University of Helsinki

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.

This review systematically surveys the current landscape of artificial intelligence and machine learning in lead optimization, covering advances in graph neural networks, transformer architectures, diffusion models, and chemical foundation models for molecular design and property prediction, and discusses emerging concepts including data-centric AI, uncertainty quantification, trustworthy AI, the AI optimization paradox, and the shift from molecular prediction toward scientific decision-making, concluding that despite increasing industrial adoption, AI remains dependent on high-quality experimental data, model generalizability, and rigorous experimental validation, and that future progress will depend less on increasingly sophisticated algorithms than on trustworthy AI systems that improve

This review systematically surveys the current landscape of artificial intelligence and machine learning in lead optimization, covering advances in graph neural networks, transformer architectures, diffusion models, and chemical foundation models for molecular design and property prediction, and discusses emerging concepts including data-centric AI, uncertainty quantification, trustworthy AI, the AI optimization paradox, and the shift from molecular prediction toward scientific decision-making, concluding that despite increasing industrial adoption, AI remains dependent on high-quality experimental data, model generalizability, and rigorous experimental validation, and that future progress will depend less on increasingly sophisticated algorithms than on trustworthy AI systems that improve