The engineering of protein therapeutics has dramatically advanced through the integration of AI and machine learning technologies, as discussed in recent studies. Notable frameworks include AlphaFold, which enables accurate protein structure predictions from amino acid sequences, and ProteinMPNN, which addresses inverse folding challenges effectively. Moreover, advancements in generative models, like ProGen and ProGen2, facilitate rapid protein sequence generation, enhancing drug discovery workflows. The use of protein language models has revolutionized rational protein design, aiding in the optimization of structural and functional properties for therapeutic applications. This pioneering intersection of computational biology and deep learning not only streamlines the engineering process but also broadens the functional capabilities of proteins, creating novel therapeutic avenues. By harnessing these technologies, researchers can substantially improve the efficacy, specificity, and stability of protein-based therapeutics, positioning AI as a critical driver in biotechnology advancements.
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