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Machine Learning-Assisted simulation of Metalloenzyme’s Reactivity

CORDIS · observation · Publication date unknown

Machine Learning-Assisted simulation of Metalloenzyme’s Reactivity Metalloenzymes play a crucial role in various biological functions, going from small molecule transportation to catalyzing essential metabolic ingredients. Understanding their reactivity is essential for advancing a broad range of fields and related industries, such as medicine, biotechnology, environmental science, and catalysis. The computational modelling of biomolecules is a pillar of new drug and catalyst design, but the common methods used for purely organic-based compounds, such as empirical force fields, cannot simply be applied in the presence of a metal centre due to their complex electronic structure. On the other hand, hybrid Quantum Mechanical/Molecular Mechanics (QM/MM) methods provide a way to study metalloenzymes, but their computational overheads prevent their large-scale use for molecular dynamics simula

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recordType

award

status

SIGNED

region

EU

value

199694.4

unit

EUR