A Chemoinformatics Investigation of Spectral and Quantum Chemistry Patterns for Discovering New Drug Leads from Natural Products Targeting the PD-1/PD-L1 Immune Checkpoint, with a Particular Focus on Naturally Occurring Marine Products
(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 target...
Saved in:
| Main Authors: | , , , |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
MDPI AG
2025-06-01
|
| Series: | Marine Drugs |
| Subjects: | |
| Online Access: | https://www.mdpi.com/1660-3397/23/6/247 |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
| Summary: | (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 (IC<sub>50</sub>) 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) classification model. (3) Conclusions: The model incorporating 570 spectral descriptors from NMR SPINUS was selected for the optimization process, and this approach yielded results for the external test set with a sensitivity of 0.74, specificity of 0.81, overall predictive accuracy of 0.78, and Matthews correlation coefficient (MCC) of 0.55. The strategy used here for estimating the IC<sub>50</sub> 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 the spectra of mixtures by combining the spectra of individual compounds. |
|---|---|
| ISSN: | 1660-3397 |