Must Read

Diverse model landscape

MHub.ai: Standardizing AI for Reproducible Medical Imaging

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An inter-institutional collaboration across the US, Germany, and the Netherlands introduces MHub.ai, an accessibility and reproducibility-prioritizing, open source platform for standardized access to AI...
Joint training of ConGLUDe on structure- and ligandbased data

ConGLUDe: Toward General-Purpose Foundation Models for Drug Discovery

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Researchers from Johannes Kepler University Linz, Austria, and Merck HealthCare, Germany, developed ConGLUDe (Contrastive Geometric Learning for Unified Computational Drug Design), an AI-based model for drug...
Claude Opus 4.5

Claude Opus 4.5 and the Future of AI-Driven Research in Healthcare and Biotechnology

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The growing complexity of biomedical research and healthcare demands AI systems that can go beyond general assistance to support end-to-end scientific workflows. Building on...
DrugCLIP

DrugCLIP Enables High-Throughput Virtual Screening Across the Human Proteome

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Researchers at Tsinghua University have introduced DrugCLIP, a contrastive learning framework that enables ultrafast and accurate virtual screening at a genome-wide scale. Published in...
FoldMason

Beyond the Sequence: How FoldMason is Redefining Multiple Protein Structure Alignment at Scale

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The authors at Seoul National University developed FoldMason, a free, open-source, progressive Multiple Structural Alignment (MSTA) method published in Science that uses a structural...