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Patent US0210081804
Inventor

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Much More than Average Length Specification


1 Independent Claims

  • Claim 2. The method of claim 1, in which molecular orbital representations of drug-like molecules and/or parts of proteins relevant to an interaction with the molecules are represented as tensor networks.
  • Claim 3. The method of claim 1 in which a training dataset is used to train the machine learning system, and the training dataset is a molecular orbital representation of the drug-like molecules and the machine learning system uses generative models to capture statistically meaningful distributions or patterns in the training dataset sand the trained machine learning system is configured to predict novel candidate drug-like molecules.
  • Claim 4. The method of claim 1 in which the machine learning system is configured to predict molecular properties and to optimize the selection of candidates across multiple parameters, such as binding affinity and favourable toxicity.
  • Claim 5. The method of claim 1 in which the machine learning system is configured as a tensor network or includes tensor decompositions in its components and further configured to predict molecular properties and to optimize the selection of candidates across multiple parameters, such as binding affinity and favorable toxicity.
  • Claim 6. The method of claim 1 used for analyzing a chemical compound dataset so as to determine suitable chemical compounds, the method comprising the steps of: (i) determining a tensorial space for said chemical compounds dataset, or other chemical compounds(ii) correlating the tensorial space with a large search or latent space, that search or latent space itself being tensorialand (iii) outputting a further dataset of candidate chemical compounds using a generative model.
  • Claim 7. The method of claim 1 in which the molecular orbital representations of drug-like molecules and/or parts of proteins relevant to an interaction with the molecules contain direct information about quantum correlations and entanglement properties.
  • Claim 10. The method of claim 1 in which molecular orbital representations of drug-like molecules and/or parts of proteins relevant to an interaction with the molecules include representations describing states with volume law entanglement, which can, for example, provide descriptions of highly excited states present in transition states of small molecules in a reaction or a binding process.
  • Claim 12. The method of claim 1 in which the molecular orbital representations of drug-like molecules and/or parts of protein relevant to an interaction with the molecules efficiently represent a set of molecular quantum states of small drug-like molecules.
  • Claim 17. The method of claim 1 in which featurization of graph models is by introducing entanglement features or other quantum mechanically features, derived from approximate molecular wave functions.
  • Claim 18. The method of claim 1 in which parts of protein relevant to an interaction with the molecules are provided as an input to the machine learning system.
  • Claim 22. The method of claim 1 in which a training dataset is used to train a generative model.
  • Claim 23. The method of claim 1 in which a training dataset is used to train a predictive model.
  • Claim 24. The method of claim 1 in which a training dataset is used to train a generative model, which in turn feeds the predictive model.
  • Claim 25. The method of claim 1 in which a predictive model is configured to predict whether a candidate small drug-like molecule is appropriate to a specific requirement.
  • Claim 31. The method of claim 1 in which a training dataset is fed high-quality molecules by a predictive model.
  • Claim 32. (canceled)
  • Claim 33. (canceled)
  • Claim 34. The method of claim 1 in which the machine learning system is configured to handle tensor network inputs by applying tensorial layers to the input, and using a mechanism to decrease the complexity of the resulting tensor network, so that it can be feasibly run on classical computers.
  • Claim 35. The method of claim 1 in which the machine learning system has been trained on a training dataset.
  • Claim 36. The method of claim 1 in which the machine learning system is supervised, semi-supervised or unsupervised.
  • Claim 37. The method of claim 1 in which the machine learning system is a generative network, or an autoencoder, or RNN, or Monte-Carlo tree search model, or an Ising model or a restricted Boltzmann machine trained in an unsupervised manner, a graph model, a graph to graph autoencoder.
  • Claim 38. The method of claim 1 in which the machine learning system is a generative adversarial network.
  • Claim 39. (canceled)
  • Claim 40. The method of claim 1 in which the machine learning system is a neural network that comprises tensorial decompositions or tensor networks.
  • Claim 41. The method of claim 1 in which the machine learning system is a quantum computer with quantum circuits configured as tensor networks.
  • Claim 42. The method of claim 1 in which the machine learning system is a quantum machine learning circuit.
  • Claim 44. The method of claim 1 in which the machine learning system outputs molecular orbital representations of drug-like molecules to a predictive model.
  • Claim 45. The method of claim 1 in which a predictive model screens the output from the machine learning system.
  • Claim 47. (canceled)
  • Claim 52. The method of claim 1 in which the machine learning system is one of the following: generative model, generative adversarial network, autoencoder or recursive neural network.
  • Claim 78. A system configured to perform method of claim 1.
  • Claim 79. A molecule or class of molecules identified using the method of claim 1.


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