Prediction model algorithm for biological toxicity of chemical molecules based on fuzzy neural network

Abstract

The invention discloses a prediction model algorithm for biological toxicity of chemical molecules based on a fuzzy neural network. A model prediction method based on the fuzzy neural network predictsand controls the biological toxicity of the chemical molecules in advance. For the problem that in the synthesis process of the chemical molecules, the influence of the hydrophobicity of the chemicalmolecules with different molecular structures on the biological toxicity has uncertainty, nonlinearity and other characteristics, and a precise model is difficult to establish in the synthesis process of the chemical molecules, a biological toxicity prediction model based on the fuzzy neural network is designed; through the adjustment of parameters of the fuzzy neural network, the method improvesthe processing capability of the neural network and achieves high-precision front-end prediction of the toxicity of chemical molecules. It is shown through experimental results that the method has good self-adaptivity, can predict and control the biological toxicity during the synthesis of the chemical molecules in advance, the cost of chemical synthesis is reduced, the time for chemical synthesis is shortened, and the pollution to the environment is reduced.

Description

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AI Technical Summary

Benefits of technology

This patented technology helps researchers study how different types of substances can cause harm or even kill animals when exposed to them for various purposes like food production or water pollution control measures. It also predicts potential health hazards caused by these compounds based on their ability to absorb into tissues during absorption process. By studying this relationships from both physical properties and biochemistry, scientists have developed models that make better predictions about animal safety issues related to exposure levels.

Problems solved by technology

Technics: Current methods for controlling harmful organic compounds during production include monitoring their effects over time (toxic indicators) and treatments such as neutralizing them afterwards. However these techniques can lead to significant financial loss due to increased costs associated therewith. Additionally, current models cannot effectively handle complex materials like pesticides because they require multiple instrumentation systems and may result in unpredictable results.

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