Discovery of dual MAO-B/Sig-1R modulators based on indole/oxindole scaffolds via machine learning and molecular modeling

Dual modulation of monoamine oxidase B (MAOB) and Sigma-1 receptor (Sig-1R) represents a promising multi-target strategy for neurodegenerative disorders.¹ To rationalize synthetic efforts toward novel multi-target directed ligands, a multi-tiered computational screening funnel was applied to investigate indole/oxindole piperazine scaffolds.² Initially, Support Vector Machine-based QSAR models (>88% accuracy) screened 147 natural and nature-inspired compounds, identifying 11 dual-target candidates. Molecular docking further refined this selection to 4 molecules showing favorable, simultaneous binding modes within both MAO-B and Sig-1R cavities. Finally, 150 ns molecular dynamics simulations confirmed structural stability for the two most promising protein-ligand complexes (compounds 072 and 102). Rather than blindly synthesizing a large compound library, this machine learning-guided approach successfully narrowed the synthetic workload to two highly validated lead candidates. Currently, the synthetic routes to obtain those compounds are underway for subsequent in vitro neuroprotective evaluations.³