The infection is a major cause of chronic liver disease, including chronic hepatitis, liver cirrhosis and liver cancer. There are no approved vaccines against hepatitis C. Recent research has shown that using machine learning is an efficient approach to look for these molecules.
One such model that researchers have developed recently is a web-based predictive tool called Pred-AHCP (predict anti-hepatitis C peptides) to evaluate if a peptide molecule can effectively inhibit the hepatitis C virus. The method employs a two-step computational filtering process that relies on statistical algorithms. Researchers can understand the underlying mechanisms that might make them work.
The findings from the development of Pred-AHCP can facilitate a more efficient discovery of lead peptide candidates than generic antiviral peptide prediction methods. Experiment with modifications guided by the model to maximise the effects of significant features, potentially creating peptides with greater efficacy, sustained virological response, and even improved pharmacological properties such as stability, permeability (how easily they diffuse across biological membranes), and bioavailability (what percentage of the drug actually reaches, or, is absorbed by the blood stream and remains active).
【MORE】