The predictive performance of trained ML models was assessed by using three performance metrics, namely, rootmean-square error (RMSE), Pearson's R and Spearman's 𝜌 correlation coefficients. Table shows performance comparison of the four ML models for the test set consisting of 165 compounds. The baseline model LR gave the poorest performance with large RMSE (RMSE = 2.80 ± 0.32 kcal/mol) and low correlation (Pearson's R = 0.46 ± 0.07; Spearman's 𝜌 = 0.58 ± 0.05) with respect to experiment. This is not unexpected since LR is a linear model which is unable to capture nonlinear relationships between the input features and the target label of binding free energy. Using more powerful nonlinear methods such as RF, XGBoost and GraphConv significantly improves the predictive performance as shown in Table . The GraphConv model gave the best performance with the lowest test RMSE (RMSE = 1.86 ± 0.22) and highest test correlation (Pearson's R = 0.80 ± 0.04, Spearman's 𝜌 = 0.84 ± 0.03). Figure shows comparison between predicted and experimental binding free energies. However, GraphConv's performace does not differ significantly from XGBoost model which is the second best. The GraphConv model was chosen to make predictions of binding free energies for nearly 2 million compounds in the CHEMBL data set. The distribution of predicted binding free energies is shown in Fig 1 . The mean and standard deviation of the distribution are -6.84 kcal/mol and 0.97 kcal/mol, respectively. A short list of 400 compounds having strongest binding free energy to neuraminidase (ranging from -12.4 kcal/mol to -10.10 kcal/mol) was chosen for further investigations. Among these 400 compounds, 184 of them were previously tested and therefore, removed from the list. Then 11 compounds (see Table in SI for the list of compounds) were randomly selected (see table S1 in SI) for experimental investigations to assess their enzymatic inhibitory and anti-viral activities and cytotoxicity. Moreover, we also performed molecular docking and MD simulations to study their binding conformation to the enzyme.