The Fourth CACHE Challenge – A Comprehensive Drug Discovery Workflow to Discover Potential Inhibitors of the Tyrosine Kinase Binding Domain of Cbl-b

25 September 2024, Version 1
This content is a preprint and has not undergone peer review at the time of posting.

Abstract

Our objective for the CACHE Challenge #4 was to identify novel inhibitors of the tyrosine kinase binding domain (TKBD) of Cbl-b using an automated workflow that bypassed the human component for selecting the final list of compounds for testing. We designed a workflow involving in-depth structural and SAR analysis, followed by extensive pharmacophore modeling. We implemented the learnings from previous challenges we participated in by selecting a larger library of molecules (~48M compounds, Enamine REAL database Diversity Set) for screening. We filtered this library according to our analyses and medicinal chemistry guidelines (i.e., Lipinski, Veber, Lilly med chem rules), and obtained ~19.4M compounds, for which we generated ~308M conformers. We screened these conformers against our pharmacophore models and selected ~340K compounds for further analysis. We used Fitted to score and rank these compounds; ~106K compounds passed our scoring thresholds and were docked against the TKBD of Cbl-b. After docking, we selected 200 compounds for MD by employing a complex selection process that involved clustering and residue interaction analysis. The MD simulation protocol was fully automated and modified to account for an increase in time and computational demand. At the end of the simulations, we used the MM-PBSA framework to obtain relative binding free energies from the MD trajectories. The top 150 predicted binders were submitted to the CACHE committee, and 87 compounds were ultimately delivered.

Keywords

Pharmacophore
Molecular dynamics
Fitted
Virtual screening

Supplementary materials

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Description
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Input and output files for computations
Description
Folder includes protein model and files, pharmacophore models, benchmarking small molecules, and final selections of predictions.
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Supplementary weblinks

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