A Chemoinformatics Investigation of Spectral and Quantum Chemistry Patterns for Discovering New Drug Leads from Marine Natural Products Targeting the PD-1/PD-L1 Immune Checkpoint

(1) Background: Although the field of natural product (NP) drug discovery has been extensively developed, there are still several bottlenecks hindering the development of drugs from NPs. The PD-1/PD-L1 immune checkpoint axis plays a crucial role in immune response regulation. Therefore, drugs targeting this axis can disrupt the interaction and enable immune cells to continue setting up a response against the cancer cells; (2) Methods: We have explored the immuno-oncological activity of NPs targeting the PD-1/PD-L1 immune checkpoint by estimating the half maximal inhibitory concentration (IC50) through molecular docking scores and predicting it using machine learning (ML) models. The LightGBM (Light Gradient Boosted Machine) a tree-based ML technique emerged as the most effective approach and was used for building the quantitative structure–activity relationship (QSAR) regression models; (4) Conclusions: The model incorporating 570 spectral descriptors from NMR SPINUS was selected for optimization process, and this approach yielded results for the training set with a MAE of 0.22, RMSE of 0.30 and R-squared of 0.86. The strategy used here of estimating the IC50 from docking scores and predicting it through ML models appears to be a promising approach for pure compounds. Nevertheless, further optimization is indicated, particularly through the simulation of spectra of mixtures by combining the spectra of individual compounds.