QSAR-Lit: a no-code platform for predictive QSAR model development - from data curation to virtual screening

Abstract

The development of predictive quantitative structure-activity relationship (QSAR) models using machine learning (ML) algorithms has become increasingly feasible due to the growing availability of chemical libraries with experimental data. These models can accelerate the drug discovery process and reduce failure rates by enabling data-driven decision-making. However, existing standalone software often lacks several critical components necessary for effective data preparation and modeling. Here, we introduce QSAR-Lit, an innovative, no-code, and comprehensive workflow designed for curating chemical and biological data, generating QSAR models, and performing virtual screening through an interactive Python-based Streamlit dashboard. The QSAR model development process begins with data curation, collecting and cleaning data on chemical structures and their biological activities. The next step is model building, where the curated data is used to train and optimize QSAR models. Finally, QSAR-Lit provides virtual screening, allowing QSAR models to predict the activity of new chemical structures. This application efficiently screens libraries of chemical compounds, assisting researchers in identifying and prioritizing potential candidates for further investigation.