The selective activation of the innate immune system through blockade of immune checkpoint PD1-PDL1 interaction has proven effective against a variety of cancers. In contrast to six antibody therapies approved and several under clinical investigation, the development of small-molecule PD1-PDL1 inhibitors is still in its infancy with no such drugs approved yet. Nevertheless, a promising series of small molecules inducing PDL1 dimerization has revealed important spatio-chemical features required for effective PD1-PDL1 inhibition through PDL1 sequestration. In the present study, we utilized these features for developing machine-learning (ML) classifiers by fitting Random Forest models to six 2D fingerprint descriptors. A focused database of ~16 K bioactive molecules, including approved and experimental drugs, was screened using these ML models, leading to classification of 361 molecules as potentially active. These ML hits were subjected to molecular docking studies to further shortlist them based on their binding interactions within the PDL1 dimer pocket. The top 20 molecules with favorable interactions were experimentally tested using HTRF human PD1-PDL1 binding assays, leading to the identification of two active molecules, CRT5 and P053, with the IC50 values of 22.35 and 33.65 µM, respectively. Owing to their bioactive nature, our newly discovered molecules may prove suitable for further medicinal chemistry optimization, leading to more potent and selective PD1-PDL1 inhibitors. Finally, our ML models and the integrated screening protocol may prove useful for screening larger libraries for novel PD1-PDL1 inhibitors.
Keywords: 2D fingerprints; PD1-PDL1; PDL1 dimerization; cancer immunotherapy; immune checkpoint; machine learning; molecular dynamics; protein-protein interaction; random forest.