Integrative computational pipeline for the in silico prioritization of potential KIF11-targeting drug candidates in glioblastoma

Glioblastoma multiforme (GBM) is the most aggressive primary brain tumor of the central nervous system and remains associated with poor prognosis. Although treatment strategies have improved, the blood-brain barrier (BBB) continues to impede effective drug delivery. Here, we implemented an integrative computational framework combining transcriptomic analysis, network biology, machine learning (ML), and structure-based validation to prioritize potential FDA-approved drug candidates for GBM. Differential gene expression analysis and network-based topological ranking identified TOP2A, KIF20A, and KIF11 as highly connected hub genes. Bioactivity data for TOP2A and KIF11 were curated from ChEMBL, rigorously filtered, and encoded using PaDEL fingerprints. The best-performing regression model for KIF11 was used to screen an FDA-approved drug library, identifying compounds with predicted potency (pIC₅₀ ≥ 6.5) and predicted BBB permeability. Selected candidates were further evaluated using molecular docking and 500 ns all-atom molecular dynamics simulations with MM-GBSA calculations to assess structural stability and relative binding energetics. Among four prioritized compounds, Ponatinib demonstrated the most favorable binding free energy, while Pimavanserin exhibited stable conformational behavior during simulation. These findings provide an in-silico prioritization framework for potential KIF11-targeting compounds in GBM. Experimental validation in relevant cellular and in vivo models will be required to determine biological and therapeutic relevance.