Evidence map›Paper›PMID 40027444›Full record

ArticleResearch and practice in thrombosis and haemostasis2025

Structural bioinformatics for rational drug design.

Soroush Mozaffari, Agnethe Moen, Che Yee Ng, Gerry A F Nicolaes, Kanin Wichapong

Abstract read
In one paragraph

Article in Research and practice in thrombosis and haemostasis, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 10 papers.

0numbers the graph read from it
0cells of the map it votes in
10citing 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

10 citing papers in PubMed.

  1. Review
  2. Article
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  5. Article
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  10. Artificial Intelligence and the future of clinical trials.Contemporary clinical trials communications · 2025
    Article
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.

Soroush MozaffariDepartment of Biochemistry, Cardiovascular Research Institute Maastricht (CARIM), Maastricht University, Maastricht, the Netherlands.
Agnethe MoenDepartment of Biochemistry, Cardiovascular Research Institute Maastricht (CARIM), Maastricht University, Maastricht, the Netherlands.
Che Yee NgHillmark B.V., Maastricht, the Netherlands.
Gerry A F NicolaesDepartment of Biochemistry, Cardiovascular Research Institute Maastricht (CARIM), Maastricht University, Maastricht, the Netherlands.
Kanin WichapongDepartment of Biochemistry, Cardiovascular Research Institute Maastricht (CARIM), Maastricht University, Maastricht, the Netherlands.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

A State of the Art lecture titled "structural bioinformatics technologies for rational drug design: from in silico to in vivo" was presented at the International Society on Thrombosis and Haemostasis (ISTH) Congress in 2024. Drug discovery remains a resource-intensive and complex endeavor, which usually takes over a decade and costs billions to bring a new therapeutic agent to market. However, the landscape of drug discovery has been transformed by the recent advancements in bioinformatics and cheminformatics. Key techniques, including structure- and ligand-based virtual screening, molecular dynamics simulations, and artificial intelligence-driven models are allowing researchers to explore vast chemical spaces, investigate molecular interactions, predict binding affinity, and optimize drug candidates with unprecedented accuracy and efficiency. These computational methods complement experimental techniques by accelerating the identification of viable drug candidates and refining lead compounds. Artificial intelligence models, alongside traditional physics-based simulations, now play an important role in predicting key properties such as binding affinity and toxicity, contributing to more informed decision-making, particularly early in the drug discovery process. Despite these advancements, challenges remain in terms of accuracy, interpretability, and the needed computational power. This review explores the state of the art in computational drug discovery, examining the latest methods and technologies, their transformative impact on the drug development pipeline, and the future directions needed to overcome remaining limitations. Finally, we summarize relevant data and highlight cases where various computational approaches were successfully applied to develop novel inhibitors, as presented during the ISTH 2024 Congress.

Indexed as

artificial intelligencecomputer-aided molecular designdrug discoverymachine learningmolecular dockingmolecular dynamics simulation

Identifiers

PMID40027444
PMCPMC11869865

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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 OpenQuestion graph.