  We devise a method for predicting certain receptor-ligand binding behaviors,
based on stochastic dynamical modelling. We consider the dynamics of a receptor
binding to a ligand on the cell membrane, where the receptor and ligand perform
different motions and are thus modeled by stochastic differential equations
with Gaussian noise or non-Gaussian noise. We use neural networks based on
Onsager-Machlup function to compute the probability $P_1$ of the unbounded
receptor diffusing to the cell membrane. Meanwhile, we compute the probability
$P_2$ of extracellular ligand arriving at the cell membrane by solving the
associated Fokker-Planck equation. Then, we could predict the most probable
binding probability by combining $P_1$ and $P_2$. In this way, we conclude with
some indication about where the ligand will most probably encounter the
receptor, contributing to better understanding of cell's response to external
stimuli and communication with other cells.
