Thermodynamic cycles are conceptual tools used in computational chemistry and drug discovery to relate different states of a molecular system through a series of reversible steps. The different states are part of a closed cycle where the total free energy change around it amounts to zero. When the energy differences within a cycle is known, the unknown energy difference can be derived. This allows the calculation of free energy differences (e.g., binding, solvation, or mutation energies) using e.g. molecular dynamics or quantum mechanics. These cycles underpin rigorous approaches like free energy perturbation (FEP) and thermodynamic integration (TI).
Importance in Computational Drug Discovery:
- Enable accurate prediction of binding affinities, solvation energies, and mutation effects by leveraging well-defined reference states.
- Provide a framework for rigorous comparison of computational and experimental free energy data.
- Support lead optimization by quantifying the energetic impact of chemical modifications or mutations.
- Facilitate the design of alchemical transformations for relative binding free energy calculations.
- Integrate with enhanced sampling and molecular simulation methods to improve reliability and reproducibility of predictions.
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