BindingDB. The binding free energy was calculated from 𝐾 " as 𝛥𝐺 = 𝑅𝑇𝑙𝑛𝐾 " , where 𝑅 is the molar gas constant, T = 298 K is the absolute temperature. The experimental binding free energy 𝛥𝐺 was used as a label for training ML regression models. The set was randomly divided into a train set consisting of 989 compounds and a test set consisting of 165 compounds. The train set was used to train machine learning (ML) models and the test set was used for performance evaluation. The 𝛥𝐺 distribution of train and test sets is shown in Figure . The best ML model was selected to make prediction of binding free energy to Neuraminidase for the CHEMBL data set consisting of nearly 2 million compounds. Compounds which have already been in the train and test sets were excluded from the CHEMBL set. The top 100 compounds having strongest binding affinity were selected for further investigations using molecular dynamics simulations, enzymatic activity, antiviral activity and cytotoxicity assays. We trained four regression models including linear regression (LR), random forest (RF), extreme gradient boosting (XGBoost) and convolutional networks on graphs (GraphConv). LR served as a baseline model due to its simplicity and being less prone to overfitting. RF and XGBoost are both ensemble methods. They differ in that in RF, regression tree learners are fit independently based on bootstrapping and random subspace of the train sample, while in XGBoost, the learners are sequentially trained such that each learner tries to fix the mistake made by previous ones. Furthermore, in RF prediction is made by averaging over the predictions of all trees in the ensemble, while in XGBoost, weighted sum of predictions from all learners is used as a final prediction. Features for LR, RF and XGBoost are physicochemical descriptors which were calculated using the RDKitDescriptors tool kit implemented in DeepChem. RDKitDescriptors calculated 200 physicochemical descriptors which were finally reduced to 104 features after removing the ones having mostly zero value and highly correlated features. For LR and RF, missing values were imputed with the median, while for XGBoost, imputation is not required because it can automatically handle missing values. For LR, the features were standardized to have a zero mean and a standard deviation of one. The deep learning method GraphConv can learn features on the fly and therefore, does not require manual feature extraction. Input into the model is a molecular graph which is passed to convolutional layers. The convolutional layers will learn a fixed-length embedding vector called molecular fingerprint which is then input into a densely connected layer.