration are poised to address the growing complexity of drug discovery. By fostering a deeper understanding of these transformative strategies, this review aims to inspire innovative research directions and further advance the field of medicinal chemistry.

Indexed as

click chemistrycomputer-aided drug design (CADD)DNA-encoded libraries (DELs)drug discoverytargeted protein degradation (TPD)

Identifiers

PMID40008135
PMCPMC11850385

What OpenQuestion holds

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LicenceCC BY
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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 OpenQuestion graph.