SOURCE-LINKED INTELLIGENCE

Bridging protein structure prediction with molecular simulations via diffusion models for missing protonation states

CORDIS · observation · Publication date unknown

rmaceutical companies use computational tools. Among the most successful are physical molecular simulations, which are limited by the availability of experimental structural data. A new generation of machine learning (ML) powered structure prediction tools, such as AlphaFold, offer the potential to supply structural data suitable for physics-based modeling without the need to experimentally solve structures. However, these tools produce 3D structures missing key physical details, which are vital for accurate molecular modeling. A critical physical detail is the assignment of relevant protein protonation states, where misprediction results in large errors in drug binding affinity predictions, slowing down drug discovery. PROTONIX will bridge this gap between physical molecular simulations and ML structure prediction tools to improve the speed and accuracy of computational drug discovery, by adding protonation details to structure predictions. I will focus on the human kinase superfamily, the main therapeutic target class for cancer and cardiovascular diseases. PROTONIX will contain

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recordType

award

status

SIGNED

region

EU

value

202125.12

unit

EUR