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Patent US0210081804
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US0210081804

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. (9) (0)

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. (0)

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. (2) (0)

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. (0)

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 (18) (2)

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. (0)

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. (0)

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. (1) (0)

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. (0)

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. (0)

22. The method of claim 1 in which a training dataset is used to train a generative model. (0)

23. The method of claim 1 in which a training dataset is used to train a predictive model. (0)

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. (0)

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. (5) (0)

31. The method of claim 1 in which a training dataset is fed high-quality molecules by a predictive model. (0)

32. (canceled) (0)

33. (canceled) (0)

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. (0)

35. The method of claim 1 in which the machine learning system has been trained on a training dataset. (0)

36. The method of claim 1 in which the machine learning system is supervised, semi-supervised or unsupervised. (0)

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. (0)

38. The method of claim 1 in which the machine learning system is a generative adversarial network. (0)

39. (canceled) (0)

40. The method of claim 1 in which the machine learning system is a neural network that comprises tensorial decompositions or tensor networks. (0)

41. The method of claim 1 in which the machine learning system is a quantum computer with quantum circuits configured as tensor networks. (0)

42. The method of claim 1 in which the machine learning system is a quantum machine learning circuit. (1) (0)

44. The method of claim 1 in which the machine learning system outputs molecular orbital representations of drug-like molecules to a predictive model. (3) (0)

45. The method of claim 1 in which a predictive model screens the output from the machine learning system. (0)

47. (canceled) (0)

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. (0)

78. A system configured to perform method of claim 1. (0)
 

79. A molecule or class of molecules identified using the method of claim 1. (0)



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