Ashutosh Shukla
Independent Researcher
Uttar Pradesh, India
Abstract
Advances in artificial intelligence (AI) have significantly influenced various domains in biotechnology, particularly in the design of synthetic proteins with potential applications in drug development. AI-based approaches, including machine learning algorithms and generative models, offer new avenues for modeling protein folding, stability, and interactions, facilitating faster drug discovery cycles. This study investigates the early methodologies and impact of AI-designed proteins, highlighting their role in optimizing ligand binding, targeting disease-specific pathways, and enhancing therapeutic efficacy. Drawing from bioinformatics tools and structure-prediction algorithms prevalent before late 2015, the study offers a synthesis of foundational AI techniques, validation methods, and application results in protein design. The manuscript outlines the integration of neural networks and evolutionary algorithms in engineering proteins with precise structural attributes, while also addressing validation mechanisms such as molecular dynamics simulations and in-vitro assays. Through literature synthesis and methodological evaluations, this research underscores the promising potential of AI in reshaping modern drug development frameworks.
Keywords
AI-designed proteins, protein engineering, machine learning, drug development, protein structure prediction, bioinformatics, therapeutic targeting, protein folding, in-silico modeling, molecular dynamics.
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