ArticleChemMedChem2026
Strategic Key Performance Indicators for AI in Lead Optimization.
Article in ChemMedChem, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.
What it found
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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.
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Who cites it
0 citing papers in PubMed.
No citing paper in PubMed yet.
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Authors and funding
8 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
With increasing cost and failure rates in the pharmaceutical R&D process not fundamentally improving over the last decade, pressure remains high to increase the probability of success to improve the effectiveness of pharmaceutical R&D. The broad introduction of AI into the R&D landscape over the last years holds the promise to lift pharmaceutical R&D out of its productivity problem, as preliminary analyses suggest that "AI-native" companies may be outpacing traditional peers. However, harnessing this potential requires moving beyond measuring technical model performance (e.g., predictive accuracy) to measuring strategic impact. In this perspective, members of the EFMC
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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.