engleski

On Approximating the pIC50 Value of COVID-19 Medicines In Silico with Artificial Neural Networks

In the case of pandemics such as COVID-19, the rapid development of medicines addressing the symptoms is necessary to alleviate the pressure on the medical system. One of the key steps in medicine evaluation is the determination of pIC50 factor, which is a negative logarithmic expression of the half maximal inhibitory concentration (IC50). Determining this value can be a lengthy and complicated process. A tool allowing for a quick approximation of pIC50 based on the molecular makeup of medicine could be valuable. In this paper, the creation of the artificial intelligence (AI)-based model is performed using a publicly available dataset of molecules and their pIC50 values. The modeling algorithms used are artificial and convolutional neural networks (ANN and CNN). Three approaches are tested—modeling using just molecular properties (MP), encoded SMILES representation of the molecule, and the combination of both input types. Models are evaluated using the coefficient of determination (R2) and mean absolute percentage error (MAPE) in a five-fold cross-validation scheme to assure the validity of the results. The obtained models show that the highest quality regression (R2¯¯¯¯=0.99, σR2¯¯¯¯=0.001 ; MAPE¯¯¯¯¯¯¯¯¯¯¯=0.009%, σMAPE¯¯¯¯¯¯¯¯¯¯¯¯=0.009), by a large margin, is obtained when using a hybrid neural network trained with both MP and SMILES.

artificial neural networks ; convolutional neural networks ; machine learning ; pIC50 ; regression modeling ; SMILES

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