ReviewActa pharmaceutica Sinica. B2026
Artificial intelligence in biologic drug discovery: A review of methodological evolution and therapeutic applications.
Review in Acta pharmaceutica Sinica. B, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 3 papers.
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
3 citing papers in PubMed.
- Engineering the Future of Precision Medicine: A Comprehensive Guide to RNA Therapeutics.Current issues in molecular biology · 2026Review
- Editorial of special column on machine learning in drug discovery.Acta pharmaceutica Sinica. B · 2026Article
- Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
4 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Biologic drugs, primarily comprising proteins and nucleic acids, have emerged as powerful therapeutic modalities; however, their discovery and optimization are often hindered by their inherent complexity. The advent of artificial intelligence (AI), particularly deep learning, is catalyzing a paradigm shift in this field, transitioning it from a process reliant on serendipity and laborious experimentation to a data-driven engineering discipline. This review systematically charts the co-evolution of AI methodologies and their transformative applications across the modern biologic drug development pipeline. We first outline AI's methodological progression, from language models deciphering biological sequence grammar to structure prediction models like AlphaFold making macromolecular folds computationally accessible, and finally to generative models enabling
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What Socratic holds
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 Socratic graph.