COMET:Combined Matrix for Elucidating Targets
This provides a faster and more accurate tool for researchers in pharmacology and drug discovery to predict compound targets, though it appears incremental as an improvement over existing algorithms.
The researchers tackled the problem of identifying interaction targets for bioactive compounds by developing COMET, a multi-technological modular target prediction tool that processes tasks in under 10 minutes on average and achieves nearly 80% accuracy in identifying at least one true target within the top 15 predictions.
Identifying the interaction targets of bioactive compounds is a foundational element for deciphering their pharmacological effects. Target prediction algorithms equip researchers with an effective tool to rapidly scope and explore potential targets. Here, we introduce the COMET, a multi-technological modular target prediction tool that provides comprehensive predictive insights, including similar active compounds, three-dimensional predicted binding modes, and probability scores, all within an average processing time of less than 10 minutes per task. With meticulously curated data, the COMET database encompasses 990,944 drug-target interaction pairs and 45,035 binding pockets, enabling predictions for 2,685 targets, which span confirmed and exploratory therapeutic targets for human diseases. In comparative testing using datasets from ChEMBL and BindingDB, COMET outperformed five other well-known algorithms, offering nearly an 80% probability of accurately identifying at least one true target within the top 15 predictions for a given compound. COMET also features a user-friendly web server, accessible freely at https://www.pdbbind-plus.org.cn/comet.