ReviewFrontiers in pharmacology2026
Artificial intelligence in drug discovery: from algorithmic foundations to clinical translation.
Review in Frontiers in pharmacology, 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.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
0 citing papers in PubMed.
No citing paper in PubMed yet.
Corrections and comments
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Authors and funding
2 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Artificial intelligence is quietly reconfiguring the landscape of drug discovery. It sharpens the search for new targets, refines lead compounds, and helps tailor clinical trial designs. Behind these advances sits a family of methods-convolutional, recurrent, and graph neural networks, generative adversarial networks, variational autoencoders, diffusion models, and Transformers-that has found its way into every stage of the pipeline. Tasks that once demanded years of trial and error, from pulling meaningful features out of molecules to predicting drug-target affinity and crystal structures with polymorph stability, now run faster and often with greater accuracy. This early insight lets researchers flag solid-form properties that influence bioavailability and manufacturability, trimming timelines and, in principle, lifting success rates. To map a field that keeps shifting shape, we searched PubMed, Web of Science, Scopus, and Google Scholar for peer-reviewed reports published between January 2007 and April 2026. The story the clinical cases tell is double-edged: one AI-discovered candidate has reached Phase IIa with encouraging efficacy, another stalled in Phase I when safety signals surfaced. AI can catch adverse effects earlier in toxicity assessments, yet nagging hurdles endure-biological complexity, patchy data, and a shortage of scientists fluent in both machine learning and pharmaceutics. Digging ourselves out will demand data standardization, nimble regulatory frameworks, and cross-disciplinary training. Weaving multimodal data together with explainable AI is becoming non-negotiable for transparency and regulatory confidence. The technology is now stretching into complex systems like Traditional Chinese Medicine and natural product screening. As AI continues to mature, it will reshape drug development in ways we are only beginning to grasp, but one truth remains stubborn: clinical translation still rests on rigorous experimental evidence.
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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.