Quantum Generators: Navigating Protein Sequences with Deep Neural Networks and Functional AI for Capturing Structural Patterns

EasyChair Preprint 9694

Abstract

Quantum Generators is a means of achieving mass food production with short production cycles and when and where required by means of machines rather than land based farming. The process for agricultural practices for plant growth in different stages is simulated in a machine with a capacity to produce multiple seeds from one seed input using computational models of multiplication. Biological systems contain complex metabolic pathways with many synergies that make them difficult to predict from first principles and Protein synthesis is an example of such a pathway. Here we show how protein synthesis may be improved by capturing protein structures from a protein sequence  i.e. the amino acids character concealed. With this background, the neural network based on simplified version of GAN( Generative Adversarial Networks) is deployed, that get finely tuned during training, in this case to predict concealed residues and it is discovered that when the network is well-trained to predict the masked amino acids of natural protein sequences, then its internal weights are actually capturing,  protein structure. The Information about the structure being modelled develops within the network, as its weights describe the structural patterns and the protein structure is predicted from the patterns activated. The desired response or generator loss, was defined as the yield of the target product, and new experimental conditions and patterns were synergistically combined with automation in CellSynputer (where the unit level computer creates low-level instructions for the hardware). In this way, it is possible to script and run desired synthesis for assessing outcome for multiple crop tissues. Although the platform model given us a method of automating cellular assemblies in an intelligent framework embodied in multi-unit system however, this need to be tested using natural crop cells and it could be promising for us in achieving quantum generation.

Keyphrases: Artificial Intelligence, CellSynputer, Generative Adversarial Networks, Quantum Generators, protein structures

@booklet{EasyChair:9694,
  author    = {Poondru Prithvinath Reddy},
  title     = {Quantum Generators: Navigating Protein Sequences with Deep Neural Networks and Functional AI for Capturing Structural Patterns},
  howpublished = {EasyChair Preprint 9694},
  year      = {EasyChair, 2023}}