Abstract: The invention is machine learning based method of, or system configured for, identifying candidate, small, drug-like molecules, in which a tensor network representation of molecular quantum states of a dataset of small, drug-like molecules is provided as an input to a machine learning system, such as a neural network system. The machine learning method or system may is itself configured as a tensor network. A training dataset may be used to train the machine learning system, and the training dataset is a tensor network representation of the molecular quantum states of small drug-like molecules.

Abstract: A protein language natural language processing (NLP) system is trained to predict binding affinity. Amino acids of proteins are tokenized and masked. A first neural network is trained on TCR sequences and epitope sequences in an unsupervised or self-supervised manner. The information obtained from the first phase of training is applied in a subsequent training operation via transfer learning, to a second neural network. An annotated compact dataset is used to fine-tune the second neural network in a second phase of training, and in a supervised manner, to predict biophysiochemical properties of proteins, including TCR-epitope binding.

Abstract: There is provided a method for a machine learning based method of analysing drug-like molecules by representing the molecular quantum states of each drug-like molecule as a quantum graph, and then feeding that quantum graph as an input to a machine learning system.

Abstract: There is provided a quantum circuit based system configured to model infinite-size systems, in which one or more quantum circuits are configured as an infinite tensor network representation of quantum states of effectively infinite physical or chemical systems.

Abstract: The invention is machine learning based method of, or system configured for, identifying candidate, small, drug-like molecules, in which a tensor network representation of molecular quantum states of a dataset of small, drug-like molecules is provided as an input to a machine learning system, such as a neural network system. The machine learning method or system may is itself configured as a tensor network. A training dataset may be used to train the machine learning system, and the training dataset is a tensor network representation of the molecular quantum states of small drug-like molecules.