ReviewActa pharmacologica Sinica2025
Advancing active compound discovery for novel drug targets: insights from AI-driven approaches.
Review in Acta pharmacologica Sinica, 2025. 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.
- From Algorithms to Assets: A Comprehensive Review of AI's Role in Preclinical Drug Discovery and the Hurdles to Clinical Translation.Pharmaceuticals (Basel, Switzerland) · 2026Review
- Primary Metabolites in ThreeLife (Basel, Switzerland) · 2026Article
- AI-driven breakthroughs in ion channel drug discovery: the future is now.Acta pharmacologica Sinica · 2026Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
12 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
The discovery of active compounds for novel, underexplored targets is essential for advancing innovative therapeutics across a wide range of diseases. Recent advancements in artificial intelligence (AI) are revolutionizing active compound discovery by dramatically enhancing the efficiency, accuracy, and scalability previously challenged by traditional methods. This review provides a comprehensive overview of AI-driven methodologies for active compound discovery, with a particular focus on their application to novel targets. Initially, we explore how AI overcomes traditional bottlenecks in molecular design, enabling precise protein perception through high-accuracy protein structure prediction and enhanced docking precision. Building upon these target-focused capabilities, AI-driven approaches also advance ligand exploration, effectively bridging biological and chemical spaces through sophisticated data transfer techniques that maximize the utility of available activity data. By assessing overall cellular or organismal responses, AI plays a pivotal role in decoding complex biological systems, driving phenotypic drug discovery (PDD) through multi-modal data integration. Finally, we discuss how AI is addressing challenges associated with targeting previously undruggable proteins, exemplified by the development of protein degraders. By synthesizing these cutting-edge advancements, this review serves as a valuable resource for researchers seeking to leverage AI in the discovery of next-generation therapeutics.
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Identifiers
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.