Machine Learning to Predict Teratogenicity: Theory and Practice.

Machine learning (ML) is a subfield of artificial intelligence (AI) that consists of developing algorithms that can automatically learn patterns and relationships from data, without being explicitly programmed. It continues to advance with the development of more sophisticated algorithms, increased computational power, and larger datasets, leading to significant advancements in AI technology. With the significant progress made in ML, the need to apply these systems in the area of teratogenicity is growing.

Artificial neural networks: non-linear QSAR studies of HEPT derivatives as HIV-1 reverse transcriptase inhibitors.

Structure-anti HIV activity relationships were established for a sample of 80 1-[2-hydroxyethoxy-methyl]-6-(phenylthio)thymine (HEPT) using a three-layer neural network (NN). Eight structural descriptors and physicochemical variables were used to characterize the HEPT derivatives under study. The network's architecture and parameters were optimized in order to obtain good results.

Neural networks: Accurate nonlinear QSAR model for HEPT derivatives.

A nonlinear quantitative structure-anti-HIV-1-activity relationship (QSAR) study was investigated in a series of 1-[2-hydroxyethoxy-methyl]-6-(phenylthio) thymine] (HEPT) derivatives acting as nonnucleoside reverse transcriptase inhibitors (NNRTIs). This QSAR study has been undertaken by a three-layered neural network (NN) using molecular descriptors known to be responsible for the anti-HIV-1 activity. The usefulness of the model and the nonlinearity of the relationship between molecular descriptors and anti-HIV-1 activity have been clearly demonstrated.