The final submission contained models based on molecular descriptors and molecular representations based on smiles and graphs (Figure ). The machine learning methods based on descriptors included Deep Neural Network (DNN) and CatBoost . DNN was a high-dense neural network composed of seven layers. The calculated descriptors were used as input to the network and the output of the network was the target activity class used as a regression task. CatBoost is an open-source gradient boosting algorithm on decision trees. It uses ordered boosting which allows it to avoid the problem of overfitting, in particular for small training sets. In addition, we also analyzed Random Forest (RF), Associative Neural Networks (ASNN), Support Vector Machines (SVM) , Partial Least Squares (PLS), XGBoost, as well as traditional k-Nearest Neighbors (kNN) and Multiple Linear Regression (MLR).