Evidence map›Paper›PMID 38882215›Full record

ReviewFrontiers in chemistry2024

The recent advances in the approach of artificial intelligence (AI) towards drug discovery.

Mahroza Kanwal Khan, Mohsin Raza, Muhammad Shahbaz, Iftikhar Hussain, Muhammad Farooq Khan, Zhongjian Xie, Syed Shoaib Ahmad Shah, Ayesha Khan Tareen, Zoobia Bashir, Karim Khan

Abstract readReview
In one paragraph

Review in Frontiers in chemistry, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 12 papers.

0numbers the graph read from it
0cells of the map it votes in
12citing 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

12 citing papers in PubMed.

  1. Review
  2. Review
  3. Integrative deep learning strategies to enhance early-stage drug discovery: optimizing computational structure-activity modeling for pharmacotherapeutic innovation.Journal of pharmacy & pharmaceutical sciences : a publication of the Canadian Society for Pharmaceutical Sciences, Societe canadienne des sciences pharmaceutiques · 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

10 authors.

Mahroza Kanwal KhanCollege of Chemistry and Environmental Engineering, Shenzhen University, Shenzhen, China.
Mohsin RazaAdditive Manufacturing Institute, Shenzhen University, Shenzhen, China.
Muhammad ShahbazAdditive Manufacturing Institute, Shenzhen University, Shenzhen, China.
Iftikhar HussainDepartment of Mechanical Engineering, City University of Hong Kong, Kowloon, Hong Kong SAR, China.
Muhammad Farooq KhanDepartment of Electrical Engineering, Sejong University, Seoul, Republic of Korea.
Zhongjian XieShenzhen Children's Hospital, Clinical Medical College of Southern University of Science and Technology, Shenzhen, China.
Syed Shoaib Ahmad ShahDepartment of Chemistry, School of Natural Sciences, National University of Sciences and Technology, Islamabad, Pakistan.
Ayesha Khan TareenSchool of Mechanical Engineering, Dongguan University of Technology, Dongguan, China.
Zoobia BashirCollege of Chemistry and Environmental Engineering, Shenzhen University, Shenzhen, China.
Karim KhanAdditive Manufacturing Institute, Shenzhen University, Shenzhen, China.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Artificial intelligence (AI) has recently emerged as a unique developmental influence that is playing an important role in the development of medicine. The AI medium is showing the potential in unprecedented advancements in truth and efficiency. The intersection of AI has the potential to revolutionize drug discovery. However, AI also has limitations and experts should be aware of these data access and ethical issues. The use of AI techniques for drug discovery applications has increased considerably over the past few years, including combinatorial QSAR and QSPR, virtual screening, and

Indexed as

AIartificial intelligencedrug discoverymachine learningstructure-activity relationship

Identifiers

PMID38882215
PMCPMC11176507

What OpenQuestion holds

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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.