Science for Life Laboratory

The authors present TRIDENT-2, a multimodal artificial intelligence model trained on 560,780 toxicity assays spanning 82,775 chemicals, 6,793 species, and multiple exposure scenarios to predict chemical toxicity across evolutionarily diverse eukaryotic species, reporting an average median absolute error of 1.76 to 3.80 and remaining accurate across broad chemical and taxonomic distances, which allows toxicity assessment for species and chemicals beyond current experimental evidence.

The authors present TRIDENT-2, a multimodal artificial intelligence model trained on 560,780 toxicity assays spanning 82,775 chemicals, 6,793 species, and multiple exposure scenarios to predict chemical toxicity across evolutionarily diverse eukaryotic species, reporting an average median absolute error of 1.76 to 3.80 and remaining accurate across broad chemical and taxonomic distances, which allows toxicity assessment for species and chemicals beyond current experimental evidence.

The authors developed multiplexed Deep Visual Proteomics (mxDVP), an end-to-end workflow combining high-plex imaging, automated computational analysis, and spatially guided ultra-sensitive mass spectrometry, powered by the open-source image analysis framework PIPΣX for whole-slide membrane-aware segmentation, annotation, and laser microdissection export, achieving more than 6,000 protein identifications from as few as 100 small islet cells; applied to human pancreatic islets it segmented over 860,000 cells and resolved twelve endocrine subtypes, including rare polyhormonal and intermediate-state populations showing spatial organization patterns, co-expression of INSM1 and SCG3, and hybrid α/β/δ signatures.

The authors developed multiplexed Deep Visual Proteomics (mxDVP), an end-to-end workflow combining high-plex imaging, automated computational analysis, and spatially guided ultra-sensitive mass spectrometry, powered by the open-source image analysis framework PIPΣX for whole-slide membrane-aware segmentation, annotation, and laser microdissection export, achieving more than 6,000 protein identifications from as few as 100 small islet cells; applied to human pancreatic islets it segmented over 860,000 cells and resolved twelve endocrine subtypes, including rare polyhormonal and intermediate-state populations showing spatial organization patterns, co-expression of INSM1 and SCG3, and hybrid α/β/δ signatures.

The study developed RepurAgent, a hierarchical multi-agent AI system in which a supervisor agent and a planning agent coordinate four specialized sub-agents (research, prediction, data, and report) through a human-in-the-loop design with episodic memory and retrieval-augmented generation, and validated it across three scenarios spanning the drug repurposing lifecycle: in Acute Myeloid Leukemia a blinded expert evaluation indicated substantially more novel and mechanistically credible candidates than a vanilla LLM baseline; in a retrospective COVID-19 antiviral screen it prioritized compounds with AUC-ROC up to 0.

The study developed RepurAgent, a hierarchical multi-agent AI system in which a supervisor agent and a planning agent coordinate four specialized sub-agents (research, prediction, data, and report) through a human-in-the-loop design with episodic memory and retrieval-augmented generation, and validated it across three scenarios spanning the drug repurposing lifecycle: in Acute Myeloid Leukemia a blinded expert evaluation indicated substantially more novel and mechanistically credible candidates than a vanilla LLM baseline; in a retrospective COVID-19 antiviral screen it prioritized compounds with AUC-ROC up to 0.