Tuning the Biological Activity of PI3Kδ Inhibitor by the Introduction of a Fluorine Atom Using the Computational Workflow

Abstract

As a member of the class I PI3K family, phosphoinositide 3-kinase δ (PI3Kδ) is an important signaling biomolecule that controls immune cell differentiation, proliferation, migration, and survival. It also represents a potential and promising therapeutic approach for the management of numerous inflammatory and autoimmune diseases. We designed and assessed the biological activity of new fluorinated analogues of CPL302415, taking into account the therapeutic potential of our selective PI3K inhibitor and fluorine introduction as one of the most frequently used modifications of a lead compound to further improve its biological activity. In this paper, we compare and evaluate the accuracy of our previously described and validated in silico workflow with that of the standard (rigid) molecular docking approach. The findings demonstrated that a properly fitted catalytic (binding) pocket for our chemical cores at the induced-fit docking (IFD) and molecular dynamics (MD) stages, along with QM-derived atomic charges, can be used for activity prediction to better distinguish between active and inactive molecules. Moreover, the standard approach seems to be insufficient to score the halogenated derivatives due to the fixed atomic charges, which do not consider the response and indictive effects caused by fluorine. The proposed computational workflow provides a computational tool for the rational design of novel halogenated drugs.

Identification of Potential Aldose Reductase Inhibitors Using Convolutional Neural Network-Based in Silico Screening

“…It should be noted that the ALR2 inhibitors identified in this study were not as potent as reported in previous studies, despite having better docking scores, which can be attributed to the known limitation of these docking scoring functions to accurately predict binding affinities. While these docking methods can help discriminate between different ligands based on binding affinity, it is not clear whether they will succeed in biological evaluation. − Nevertheless, the hits generated contain new chemotypes and unfold new avenues for further structural refinement in a hit-to-lead optimization process, thus consolidating them to be new ALR2 potent inhibitors in the future.…”