ChemCarcinoPred: Carcinogenicity Prediction of Small Drug-Like Molecules Using LightGBM and Molecular Fingerprints

Abstract

The conventional method for determining whether a drug is carcinogenic involves subjecting rodents to a 2-year bioassay, but this approach is both time-consuming and expensive, not to mention unethical. Consequently, machine learning techniques have emerged as a popular alternative. One such technique is ensemble learning, which aims to create more accurate and robust models. In this particular study, the LightGBM model was utilized to predict the carcinogenicity of chemicals using its fingerprints. Molecular fingerprints were generated from the Simplified Molecular-Input Line-Entry System (SMILES) of 1003 chemicals from the Carcinogenic Potency Database (CPDB) dataset. The performance of the LightGBM model was found to be superior to other machine learning models reported in previous research. To further validate the model, it was tested on a database related to humans from the International Agency for Research on Cancer (IARC), as well as on chemicals that were withdrawn from the market between 1950 and 2014. The results showed that the LightGBM model was effective in identifying carcinogenic chemicals, suggesting that this approach could potentially replace traditional methods of carcinogenicity testing in the future.