Evidence map›Paper›PMID 39097862›Full record

ReviewMolecular diversity2024

Application progress of deep generative models in de novo drug design.

Yingxu Liu, Chengcheng Xu, Xinyi Yang, Yanmin Zhang, Yadong Chen, Haichun Liu

Abstract readReview
PubMed Publisher
In one paragraph

Review in Molecular diversity, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 3 papers.

0numbers the graph read from it
0cells of the map it votes in
3citing 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

3 citing papers in PubMed.

  1. Review
  2. Review
  3. Review
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

6 authors.

Yingxu LiuSchool of Science, China Pharmaceutical University, Nanjing, 210009, China.
Chengcheng XuSchool of Science, China Pharmaceutical University, Nanjing, 210009, China.
Xinyi YangSchool of Science, China Pharmaceutical University, Nanjing, 210009, China.
Yanmin ZhangSchool of Science, China Pharmaceutical University, Nanjing, 210009, China.
Yadong ChenSchool of Science, China Pharmaceutical University, Nanjing, 210009, China.
Haichun LiuSchool of Science, China Pharmaceutical University, Nanjing, 210009, China. 1020061011@cpu.edu.cn.

Funding

National Natural Science Foundation of China 81973182National Natural Science Foundation of China 82073704
6 · The paper itself

Abstract

The deep molecular generative model has recently become a research hotspot in pharmacy. This paper analyzes a large number of recent reports and reviews these models. In the central part of this paper, four compound databases and two molecular representation methods are compared. Five model architectures and applications for deep molecular generative models are emphatically introduced. Three evaluation metrics for model evaluation are listed. Finally, the limitations and challenges in this field are discussed to provide a reference and basis for developing and researching new models published in future.

Indexed as

Drug DesignDeep LearningModels, MolecularDeep learningDrug designGenerative modelPharmaceutical chemistry

Identifiers

PMID39097862

What OpenQuestion holds

Textmetadata
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 OpenQuestion graph.