ArticleExploration (Beijing, China)2026
Transforming the Landscape of Drug Innovation in AI Era: AlphaFold 3 and Beyond.
Article in Exploration (Beijing, China), 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.
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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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0 citing papers in PubMed.
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Authors and funding
7 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
The traditional drug discovery and development (DDD) pipeline has long been characterized by inefficiency and high costs. The rapid evolution of artificial intelligence (AI), especially AI-based toolkits for protein structure and interaction prediction, exemplified by AlphaFold 3 (AF3), promises to reshape the landscape of drug innovation. AF3 advances structural biology by predicting protein structures and their interactions with a range of biomolecules, including nucleic acids, small-molecule ligands, and ions, potentially changing the conventional DDD paradigm, although prediction accuracy varies substantially across these categories. This perspective examines the impact of AF3 on drug target identification and validation, hit identification, lead optimization, and personalized clinical decision-making, while addressing the technical challenges, ethical considerations, and governance implications. Solutions to overcome current limitations are proposed to advance the development of novel therapeutics.
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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.