Retrospective Benchmarking and Novel Shape-Pharmacophore Based Implementation of the MORLD Method for the Autonomous Optimization of 3-Aroyl-1,4-diarylpyrroles (ARDAP)

The use of artificial intelligence (AI) is increasingly integral to the drug-discovery process, and among AI-driven methodologies, deep generative models stand out as one of the most promising approaches for hit identification and optimization. Here, we report a retrospective benchmarking analysis of a series of tubulin inhibitors, 3-aroyl-1,4-diarylpyrroles (ARDAP), using the deep-generative algorithm Molecule Optimization by Reinforcement Learning and Docking (MORLD) in combination with five docking software (QuickVina 2, AutoDock-GPU, PLANTS, GOLD, and Glide). Our results indicate that the performance of the MORLD/docking workflow is highly dependent on the availability of initial structural information; only the incorporation of a core constraint in Glide yields satisfactory predictions. To address this limitation, we developed a docking-free variant of MORLD that exploits receptor-derived shape similarity and pharmacophore alignment. Kernel-density estimation, convergence analysis, and SMARTS-based success-rate metrics confirmed that this Shape-Pharmacophore implementation autonomously generates chemically valid, SAR-consistent analogues of the reference compounds. Collectively, this work demonstrates a practical, structure-only driven paradigm for reinforcement-learning-based compound optimization, thereby extending the reach of AI-enabled drug design beyond traditional docking workflows.