The LASSO regression (L1-regularized linear regression) is an accurate linear regression and (simultaneously) a MD selection method. When a weighted sum of absolute values of coefficients is added to the loss function, the minimization of the loss function leads to zeroing of most of coefficients. We consider a MD to be selected if its coefficient (for scaled to the [0; 1] range value of the MD) is positive and above the threshold value of 0.1. In Fig. , the dependence of the accuracy and number of selected MD on l 1 constant is shown. Smaller values of l 1 result into better accuracy and larger number of important MD. At values l 1 < 1.0, the accuracy decreases very slowly with decrease of l 1 , and l 1 = 1.0 was used for further investigation. It can be clearly seen that the following MD: LabuteASA, BCUT2D_MRHI are the most important (for Bis4MPyC6) and play the largest role. The 5 most important MD and their order can be established reliable. The average number of MD selected in both runs for all pairs of repeats is ~16.0.