{"id":6769,"date":"2025-10-09T09:50:00","date_gmt":"2025-10-09T00:20:00","guid":{"rendered":"https:\/\/hepatitissa.asn.au\/communitynews\/?p=6769"},"modified":"2025-11-28T10:40:46","modified_gmt":"2025-11-28T01:10:46","slug":"how-machine-learning-can-help-discover-anti-hepatitis-c-drugs","status":"publish","type":"post","link":"https:\/\/hepatitissa.asn.au\/communitynews\/2025\/10\/how-machine-learning-can-help-discover-anti-hepatitis-c-drugs\/","title":{"rendered":"How Machine Learning Can Help Discover Anti-Hepatitis C Drugs"},"content":{"rendered":"\n

Finding a single novel molecule for drug development can take years. Machine learning can help process billions of molecules in a day, and aid drug discovery against viral illnesses that currently lack effective treatments.<\/p>\n\n\n\n

Though anti-viral drugs for hepatitis C have\u00a0revolutionised treatment\u00a0<\/a>with high success rates, there\u2019s another class of drugs that may be highly effective, particularly in cases of drug resistance or even disease prevention.<\/p>\n\n\n\n

These drugs are based on peptide molecules that are short chains of amino acids and have higher specificity to target viral components, better solubility and lower toxicity. They can also work against every genotype of the virus and are easier to synthesise than conventional small-molecule drugs.<\/p>\n\n\n\n

The trouble, however, lies in looking for peptides that will work against a specific virus. This is because there are large numbers of peptides but very few that will work against a specific virus \u2014 a process akin to finding a needle in a haystack.<\/p>\n\n\n\n

Recent research has shown that using machine learning is an efficient approach to look for these molecules. Though machine learning\u00a0has been used<\/a>\u00a0to predict general antimicrobial and antiviral peptides,\u00a0only a handful of studies<\/a>\u00a0have concentrated on those that target specific viruses such as hepatitis C.<\/p>\n\n\n\n

\"\"\n\t\t\t\n\t\t\t\t\n\t\t\t<\/svg>\n\t\t<\/button><\/figure>\n\n\n\n

Hunt for Peptides<\/strong><\/h2>\n\n\n\n

Proteins and peptides have garnered enough attention from scientists and researchers worldwide for their potential use in diagnostics, therapeutics and\u00a0drug delivery systems<\/a>.<\/p>\n\n\n\n

Insulin, for instance, is a peptide with 51 amino acids, and its discovery and development is\u00a0considered<\/a>\u00a0one of the most significant advancements in drug discovery. More recently, the weightloss drug\u00a0Semaglutide,<\/a>\u00a0a peptide with 31 amino acids, is being hailed as a game-changer for patients with Type 2 diabetes, obesity and heart disease.<\/p>\n\n\n\n

\"\"\n\t\t\t\n\t\t\t\t\n\t\t\t<\/svg>\n\t\t<\/button>
The two peptides (in white and blue) that make up insulin. Image by User:AtikaAtikawa – Own work, CC BY-SA 4.0<\/a>.<\/figcaption><\/figure>\n\n\n\n

Proteins and peptides are both chemically the same. A peptide chain is made up of amino acid residues, the same chemical molecules that constitute proteins and enable cells to perform various functions.<\/p>\n\n\n\n

Given that 22 amino acids are found in nature, it is possible to create distinct short peptide chains in numerous ways. For example, synthesising a peptide chain that is made up of 10 amino acid residues can yield a large number \u2014 over 26 trillion<\/em>\u2014 of distinct chains.<\/p>\n\n\n\n

However, very few of these peptides have therapeutic effects, and even fewer can act against a specific virus.<\/p>\n\n\n\n

..Machine learning models … can screen billions of molecules, allowing researchers to process a billion molecules each day<\/p><\/blockquote><\/figure>\n\n\n\n

Besides, the number of FDA-approved bioactive peptides \u2014 those with therapeutic properties \u2014 is quite limited. Hence, identifying those that exhibit antimicrobial and antiviral properties among the vast possibilities is a herculean task. Here, machine learning has come to the aid of researchers.<\/p>\n\n\n\n

Traditional methods for identifying effective anti-hepatitis C peptides \u2014 or any drug molecule for that matter \u2014  are time-consuming and resource-intensive.<\/p>\n\n\n\n

Some estimates\u00a0<\/a>suggest that traditional methods may require synthesising up to 5,000 molecules for over four to six years to find one promising candidate for drug development.<\/p>\n\n\n\n

Machine learning models, however, can bring down the screening time drastically. They can screen billions of molecules, allowing researchers to process a billion molecules each day. This makes the drug discovery process, particularly the early stages of drug development, quicker and cost-efficient.<\/p>\n\n\n\n

\"\"\n\t\t\t\n\t\t\t\t\n\t\t\t<\/svg>\n\t\t<\/button>
The web interface for the publicly available Pred-AHCP analysis engine<\/figcaption><\/figure>\n\n\n\n

One such model that researchers have developed recently is a\u00a0web-based predictive tool called Pred-AHCP<\/a>\u00a0(predict anti-hepatitis C peptides) to evaluate if a peptide molecule can effectively inhibit the hepatitis C virus.<\/p>\n\n\n\n

The model does this by analysing its amino acid composition and physico-chemical properties.<\/p>\n\n\n\n

The method employs a two-step computational filtering process that relies on statistical algorithms. This approach is particularly useful because not only does it predict whether a peptide is likely to be anti-hepatitis C, but it also explains the reasons for its effectiveness by highlighting the molecule\u2019s most significant molecular characteristics.<\/p>\n\n\n\n

Hence, rather than just predicting candidates, researchers can understand the underlying mechanisms that might make them work.<\/p>\n\n\n\n

Virus-Specific Prediction Tools<\/strong><\/h2>\n\n\n\n

Beyond treating hepatitis C, this approach can be adapted to develop similar predictive tools to look for peptides against other viruses, thus creating a family of virus-specific prediction tools.<\/p>\n\n\n\n

This would be particularly valuable for viruses that currently lack effective treatments such as HIV, Herpes Simplex Virus and Zika.<\/p>\n\n\n\n

The\u00a0findings<\/a>\u00a0from the development of Pred-AHCP can facilitate a more efficient discovery of lead peptide candidates than generic antiviral peptide prediction methods.<\/p>\n\n\n\n

this machine learning model … provides … insights into which molecular features might make particular peptides effective against hepatitis C.<\/p><\/blockquote><\/figure>\n\n\n\n

This is in large measure due to the explainable nature of this machine learning model that provides synthetic biochemists, organic chemists  and bioengineers with insights into which molecular features might make particular peptides effective against hepatitis C.<\/p>\n\n\n\n

Knowing what features work well means that the scientists can then focus on designing medications that emphasise the most effective molecular arrangements. They could, for instance, 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\u00a0bioavailability (<\/a>what percentage of the drug actually reaches, or, is absorbed by the blood stream and remains active).<\/p>\n\n\n\n

Besides, this model\u00a0is available<\/a>\u00a0as a webserver, allowing for greater accessibility among researchers without specialised computational expertise.<\/p>\n\n\n\n

Such democratisation of advanced computational tools by sharing therapeutic approaches has the potential to enhance collaboration and innovation in antiviral research globally for various infectious diseases.<\/p>\n\n\n\n

\n\n\n\n

This is an edited version of an article originally published under<\/em>\u00a0Creative Commons<\/em><\/a>\u00a0by<\/em>\u00a0360info<\/em><\/a>\u2122.<\/em><\/p>\n\n\n\n

Akash Saraswat<\/strong> is a senior research scholar at BML Munjal University, Gurugram, Haryana<\/em><\/p>\n\n\n\n

Bipin Singh<\/strong> is Assistant Professor, Centre for Life Sciences, Mahindra University, Hyderabad, Telangana<\/em><\/p>\n\n\n\n

Arijit Maitra<\/strong> is Associate Professor, School of Engineering and Technology, BML Munjal University, Gurugram, Haryana<\/em><\/p>\n\n\n\n

<\/p>\n","protected":false},"excerpt":{"rendered":"

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