Predicting binding free energies: Frontiers and benchmarks
By David L. Mobley, Michael K. Gilson. bioRxiv, 2016
354 citations recorded by the source corpus.
Binding free energy calculations based on molecular simulations provide predicted affinities for biomolecular complexes. These calculations begin with a detailed description of a system, including its chemical composition and the interactions between its components. Simulations of the system are then used to compute thermodynamic information, such as binding affinities. Because of their promise for guiding molecular design, these calculations have recently begun to see widespread applications in early stage drug discovery. However, many challenges remain to make them a robust and reliable tool. Here, we briefly explain how the calculations work, highlight key challenges, and argue for the development of accepted benchmark test systems that will help the research community generate and evaluate progress. Manuscript version 1.1.1 pre-release See https://github.com/mobleylab/benchmarksets for all versions.
Subfields: Atomic and Molecular Physics, and Optics, Molecular Biology
View Paper: https://www.semanticscholar.org/paper/4f7264ebb0ae6e3fcfebe3162b2cd3b22e98d85e
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In Atomic and Molecular Physics, and Optics
In Molecular Biology
- David L. Mobley
- John D. Chodera
- Frank No'e
- Anthony Nicholls
- Hao Wu
- Justin A. Lemkul
- Jason Tyler Rolfe
- Theofanis Karaletsos
Citations in the corpus
- Best practices for constructing, preparing, and evaluating protein-ligand binding affinity benchmarks [Article v0.2] (2021, background): “Mobley and Gilson discussed the need for well-chosen validation datasets and how this will have multiple benefits to understanding and expanding the domain of applicability of FE methods [1].”
- Best Practices for Constructing, Preparing, and Evaluating Protein-Ligand Binding Affinity Benchmarks [Article v1.0] (2021, background): “Mobley and Gilson discussed the need for well-chosen validation datasets and how this will have multiple benefits to understanding and expanding the domain of applicability of FE methods [1].”