Abstract: A system for predicting biological activities or properties of one or more chemical compounds in a new drug. The system includes a processor configured to receive a plurality of input datasets including a plurality of simplified molecular-input line-entry system (SMILES) notations and execute a pre-trained Natural Language Processing (NLP) model to transform the plurality of SMILES notations into a plurality of non-sparse matrices. The processor is configured to train a deep learning model using the plurality of non-sparse matrices to obtain a trained deep learning model, where the trained deep learning model is used to predict one or more biological activities of an untested chemical compound in the new drug when the chemical compound in the drug is subjected to at least one chemical modification to form the new drug. The system efficiently and reliably predicts the biological activities or properties of the untested chemical compound in the new drug.

Abstract: There is disclosed an Artificial Intelligence (AI) assisted drug discovery system and a method for generating active and inactive targets for a given drug. The generated targets comprise the complete target landscape for the given drug. The system comprises a processor which is configured to train a multi-label deep learning neural network and a predictive model to generate and validate a prediction score, and further execute the trained multi-label deep learning neural network and the trained predictive model to identify targets associated with the given drug, and generate the active and inactive targets for the given drug molecule in response to the generated prediction score for each target associated with the given drug.