The transformation amplified and distorted the scatter, and thus the linear regression fit does not yield the most accurate values for Bmax and Kd. Transformations can be very useful when used appropriately. You should not transform data when the transformation makes the variability less consistent and less Gaussian. Since nonlinear regression is easy, there is no reason to force your data into a linear form. Although it is usually inappropriate to analyze transformed data, it is often helpful to display data after a linear transform.
Many people find it easier to visually interpret transformed data.
Even if you analyze your data with nonlinear regression, it may make sense to display transformed data. The goal of nonlinear regression is to fit a model to your data. Choosing a model is a scientific decision. The choice should not be based solely on the shape of the graph. Using such a program is appealing because it frees you from the need to choose an equation.