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The ability to create such molecules by computation alone may accelerate drug development. This number was filtered down to around 100 peptides that were synthesised and tested in the wet lab. Instead of using a single pdb structure, generative models that take a set of peptide structure conformers or a trajectory of structure changes (computed by molecular dynamics) as input may lead to more optimal peptide representation learning and subsequent peptide generation.
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This is a rapidly emerging field that moves beyond traditional methods by using machine learning and deep learning to accelerate the discovery, de novo design, and optimization of therapeutic peptides, as well as peptides for areas like food and cosmetics. Additionally, it presents case studies and future prospectives that demonstrate the impact of ai on the generation of new peptide drugs. This review explores the current landscape of ai applications in peptide drug discovery, highlighting its potential, challenges, and ethical considerations
