Exploitation, QSAR, and Structure-Based Modeling of Benzothiazole Derivatives as Protoporphyrinogen Oxidase IX Inhibitors

High Resolution Image Download MS PowerPoint Slide A structure–activity relationship (SAR) study employing multivariate image analysis (MIA) descriptors was performed to cluster benzothiazole derivatives, including herbicide-like compounds, by structural similarity and protoporphyrinogen oxidase IX (PPO) inhibition. Partial least-squares (PLS) models based on van der Waals radii and electronegativity descriptors identified substituents most influential for activity through MIA contour maps of regression coefficients and VIP scores. A genetic algorithm selected key descriptors for a simplified multiple linear regression (MLR) model enabling rapid p K i prediction. Both the PLS and MLR models were robust and predictive ( r 2 = 0.85–0.88, q 2 = 0.75–0.83, r 2 pred = 0.77–0.86). A proposed compound ( P12 ) showed predicted activity (p K i = 7.93) comparable to the most potent derivative (p K i = 8.14) but higher lipophilicity (log P = 5.45 vs 4.55), suggesting improved leaf membrane permeation. These results demonstrate the usefulness of MIA-based modeling for designing potent, permeable PPO inhibitors. In addition, a molecular docking study and molecular dynamics simulations were performed to investigate the PPO binding mode, validating the MIA-QSAR model specification. Key residues were identified, and strong thickness and stability of the candidates were observed. A proposed derivative showed greater potential than the library compounds, and a synthetic route for its production was suggested.