CLC-Pred (Cell Line Cytotoxicity Predictor) 2.0 is a web-service for in silico prediction of cytotoxic effect of chemical and natural compounds in non-transformed and cancer cell lines based on structural formula. CLC-Pred provides a prediction of the cytotoxicity of a compound to assess the relevance of the substance's inclusion in experimental screening.
CLC-Pred 2.0 provides three types of qualitative prediction: (1) cytotoxicity against 391 tumor and 47 normal human cell lines based on ChEMBL and PubChem data (128,545 structures) with a mean accuracy of prediction (AUC), calculated by the leave-one-out (LOO CV) and the 20-fold cross-validation (20F CV) procedures, of 0.925 and 0.923, respectively; (2) cytotoxicity against the NCI60 tumor cell-line panel based on the Developmental Therapeutics Program’s NCI60 data (22,726 structures, the link “Compound activity: DTP NCI-60”) with different thresholds of IG50 data (100, 10 and 1 nM) and a mean accuracy of prediction from 0.870 to 0.945 (LOO CV) and from 0.869 to 0.942 (20F CV), respectively; (3) 2170 molecular mechanisms of actions based on ChEMBL and PubChem data (656,011 structures) with a mean accuracy of prediction 0.979 (LOO CV) and 0.978 (20F CV).
All predictions of CLC-Pred 2.0 are based on PASS (Prediction of Activity Spectra for Substances) technology (https://www.way2drug.com/PASSonline ).
The previous version – CLC-Pred is available at https://www.way2drug.com/cell-line/
Try BC CLC-Pred - a special web-application for simultaneous quantitative and qualitative predictions of IC50 and IG50 values for the nine breast cancer cell lines (T47D, ZR-75-1, MX1, Hs-578T, MCF7-DOX, MCF7, Bcap37, MCF7R, BT-20).
Please cite us: Lagunin, A.A.; Rudik, A.V.; Pogodin, P.V.; Savosina, P.I.; Tarasova, O.A.; Dmitriev, A.V.; Ivanov, S.M.; Biziukova, N.Y.; Druzhilovskiy, D.S.; Filimonov, D.A.; Poroikov, V.V. CLC-Pred 2.0: A Freely Available Web Application for In Silico Prediction of Human Cell Line Cytotoxicity and Molecular Mechanisms of Action for Druglike Compounds. Int. J. Mol. Sci. 2023, 24, 1689. https://doi.org/10.3390/ijms24021689
