If you are doing regular old linear regression, there is no need to transform the x variables on the predictor side of the equation because the assumption of normality is for the residual error term so the y variable is usually the one that is transformed. That said, I don't think there is any harm in transforming the x variables. It just changes the interpretation of the regression coefficients.
As far as the exponentiating, it is not correct because the exponential of a sum is not the sum of the exponentials. Check the laws of exponents.
I think your idea is to do the regression on the log-transformed variables, and then exponentiate the resulting regression equation to interpret the coefficients on the original scale. You can't do that because when you exponentiate, the coefficients are no longer add, they are multiplied. When you log-transform the outcome, you are pretty much stuck interpreting the coefficients on the log scale of the outcome. At least I think . . .