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Efficient calculation of molecular configurational entropies using an information theoretic approximation
Bracken M King1, Nathaniel W Silver, Bruce Tidor
1Computer Science and Artificial Intelligence Laboratory, Massachusetts Institute of Technology, Cambridge, Massachusetts 02139-4307, USA.
This study introduces a novel method for calculating molecular entropies using the Maximum Information Spanning Tree (MIST) approximation. This approach accurately computes configurational entropy changes, crucial for understanding molecular binding and drug design.
Area of Science:
- Computational chemistry
- Molecular modeling
- Thermodynamics
Background:
- Calculating free energy changes in molecular binding is complex.
- Configurational entropy is particularly challenging due to numerous local minima and anharmonicity.
Purpose of the Study:
- To develop a new, accurate method for computing molecular entropies.
- To address the challenges in calculating configurational entropy changes.
Main Methods:
- Utilized the Maximum Information Spanning Tree (MIST) approximation.
- Applied MIST to estimate high-order couplings using low-order terms.
- Guaranteed theoretical bounds for entropy calculations.
Main Results:
- Demonstrated excellent convergence properties for entropy estimation.
- Successfully calculated entropies for small molecules.
- Computed binding entropy changes for HIV protease inhibitors.
Conclusions:
- The MIST framework provides accurate molecular entropy calculations.
- The method shows favorable comparison with the Mutual Information Expansion (MIE) approach.
- Highlights the potential of MIST in computational drug discovery.
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