View source for 20230178186. GENERATION OF PROTEIN SEQUENCES USING MACHINE LEARNING TECHNIQUES simplified abstract (Just-Evotec Biologics, Inc.)

=GENERATION OF PROTEIN SEQUENCES USING MACHINE LEARNING TECHNIQUES=


==Organization Name==

[[:Category:Just-Evotec Biologics, Inc.|Just-Evotec Biologics, Inc.]]


[[Category:Just-Evotec Biologics, Inc.]]

==Inventor(s)==

[[:Category:Tileli Amimeur of Seattle WA (US)|Tileli Amimeur of Seattle WA (US)]][[Category:Tileli Amimeur of Seattle WA (US)]]

[[:Category:Randal Robert Ketchem of Snohomish WA (US)|Randal Robert Ketchem of Snohomish WA (US)]][[Category:Randal Robert Ketchem of Snohomish WA (US)]]

[[:Category:Jeremy Martin Shaver of Lake Forest Park WA (US)|Jeremy Martin Shaver of Lake Forest Park WA (US)]][[Category:Jeremy Martin Shaver of Lake Forest Park WA (US)]]

[[:Category:Rutilio H. Clark of Bainbridge Island WA (US)|Rutilio H. Clark of Bainbridge Island WA (US)]][[Category:Rutilio H. Clark of Bainbridge Island WA (US)]]

[[:Category:John Alex Taylor of Bellevue WA (US)|John Alex Taylor of Bellevue WA (US)]][[Category:John Alex Taylor of Bellevue WA (US)]]

==GENERATION OF PROTEIN SEQUENCES USING MACHINE LEARNING TECHNIQUES - A simplified explanation of the abstract==


This abstract first appeared for US patent application 20230178186 titled 'GENERATION OF PROTEIN SEQUENCES USING MACHINE LEARNING TECHNIQUES

==Simplified Explanation==

The patent application describes a method for generating amino acid sequences of antibodies using a generative adversarial network. This network consists of two components: one that generates amino acid sequences for antibody light chains and another that generates sequences for antibody heavy chains. These sequences can be combined to produce complete antibody amino acid sequences.

* A generative adversarial network is used to generate amino acid sequences of antibodies.
* The network includes separate components for generating light chain and heavy chain sequences.
* The training of these components can proceed at different rates.
* The generated amino acid sequences can be evaluated based on the complementarity-determining regions of the antibodies.
* Training datasets can be created using amino acid sequences of antibodies with specific binding affinities, such as binding affinity with major histocompatibility complex (MHC) molecules.

== Potential Applications ==
* Antibody engineering and drug development
* Immunotherapy and cancer treatment
* Diagnostic tools for detecting specific molecules

== Problems Solved ==
* Streamlining the process of generating amino acid sequences of antibodies
* Improving the efficiency and accuracy of antibody engineering
* Enabling the development of antibodies with specific binding affinities

== Benefits ==
* Faster and more efficient generation of antibody amino acid sequences
* Ability to create antibodies with desired binding affinities
* Potential for developing targeted therapies and diagnostic tools


==Original Abstract Submitted==

amino acid sequences of antibodies can be generated using a generative adversarial network that includes a first generating component that generates amino acid sequences of antibody light chains and a second generating component that generates amino acid sequences of antibody heavy chains. amino acid sequences of antibodies call be produced by combining the respective amino acid sequences produced by the first generating component and the second generating component. the training of the first generating component and the second generating component can proceed at different rates. additionally, the antibody amino acids produced by combining amino acid sequences front the first generating component and the second generating component may be evaluated according to complentarity-determining regions of the antibody amino acid sequences. training datasets may be produced using amino acid sequences that correspond to antibodies have particular binding affinities with respect to molecules, such as binding affinity with major histocompatibility complex (mhc) molecules.



[[Category:G16B40/20]]
[[Category:G16B20/30]]
[[Category:G06N3/123]]