Polina Mamoshina
Papers
1
Total Citations
600
H-Index
1
About
Polina Mamoshina is a pioneering computational biologist whose work sits at the intersection of artificial intelligence, drug discovery, and aging research. She is best known for her groundbreaking contributions to applying deep learning to pharmacology, most notably in her highly cited 2016 paper (600 citations) that demonstrated how deep neural networks trained on large transcriptional response datasets can predict drug properties and enable drug repurposing. This work established a powerful new paradigm for using transcriptomic data to accelerate pharmaceutical development. Mamoshina has also made significant contributions to the emerging field of deep aging clocks, using AI to analyze blood biochemistry and transcriptomic data to predict biological age and assess the effects of various interventions on aging. Her research has been instrumental in showing how machine learning can identify biomarkers of aging and evaluate potential geroprotective drugs. With her work spanning both computational methods and practical biomedical applications, Mamoshina continues to be a leading voice at the forefront of AI-driven longevity research and precision medicine.
Research Focus
Key Achievements
Top Papers
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