Evidence map›Paper›PMID 34216835›Full record

ReviewDrug discovery today2021

AI-based language models powering drug discovery and development.

Zhichao Liu, Ruth A Roberts, Madhu Lal-Nag, Xi Chen, Ruili Huang, Weida Tong

Abstract readReview
PubMed Full text at PMC Publisher
In one paragraph

Review in Drug discovery today, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 52 papers, 1 of them a synthesis that pooled it.

0numbers the graph read from it
0cells of the map it votes in
52citing papers in PubMed, 1 pooled it
–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

52 citing papers in PubMed, 1 synthesis or guideline pooled it.

  1. Pooled it
  2. Review
  3. Transforming Molecular Synthesis With Large Language Models.Chemistry (Weinheim an der Bergstrasse, Germany) · 2026
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  11. MolOrgGPT:Journal of chemical information and modeling · 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

6 authors.

Zhichao LiuNational Center for Toxicological Research, US Food and Drug Administration, Jefferson, AR 72079, USA. Electronic address: zhichao.liu@fda.hhs.gov.
Ruth A RobertsNational Center for Toxicological Research, US Food and Drug Administration, Jefferson, AR 72079, USA; ApconiX, BioHub at Alderley Park, Alderley Edge SK10 4TG, UK; University of Birmingham, Edgbaston, Birmingham B15 2TT, UK.
Madhu Lal-NagOffice of Translational Sciences, Center for Drug Evaluation and Research, US FDA, Silver Spring, MD 20993, USA.
Xi ChenNational Center for Toxicological Research, US Food and Drug Administration, Jefferson, AR 72079, USA.
Ruili HuangNational Center for Advancing Translational Sciences, National Institutes of Health, Rockville, MD 20850, USA.
Weida TongNational Center for Toxicological Research, US Food and Drug Administration, Jefferson, AR 72079, USA. Electronic address: weida.tong@fda.hhs.gov.

Funding

Toxicology in the 21st Century Program (Tox21) - Computational ToxicologyZIATR000040 · NCATS · NATIONAL CENTER FOR ADVANCING TRANSLATIONAL SCIENCES · PI HUANG, RUILI · 2016 to 2025
$3.5M
6 · The paper itself

Abstract

The discovery and development of new medicines is expensive, time-consuming, and often inefficient, with many failures along the way. Powered by artificial intelligence (AI), language models (LMs) have changed the landscape of natural language processing (NLP), offering possibilities to transform treatment development more effectively. Here, we summarize advances in AI-powered LMs and their potential to aid drug discovery and development. We highlight opportunities for AI-powered LMs in target identification, clinical design, regulatory decision-making, and pharmacovigilance. We specifically emphasize the potential role of AI-powered LMs for developing new treatments for Coronavirus 2019 (COVID-19) strategies, including drug repurposing, which can be extrapolated to other infectious diseases that have the potential to cause pandemics. Finally, we set out the remaining challenges and propose possible solutions for improvement.

Indexed as

COVID-19 Drug TreatmentDrug DevelopmentDrug DiscoveryNatural Language ProcessingPharmacovigilanceArtificial IntelligenceHumansSARS-CoV-2Artificial intelligenceCOVID-19Drug developmentDrug discoveryLanguage modelsNatural language processing

Identifiers

PMID34216835
PMCPMC8604259

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.