DrugMint: a webserver for predicting and designing of drug-like molecules

DrugMint is a specialized computational platform designed to differentiate between drug-like and non-drug-like compounds. This resource is particularly valuable in the early stages of drug discovery, where it helps prioritize small molecules that have the potential to become effective oral medications. Web Server: https://webs.iiitd.edu.in/oscadd/drugmint/index.php Citation Dhanda, S.K., Singla, D., Mondal, A.K. et al. DrugMint: A web server for predicting and designing drug-like molecules. Biology Direct, 8:28. https://doi.org/10.1186/1745-6150-8-28 About the Research Identifying "drug-likeness" is essential to avoid late-stage clinical failures caused by poor pharmacokinetic properties. While traditional methods like Lipinski’s "Rule of Five" provide general guidelines, DrugMint uses a more sophisticated machine learning approach to analyze the structural and chemical features of a molecule. Diverse Dataset: The model was developed using a large dataset of FDA-approved drugs (representing drug-like compounds) and a collection of metabolites and common chemicals (representing non-drug-like compounds). Methodology: The platform utilizes Support Vector Machine (SVM) algorithms based on molecular descriptors and fingerprints.