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Updated: May 30, 2025

Author Spotlight: A Computational Approach to Decipher Amino Acid Preferences in Multispecific Protein-Protein Interactions
Published on: January 26, 2024
Modern machine learning methods for protein property prediction
Arjun Dosajh1, Prakul Agrawal1, Prathit Chatterjee1
1Center for Computational Natural Sciences and Bioinformatics, International Institute of Information Technology, Hyderabad, 500032, Telangana, India.
Abstract:
Recent progress and development of artificial intelligence and machine learning (AI/ML) techniques have enabled addressing complex biomolecular problems. AI/ML models learn the underlying distribution of data they are trained on and when exposed to new inputs, they make predictions based on patterns and relationships previously observed in the training set. Further, generative artificial intelligence (GenAI) can be used to accurately generate protein structure or sequence from specific selected properties. This review specifically focuses on the applications of AI/ML in predicting important functional properties of proteins, and the potential prospects of reverse-engineering in depicting the sequence and structure, from available protein-property information.
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