Algorithm Model Construction of Protein Structure Prediction and Drug Interaction Analysis in Biopharmaceuticals
Abstract
The purpose of this article is to explore the algorithm model construction of protein structure prediction and drug interaction analysis in biopharmaceuticals. Protein is the basic unit of life activities. Accurate prediction of its structure is very important for understanding the function of protein and designing new drugs. The interaction analysis between drugs and target proteins is also a key link in the process of drug research and development. Based on deep learning (DL) technology, this article constructs an algorithm model for protein structure prediction and drug interaction analysis. In protein structure prediction, the model can accurately predict the secondary structure of protein by learning a lot of protein sequence and structure data. In the aspect of drug interaction analysis, the model uses the structural information of drug molecules and target proteins to predict the binding energy between them, which provides a basis for drug screening. The results show that the model has achieved high accuracy in protein structure prediction and drug interaction analysis, showing obvious advantages compared with other commonly used methods.
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