Computer-aided drug discovery (CADD) is nurtured by late advances in big data analytics and Artificial Intelligence (AI) towards enhanced drug discovery (DD) outcomes. In this context, reliable datasets are of utmost importance. We herein present CidalsDB a novel web server for AI-assisted DD against infectious pathogens, namely Leishmania parasites and Coronaviruses. We performed a literature search on molecules with validated anti-pathogen effects. Then, we consolidated these data with bioassays from PubChem. Finally, we constructed a database to store these datasets and make them accessible and ready-to-use for the scientific community through CidalsDB, a web-based interface. In a second step, we implemented and optimized four machine learning (ML) and three deep learning (DL) algorithms that optimally predicted the biological activity of molecules. Random Forests (RF), Multi-Layer Perceptron (MLP) and ChemBERTa were the best classifiers of anti-Leishmania molecules, while Gradient Boosting (GB), Graph-Convolutional Network (GCN) and ChemBERTa achieved the best performances on the Coronaviruses dataset. All six models were optimized and deployed through CidalsDB as anti-pathogen activity prediction models.Scientific contributionCidalsDB is an open access web-based tool that allows browsing and access to ready-to-use datasets of anti-pathogen molecules, alongside best performing AI models for biological activity prediction. It offers a democratized no-code platform for AI-based CADD, which shall foster innovation and collaboration within the DD community. CidalsDB is accessible through https://cidalsdb.streamlit.app/ .