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Calculating Protein-Ligand Residence Times Through State Predictive Information Bottleneck based Enhanced Sampling
Suemin Lee1, Dedi Wang1, Markus A Seeliger2
1Biophysics Program and Institute for Physical Science and Technology, University of Maryland, College Park 20742, USA.
Predicting drug residence times is crucial for drug development. This study introduces a novel computational protocol using deep learning and enhanced sampling to accurately calculate these times, aiding in drug discovery and understanding molecular interactions.
Area of Science:
- Biochemistry and computational drug discovery.
- Molecular dynamics simulations and machine learning applications.
Background:
- Drug residence time is critical for efficacy, yet challenging to compute at the atomic level due to long simulation timescales.
- Accurate prediction of protein-ligand residence times is essential for rational drug design and understanding molecular recognition.
Conclusions:
- The developed semi-automated protocol enables accurate calculation of drug residence times.
- This approach offers a powerful tool for advancing drug development and elucidating ligand recognition mechanisms.
- Facilitates deeper insights into drug efficacy and the design of novel therapeutics.
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