Abstract
Identifying allergenic proteins in raw materials can help reformulate products to make them safer for sensitive populations. Computational tools are powerful for identifying the allergenic potential of proteins and chemicals in food and personal care products. These tools can help minimize allergic risks and guide safer product development by leveraging sequence analysis, structural modelling, and epitope mapping. Food allergens can sometimes impact how a drug is processed or worsen allergic responses. These interactions can pose significant health risks and complicate treatment plans. In addition to this, certain foods can influence how drugs are absorbed, metabolized, or broken down in the body. While not all interactions trigger allergies, they may amplify reactions or side effects. Cross-reactivity occurs when proteins in foods share structural similarities with components in certain drugs, leading the immune system to react to both mistakenly. Here, we present AllergyPred, a web server that predicts both protein- and chemical-based allergens. Five different models take protein IDs, sequences, chemical IDs, and structures as inputs for predicting respective allergy endpoints. The AllergyPred web server is free and open to all users, and there is no login requirement. It can be accessed via https://allergypred.charite.de/AllergyPred/. The prediction results will be presented in the form of a table and can be downloaded in several file formats supporting users to report the results for the respective projects.