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This study identified a series of novel β-glucuronidase inhibitors that may help mitigate adverse effects of first-line anti-cancer drugs by targeting gut bacteria, based on virtual high-throughput screening findings.

This study reviews the transformative role of artificial intelligence in biomaterial design, highlighting its ability to reduce costs through virtual screening, enhance material performance, and predict biological interactions to advance personalized and precision medicine.

This study used machine learning to develop classifiers for identifying effective inhibitors of 5α-reductase isozyme 2, achieving high performance in distinguishing potent from weak inhibitors.

This study identified 30 new compounds with significant androgen receptor binding affinity through a combination of virtual screening and in vitro testing.

This systematic review synthesizes the current state of drug repurposing, revealing its potential in various therapeutic areas like oncology and infectious diseases through insights from mechanistic studies and real-world evidence, and discusses emerging tools and challenges in the field.