General theory of area reactivity models: rate coefficients, binding probabilities and all that
Thorsten Pr\"ustel, Martin Meier-Schellersheim

TL;DR
This paper develops a comprehensive theoretical framework for the area reactivity model, analyzing reaction dynamics, rate coefficients, and binding probabilities to enhance understanding of diffusion-influenced receptor-ligand interactions.
Contribution
It extends the general theory of the area reactivity model, deriving equations for survival and separation probabilities, and relating rate coefficients to reaction dynamics.
Findings
Derived equations for survival and separation probabilities.
Calculated asymptotic expressions for rate coefficients and binding probabilities.
Discussed differences between area and contact reactivity models.
Abstract
We further develop the general theory of the area reactivity model that provides an alternative description of the diffusion-influenced reaction of an isolated receptor-ligand pair in terms of a generalized Feynman-Kac equation. We analyze both the irreversible and reversible reaction and derive the equation of motion for the survival and separation probability. Furthermore, we discuss the notion of a time-dependent rate coefficient within the alternative model and obtain a number of relations between the rate coefficient, the survival and separation probabilities and the reaction rate. Finally, we calculate asymptotic and approximate expressions for the (irreversible) rate coefficient, the binding probability, the average lifetime of the bound state and discuss on- and off-rates in this context. Throughout our treatment, we will point out similarities and differences between the area…
Peer Reviews
No public reviews on file for this paper yet. If you reviewed it on a platform where reviews are public (OpenReview, ICLR, NeurIPS, ICML), you can paste yours below so the community can read it here.
Videos
No videos yet. Explain this paper in a talk, walkthrough, or lecture? Add one.
Taxonomy
TopicsMolecular Junctions and Nanostructures · DNA and Nucleic Acid Chemistry · Computational Drug Discovery Methods
