Utilizing a combined approach of machine learning and structure-based drug design principles to identify potential hits targeting SphK1

Sphingosine kinase (SphK1) is acrucial enzyme that aids in the processing of sphingolipids by adding a phosphate group to sphingosine, converting it into sphingosine-1-phosphate. A recent study has suggested that dysregulation of SphK1 is linked to tumor progression and metastasis in lung and bladder cancers,making SphK1 a promising therapeutic target for these diseases. In this study, we employedmachine learning-based virtual screening along with structure-based drug design to identify potential SphK1 inhibitors with diverse chemical scaffolds. A total of 16 machine learning models were generated using molecular fingerprints, and the most effective models were employed to conductvirtual screening of the Maybridge library. The screened compounds were then subjected to molecular docking to determine a suitable docked pose against the SphK1 protein. Upon visualization of the best docked compounds, we found that six compounds exhibited strong interactions with the SphK1 protein compared to the control (SQS). To further support our findings, we conducted 100 ns long molecular dynamics (MD) simulations of all six compounds to analyzeconformational changes and stability. Two compounds (SCR00139 and SCR00133) demonstratedpromising stability and fit well within the binding pocket of the SphK1 protein. Furthermore, MM-PBSA and MM-GBSA studies were carried out on these two compounds, providing favorable relative binding estimations. This study introduces an integrated pipeline of machine learning-based virtual screening for the identification of new scaffolds targeting cancer progression. However, in vitro evaluations are necessary to assess the efficacy of these compounds.