We have explored correlations between the measured efficiency of the RNAi process and several computed signatures that characterize equilibrium secondary structure of the participating mRNA, siRNA, and their complexes. A previously published data set of 609 experimental points was used for the analysis. While virtually no correlation with the computed structural signatures are observed for individual data points, several clear trends emerge when the data is averaged over 10 bins of N ~ 60 data points per bin. The strongest trend is a positive linear (r² = 0.87) correlation between ln(remaining mRNA) and ΔG_ms, the combined free energy cost of unraveling the siRNA and creating the break in the mRNA secondary structure at the complementary target strand region. At the same time, the free energy change ΔG_total of the entire process mRNA + siRNA → (mRNA – siRNA)_complex is not correlated with RNAi efficiency, even after averaging. These general findings appear to be robust to details of the computational protocols. The correlation between computed ΔG_ms and experimentally observed RNAi efficiency can be used to enhance the ability of a machine learning algorithm based on a support vector machine (SVM) to predict effective siRNA sequences for a given target mRNA. Specifically, we observe modest, 3 to 7%, but consistent improvement in the positive predictive value (PPV) when the SVM training set is pre- or post-filtered according to a ΔG_ms threshold. / Master of Science