ArticleFrontiers in pharmacology2026
Clinical AI-assisted FXR prioritization and WADDAICA-guided design of a berberine-inspired analogue for MASH: integrated computational and experimental validation.
Article 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
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
0 citing papers in PubMed.
No citing paper in PubMed yet.
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
6 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Background: Metabolic dysfunction-associated steatotic liver disease (MASLD) and metabolic dysfunction-associated steatohepatitis (MASH), lack effective targeted therapies despite increasing global prevalence. The farnesoid X receptor (FXR) is a key regulator of hepatic lipid metabolism, inflammation, and fibrosis, making it a promising therapeutic target. However, existing FXR modulators are associated with limited efficacy and adverse effects. Objective: This study aimed to establish a sequential clinical-to-molecular framework combining clinical AI-assisted liver-disease feature learning, FXR target prioritization, WADDAICA-guided molecular design, and experimental validation of a berberine-inspired analogue. Methods: An SE attention-based Transformer model was trained on the Indian Liver Patient Dataset only for liver-disease classification and clinical feature-prioritization analysis. The model output consisted of disease-classification probability and feature-importance information; it was not used for compound optimization. Molecular design of the berberine-inspired analogue was performed independently using the WADDAICA platform. Results: The AI model demonstrated excellent performance (AUC = 0.9998), identifying clinically relevant metabolic markers linked to FXR pathways. The WADDAICA-designed berberine-inspired analogue showed favorable predicted interactions with FXR, supported by MD and DFT analyses. Favorable pharmacokinetic properties, including high gastrointestinal absorption and low blood-brain barrier permeability, were observed. Conclusion: This study presents a multi-scale, AI-integrated pipeline linking clinical prediction with molecular design and biological validation. The WADDAICA-designed berberine-inspired analogue showed FXR-associated molecular changes and preliminary cytoprotective and anti-steatotic effects in an FFA-induced HepG2 model. These findings support further investigation of its putative FXR-mediated mechanism of action using dedicated receptor functional assays.
Indexed as
Identifiers
What OpenQuestion 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 OpenQuestion graph.