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The vehicle routing problem is one of the most challenging optimization tasks involved in searching optimal route sets in operational research. The objective of solving capacitated vehicle routing problem (CVRP) is to identify a set of shortest paths for a fleet of individual vehicles with fixed capacity from a central depot that serve all customer demand. This study describes a deoxyribonucleic acid (DNA) computing model, a modified Adleman-Lipton model, for calculating path distance with respect to vertex set of given paths with embedded capacity constraint, exponentially accelerating the search for the solution of CVRP with large nodes by executing molecular operation in parallel processing. This work provided molecular solutions via four steps: (1) generating the solution space of all partial paths, (2) collecting feasible paths, (3) calculating vertices capacity, and (4) gathering strands with the minimum distance.