Patent ID: 11934929
Assignee: ARIZONA BOARD OF REGENTS ON BEHALF OF ARIZONA STATE UNIVERSITY
Field: Computer technology (Electrical engineering)
Classification: CPC G | IPC G

Claim 0:
1. A method utilizing a computer system programed with one or more algorithms for relating functional data from a library of defined molecules, whose structures consist of combinations of simpler components, to a respective structure of those defined molecules, the method comprising:
(a) obtaining a data set associated with one or more chemical structures based on a signal derived from interaction of the one or more chemical structures with a chemical or physical phenomenon of interest; and
(b) applying a model description utilizing the one or more algorithms to the data set to thereby determine a function of a defined molecule in the library according to a value representing the defined molecule's covalent structure, one or more components of that structure, and one or more properties of the components as each relates to the function in question
wherein said one or more algorithms comprises:

fn(sequence)=ΣmΣrΣkCn,m,rQk,mAk,r;

wherein fn is the function of the nth defined molecule in the library, Cn,m,r is a description of the covalent structure of the defined molecule, where n is again the defined molecule in the library, m represents chemical entities that make up the defined molecule, and r represents the positions of a set of structural elements made from those entities, Qk,m represents the assignment of properties to the chemical entities, wherein there are k properties assigned to each of the m chemical entities, and Ak,r represents a weighting coefficient assigned to the different functional components of the defined molecule in terms of each of their properties and relates these structures and properties to the measured function, and
wherein relating functional data from a library of defined molecules to a respective structure of those defined molecules comprises defining an antigen or relating functional data from a library of defined molecules to a respective structure of those defined molecules comprises peptide-protein binding prediction utilizing machine learning trained on random sequences synthesized in individual positions on a peptide array.