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Enabling late-stage drug diversification by high-throughput experimentation with geometric deep learning

Chemistry

Enabling late-stage drug diversification by high-throughput experimentation with geometric deep learning

D. F. Nippa, K. Atz, et al.

Discover how a groundbreaking platform that integrates geometric deep learning with high-throughput reaction screening revolutionizes late-stage functionalization in drug development. This innovative research, conducted by a team including David F. Nippa and Kenneth Atz, reveals powerful strategies for optimizing drug candidates through predictive modeling and enhanced reaction yields.

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