Abstract: A method and system for generating a plurality of new antibody sequences corresponding to a target from a single lead antibody sequence. A model is pre-trained with a training dataset of plurality of known antibody sequences to learn a structural pattern from the plurality of known antibody sequences. The method includes receiving the single lead antibody sequence. The lead antibody sequence has one or more regions. The regions have one or more lead framework regions (FR) and one or more lead complementarity determining regions (CDR). The pre-trained model is configured to process the single lead antibody sequence to identify a relationship between the one or more lead framework regions (FR) and one or more lead complementarity determining regions (CDR). A plurality of new antibody sequences are generated from the single lead antibody amino acid of the sequence by the pre-trained model based on the identified relationship and the structural pattern.

Abstract: A method and system for generating a plurality of antibody sequences of a target from one or more framework regions based on at least one model. The model is trained on a training dataset of high binding affinity to generate the complementarity determining regions (CDR) from the received one or more framework regions (FR). The generated complementarity determining regions (CDR) from the each of the one or more framework regions (FR) are combined with the associated one or more framework regions to generate one or more regions of the target. The generated one or more regions comprises each of the received one or more framework regions (FR) and corresponding each of the generated complementarity determining regions (CDR). The generated one or more regions are concatenated to generate the plurality of antibody sequences of the target. The generated plurality of antibody sequences of the target has high binding affinity.

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.