ArticleRSC medicinal chemistry2025
Accelerating compound synthesis in drug discovery: the role of digitalisation and automation.
Article in RSC medicinal chemistry, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.
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.
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.
Who cites it
1 citing paper in PubMed.
- Three privileged scaffolds, one pipeline: chalcones, pyrazolines and pyrazoles in drug discovery and fluorescent sensing.RSC medicinal chemistry · 2026Review
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
6 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
The Design-Make-Test-Analyse (DMTA) cycle relies on efficient compound synthesis, yet the synthesis ("Make") process remains a significant bottleneck, especially for complex molecules. This opinion letter explores how digitalisation and automation are accelerating the entire synthesis process. It details their current integration, from AI-powered synthesis planning and streamlined sourcing to automated reaction setup, monitoring, purification, and characterisation. FAIR data principles are emphasised as crucial for building robust predictive models and enabling interconnected workflows. Finally, the future of fully integrated, data-driven synthesis with tools like "Chemical ChatBots" and the evolving skill set required for medicinal chemists in this increasingly digital and automated landscape are discussed.
Identifiers
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Registered trials
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.