AI-driven structure- and ligand-based discovery of novel Ferrochelatase inhibitors from heterocyclic libraries.

Ferrochelatase (FECH), the terminal enzyme in the heme biosynthetic pathway, has emerged as a compelling therapeutic target for enhancing photodynamic therapy (PDT) and suppressing angiogenesis. Consequently, the design of FECH inhibitors represents a highly promising strategy for discovering dual-modality therapeutics, potentially offering a foundation to simultaneously explore phototoxicity and anti-angiogenic mechanisms. In this study, we deployed a rigorous and multi-tiered computational pipeline to identify potent heterocyclic FECH inhibitors from a compound library. The screening protocol integrated deep learning (DL)-assisted molecular docking and a dual-algorithm machine learning (ML) consensus classifier to systematically eliminate false positives. The orthogonal artificial intelligence filters isolated two structurally distinct and highly viable candidates: AZD-8055 and hSMG-1 inhibitor 11e. Subsequent 200 ns molecular dynamics simulations and Molecular Mechanics/Poisson-Boltzmann Surface Area (MM/PBSA) thermodynamic evaluations reveal that AZD-8055 demonstrated a stabilizing effect on the structure, exhibiting the most stable properties and favorable binding free energy. Conversely, hSMG-1 inhibitor 11e achieved the most potent binding free energy. Ultimately, this study demonstrates the immense utility of constructing an integrated, AI-driven structure- and ligand-based workflow to explore FECH inhibitors. By successfully identifying AZD-8055 and hSMG-1 inhibitor 11e as potential FECH inhibitors in silico, this approach provides a strong theoretical foundation for the development of dual-modality photodynamic and anti-angiogenic therapeutics.