A neural network based classification scheme for cytotoxicity predictions:Validation on 30,000 compounds

A neural network based classification approach using cytotoxicity data measured for 30,000 compounds has been developed to predict cytotoxicity. Elimination of cytotoxic compounds in the early phases of drug discovery can save substantial amounts of research and development costs. An artificial neural network based approach using atomic fragmental descriptors has been developed to categorize compounds according to their in vitro human cytotoxicity. Fragmental descriptors were obtained from the Atomic7 linear log P calculation method implemented in Pallas PrologP program. We used cytotoxicity values obtained from an in-house screening campaign of a diverse set of 30,000 drug-like molecules. The training set included only the most and least toxic 12,998 compounds, however, cytotoxicity data for all compounds were used for validation. The proposed approach can be safely used for filtering out potentially cytotoxic candidates from the development pipeline before synthesis or assays during lead development or lead optimisation. The trained neural network misclassified less than 5% percent of the non-toxic and 9% of the toxic compounds.