Molecular Communication (MC) channels are characterized by significant memory and nonlinear dynamics arising from diffusion and receptor kinetics. While often viewed as impairments to reliable data transmission, this work introduces a paradigm shift by reconceptualizing these intrinsic physical properties as computational resources. We frame a canonical point-to-point MC channel, comprising ligand diffusion and reversible ligand-receptor binding at a spherical receiver, as a Physical Reservoir Computer (PRC). Utilizing deterministic mean-field modeling and particle-based spatial stochastic simulations, we demonstrate the MC system's inherent capability for complex temporal information processing on standard chaotic time-series benchmarks. We comprehensively evaluate performance using both task-specific Normalized Root Mean Square Error (NRMSE) and the task-independent Information Processing Capacity (IPC). Our results reveal a non-monotonic dependence of computational power on key biophysical parameters (receptor kinetic rates, diffusion coefficient, and transmitter-receiver distance), identifying optimal operational regimes where memory and nonlinearity are balanced. These findings establish the MC channel as a viable computational substrate, paving the way for novel architectures in wetware artificial intelligence.
Uzun et al. (Wed,) studied this question.