Attention Mechanism and Transformer Learning Based Protein Structure Prediction

Protein Property prediction is useful in the prediction of drug discovery, vaccine design and precision medicine.The existing protein folding approaches use methods like AlphaFold, Ribonucleic Acid (RNA) fold, CONTRAfold and RNA sequence based deep learning techniques that focuses on attention based neural networks for protein folding prediction.RNAfold employs dynamic programming to predict RNA secondary structure, while CONTRAfold used conditional log linear models to predict RNA structures technique with an accuracy is 90%.The limitations of AlphaFold approach are that it requires high computational resources for dynamic proteins and nonstandard interactions.RNAfold approach is limited by a simple energy model that may overlook complex interactions and doesn't account for environ-mental conditions and CONTRAfold is dependent on diverse and accurate training data, with a risk of overfitting, potentially reducing effectiveness on novel RNA sequences.This paper proposes an integrated advanced machine learning techniques with transformer model platforms to predict protein structure with improved accuracy than existing models.By developing tailored pre training models and leveraging RNA design algorithms, the proposed framework aims to predict molecular properties and enhance drug target affinity prognostication.Drawing from diverse disciplines, including molecular biology and graph theory, the paper employs optimized deep learning architectures to optimize model training processes.A bioactivity dataset for small molecules with binding affinities, toxicity endpoints and protein targets are considered for the research work.The proposed model represents a systematic effort to harness advanced machine learning for healthcare advancement with the efficiency of 99.9%.