The only element of depth-first search you tweak is the order in which children are investigated. The normal version proceeds in arbitrary order, i.e. in the order the children are stored.
The only feasible alternative (towards shortest paths) I can come up with is a greedy approach, that is looking at children in order of their distance from the current node (from small to large). It is easy to construct a counterexample for this rule:
Now, that is no proof that there does not exist a strategy of choosing the next child to be investigated which will make DFS find shortest paths.
However, no matter the rule¹ you can construct graphs that have DFS commit to a long detour at the very first node, just as I did for the greedy rule. Assign edges $(s,t)$ and $(s,a)$ weights such that the rule chooses to visit $a$ first, and assign $(a,b)$ a weight greater than the one of $(s,t)$. Therefore, it is plausible that DFS can never find shortest paths (in general graphs).
Note that since you can express every (positive-integer-)weighted graph as unweighted graph -- simply replace edges with cost $c$ with a chain with $c-1$ nodes -- the same examples deal with DFS on unweighted graphs. Here, the situation is actually even more bleak: without weights, what can DFS use to determine the next child to visit?
- As long as the rule is deterministic. If it is not, it can clearly not always find shortest paths.