First 4D-QSAR Study of Human Kynurenine 3 Monooxygenase (hKMO) Inhibitors: Integrating Chemical Space Networks and an Explainable Artificial Intelligence Platform for Neurodegenerative Disease Drug Discovery

Human kynurenine 3-monooxygenase (hKMO) is a crucial enzyme in the kynurenine pathway (KP), which increases neurotoxicity by converting kynurenine into 3-hydroxykynurenine and quinolinic acid (QA)both linked to oxidative stress and neuronal damage. KMO activity also reduces the neuroprotective metabolite kynurenic acid (KYNA), worsening disease progression. Inhibiting KMO counters these harmful effects since it restores KYNA levels, prevents toxic metabolite production, and reduces oxidative stress. This dual action makes KMO a vital therapeutic target in conditions such as neurodegenerative diseases, psychiatric disorders, acute pancreatitis, and immune dysregulation. In contemporary drug discovery, in silico design strategies offer significant advantages by revealing essential structural insights for lead optimization. The study is guided by three main objectives: (i) the development of a supervised machine learning (ML) model for a data set of hKMOis, (ii) chemical space networks (CSNs) analysis, and (iii) LQTA-QSAR (3D and 4D-QSAR) studies to generate interaction energy descriptors of Lennard-Jones (LJ) and Coulomb (C). To enhance accessibility, we present "phKMOi_v1.0," a Streamlit-based web application accessible at https://phkmoiv1.streamlit.app/. This platform not only supports the prediction but also allows experts and nonexperts to interpret the key molecular features influencing KMO inhibitory activity through an interactive waterfall plot. These modeling analyses will assist medicinal chemists in designing more potent hKMOis in the future.