To my knowledge, no. I barely know how to play go, but I can speak to the AI side of things a bit.
Deep Blue basically uses a big ol' search tree to look many, many moves into the future, like it's testing out numerous parallel games. If a series of moves doesn't terminate in a win or loss, a unit called the "static evaluator" applies a bunch of heuristics to figure out who gains an advantage from the board state. These values then percolate up the tree and are used to decide which current move is most likely to win some advantage for the AI player.
There are two main limiting factors prohibiting a Deep-Blue-like approach to an AI for go: game complexity and hardware limitations.
The first is the inherent complexity of the game. For instance, there's "branching factor". Try to visualize a go game in progress and a chess game in progress, like you've just sat down in front of a half-played board and are trying to make your next move. Because the go board is larger you can place a stone anywhere, there are more possible moves you could make on any given turn. The set of an opponent's possible plays in response to that move is also greater, and so on and so on. This means the tree of all possible game states grows much more quickly, making it harder to look many, many moves into the future. (You can work around this somewhat with "pruning" techniques: basically write off any branches that seem like they're quickly heading off into a losing position; there's no evaluative shortcut that works as well as counting material in chess, though.)
The other is actually the rules for AI players. If I recall correctly, the major tournament organizations require AI software to run on off-the-shelf consumer hardware. Deep Blue had a supercomputer's worth of memory and processing power, including custom-built chips for performing static evaluation in hardware.
So, in essence, a go AI in the style of Deep Blue, using deep search supported by massive processing power, is infeasible. However, a go AI in the style of TD-Gammon (which essentially operates based on learned heuristics) may be. The best information I can really offer on the current state of the field is a Wikipedia link, though.