You do not need to know the distribution of the dependent variable to design a useful regression model. One introduction to the assumptions of linear regression is available here. You need to determine whether there is a linear relation between the dependent variable and the predictor variables, or transform the variables in some way that the relation is linear. If you want further to do inference, this is simplest if the error terms are independent of the predicted values and have normal distributions in their (potentially transformed) scales.
A logarithmic transformation of the dependent (ratio) variable in your case would make sense if, as is often the case, the errors are proportional to the values. For example, if a variable used for calculating that ratio has an error of $\pm$ 3%, then the log transformation would provide errors in the log-transformed scale (measurement errors, at least) independent of the values. That would also make the distribution of (measurement) errors in the log of the ratio of the two variables independent of the value of the ratio, absent some interaction between them. Whether that will also provide normally distributed residual errors in your particular model cannot be predicted, as the residual distribution will also depend on how well the linear model captures the underlying phenomena. This page is an entry into the issues involved in choosing and using log transformations.
Note that normality of errors is perhaps the least important of all the assumptions for a linear model, unless you need to calculate p-values and the like directly from the regression and for some reason you can't use techniques like bootstrapping to estimate confidence intervals. Some type of generalized linear model, which allows for variance to depend on values, might be appropriate for your problem. The choice of generalized model would then be based on the particular way that you think that error terms will depend on the values and thus is specific to the problem at hand.