Alexander Aliper
Papers
2
Total Citations
611
H-Index
2
About
Alexander Aliper is a pioneering researcher at the intersection of artificial intelligence, drug discovery, and aging biology. His key research areas include deep learning for pharmacology, drug repurposing, and AI-driven longevity therapeutics. Aliper’s major contributions began with his highly influential 2016 paper on applying deep neural networks to transcriptomic data for predicting drug properties and repurposing existing drugs—a work that has garnered over 600 citations and helped establish computational approaches in pharmaceutical research. More recently, he led groundbreaking work published in 2024 demonstrating an AI-driven robotics laboratory that identified TNIK inhibition as a potent senomorphic agent, targeting cellular senescence—a central hallmark of aging. This achievement represents a significant advance in the emerging field of geroscience, showing how artificial intelligence can systematically identify compounds that simultaneously address aging and age-related diseases. Aliper’s work exemplifies the transformative potential of combining machine learning with automated experimentation, positioning him as a key figure in the development of computational platforms for longevity therapeutics and precision medicine.
Research Focus
Key Achievements
Top Papers
- 1
- 2