Summary: Since tens of millions of chemical compounds have been accumulated in public chemical databases, fast comprehensive computational methods to predict interactions between chemical compounds and proteins are needed for virtual screening of lead compounds. Previously, we proposed a novel method for predicting protein-chemical interactions using two-layer Support Vector Machine classifiers that require only readily available biochemical data, i.e. amino acid sequences of proteins and structure formulas of chemical compounds. In this article, the method has been implemented as the COPICAT web service, with an easy-to-use front-end interface. Users can simply submit a protein-chemical interaction prediction job using a pre-trained classifier, or can even train their own classification model by uploading training data. COPICAT's fast and accurate computational prediction has enhanced lead compound discovery against a database of tens of millions of chemical compounds, implying that the search space for drug discovery is extended by >1000 times compared with currently well-used high-throughput screening methodologies.
ASJC Scopus subject areas
- Statistics and Probability
- Molecular Biology
- Computer Science Applications
- Computational Theory and Mathematics
- Computational Mathematics