Quantum Generators: Building a Model that Generate Crop Molecules using Molecular Synthesis and Generative AI

EasyChair Preprint 14519

Abstract

Quantum Generators is a means of achieving mass food production with short production cycles  by means of machines. The process for agricultural practices for plant growth in different stages is simulated in a machine to produce multiple seeds from one seed input using computational models. Protein synthesis contain complex metabolic pathways with many synergies that make them difficult to predict. Here we show how protein synthesis may be improved by capturing protein structures in quantum generator( a process involving stages like - protein sequencing to generate crop molecules )from a protein sequence. It typically consists of stages like target molecules, sequence design, robotic testing, and rigorous trials before final buildup of molecules. However, it is essential to generate correct molecules with close monitoring at each step in a controlled–intensive CellSynputer platform with real-time monitoring. Over here, we try to take advantage of generative AI to produce novel protein molecules with robotic synthesis. For this, we used a different class of AI models – a variant of Autoencoder for more improved generation quality. With this, we build a Variational Autoencoder that use simulated structures to learn the structural patterns of molecules and generate new ones as neural networks are used for data reconstruction, and generation. The model converts input structures used as a numerical representation to process data to generate new molecules that comes from the same embedding pattern as the input data, enabling generative molecular design in a quantum generator.and in that respect an implementation of Variational Autoencoder algorithm based on small model is presented. Although the platform model with cell type intelligence as modular CellSynputer given us a method of automating and optimizing cellular assemblies however, this need to be tested using natural crop cells for quantum generation.

Keyphrases: CellSynputer, Generative AI, Quantum Generators, variational autoencoder

@booklet{EasyChair:14519,
  author    = {Poondru Prithvinath Reddy},
  title     = {Quantum Generators: Building a Model that Generate Crop Molecules using Molecular Synthesis and Generative AI},
  howpublished = {EasyChair Preprint 14519},
  year      = {EasyChair, 2024}}