Myaskovsky, A. | bioRxiv Science

bioRxiv2025

AI-assisted Drug Re-purposing for Human Liver Fibrosis

Liver fibrosis is a severe disease with few treatment options due to the poor quality of the available animal and in vitro models. To address this, we investigated whether a hypothesis generating multi-agent AI system (AI co-scientist) could assist in re-purposing drugs for treatment of liver fibrosis and direct their experimental characterization. A multi-parameter image analysis workflow, which enabled anti-fibrotic efficacy and drug toxicity to be serially assessed in multi-lineage human hepatic organoids grown in microwells (i.e., microHOs), was used to assess the effects of 14 drugs. Remarkably, two of the three AI co-scientist-recommended drugs that targeted epigenomic modifiers exhibited significant anti-fibrotic activity. Analysis of the anti-fibrotic effects of five drugs indicated that two inhibited TGF{beta}-induced intracellular signaling and three drugs altered TGF{beta}-induced mesenchymal cell differentiation. Since all five of the anti-fibrotic drugs reduced TGF{beta}-induced chromatin structural changes, epigenomic changes play an important role in the pathogenesis of liver fibrosis. One AI co-scientist recommended drug is an FDA-approved anti-cancer treatment (Vorinostat) that reduced TGF{beta}-induced chromatin structural changes by 91% and promoted liver parenchymal cell regeneration in microHOs. Hence, the use of AI co-scientist and this microHO platform identified a potential new generation of liver fibrosis treatments that also promote liver regeneration.

bioinformatics↗

bioRxiv2025

AI mirrors experimental science to uncover a novel mechanism of gene transfer crucial to bacterial evolution

AI models have been proposed for hypothesis generation, but testing their ability to drive high-impact research is challenging, since an AI-generated hypothesis can take decades to validate. Here, we challenge the ability of a recently developed LLM-based platform, AI co-scientist, to generate high-level hypotheses by posing a question that took years to resolve experimentally but remained unpublished: How could capsid-forming phage-inducible chromosomal islands (cf-PICIs) spread across bacterial species? Remarkably, AI co-scientists top-ranked hypothesis matched our experimentally confirmed mechanism: cf-PICIs hijack diverse phage tails to expand their host range. We critically assess its five highest-ranked hypotheses, showing that some opened new research avenues in our laboratories. We benchmark its performance against other LLMs and outline best practices for integrating AI into scientific discovery. Our findings suggest that AI can act not just as a tool but as a creative engine, accelerating discovery and reshaping how we generate and test scientific hypotheses.

microbiology↗