Three-dimensional QSAR of HPPD inhibitors, PSA inhibitors, and anxiolytic agents: Effect of tautomerism on the CoMFA models

4-Hydroxyphenylpyruvate Dioxygenase Inhibitors: From Molecular Design to Synthesis

Weed resistance is a critical issue in crop production. Among the known herbicides, 4-hydroxyphenylpyruvate dioxygenase (HPPD) inhibitors are crucial for addressing weed resistance. HPPD inhibitors constitute a pivotal aspect of contemporary crop protection strategies. The advantages of these herbicides are their broad weed spectrum, flexible application, and excellent compatibility with other herbicides. They also exhibit satisfactory crop selectivity and low toxicity and are environmentally friendly. An increasing number of new HPPD inhibitors have been designed by combining computer-aided drug design with conventional design approaches. Herein, the molecular design and structural features of innovative HPPD inhibitors are reviewed to guide the development of new HPPD inhibitors possessing an enhanced biological efficacy.

Integrated Virtual Screening and Validation toward Potential HPPD Inhibition Herbicide

4-Hydroxyphenylpyruvate dioxygenase (EC 1.13.11.27, HPPD) is one of the most widely studied herbicide targets and has gained significant attention. To identify potential effective HPPD inhibitors, a rational multistep virtual screening workflow was built, which included CBP models (based on the receptor-ligand interactions in the crystal complex), Hypogen models with activity prediction ability (according to the derivation of structure-activity relationships from a set of molecules with reported activity values), and a consensus docking procedure (consisting of LibDock, Glide, and CDOCKER). About 1 million molecules containing diketone or β-keto-enol substructures were filtered by Lipinski's rules, CBP model, and Hypogen model. A total of 12 compounds with similar docking postures were generated by consensus docking. Eventually, four molecules were screened based on the specific binding pattern and affinity of the HPPD inhibitor. The biological evaluation in vivo displayed that compounds III-1 and III-2 exhibited comparable herbicidal activity to isoxaflutole and possessed superior safety on various crops (wheat, rice, sorghum, and maize). The ADMET prediction (absorption, distribution, metabolism, excretion, and toxicity) showed that compound III possessed relatively good toxicological results. This work provides a theoretical basis and valuable reference for the virtual screening and molecular design of novel HPPD inhibition herbicides.

Discovery of novel HPPD inhibitors: Virtual screening, molecular design, structure modification and biological evaluation

4-Hydroxyphenylpyruvate dioxygenase (EC 1.13.11.27, HPPD, a Fe(II)/α-ketoglutarate dependent oxygenases), is a popular herbicide target. In this work, two pharmacophore models based on common molecular characteristics (HipHop) and receptor-ligand complex (CBP) were generated for virtual screening for HPPD inhibitors. About 1,000,000 molecules containing diketone structure from PubChem were filtered by Lipinski's rules to build a 3D database. Then the database was screened through combining HipHop model, CBP model, ADMET (absorption, distribution, metabolism, excretion and toxicity) prediction and molecular docking. Subsequently, based on the specific binding mode and affinity of HPPD inhibitors, 4 molecules with high -CDOCKER energy, good aqueous solubility and human safety predicative properties values were screened. From the screening results and combined with previous work, three novel HPPD inhibitors were designed and synthesized through fragment splicing and bioisosterism strategies. Compound IV-a exhibited similar inhibition of Arabidopsis thaliana HPPD (AtHPPD) and herbicidal activity as mesotrione. Crop selectivity showed that compound IV-a had better crop safety than mesotrione. Comparing the molecular properties, ADMET and molecular docking studies indicated that compounds IV-a exhibited better properties than mesotrione, which could be further modified as novel HPPD inhibitor herbicides.

Quantitative Structure Activity Relationship Studies and Molecular Dynamics Simulations of 2-(Aryloxyacetyl)cyclohexane-1,3-Diones Derivatives as 4-Hydroxyphenylpyruvate Dioxygenase Inhibitors

4-Hydroxyphenylpyruvate dioxygenase (HPPD) is a significant enzyme in the biosynthesis of plastoquinone and tocopherol. Moreover, it is also a potential target to develop new herbicide. The technology of computer-aided drug design (CADD) is a useful tool in the efficient discovery of new HPPD inhibitors. Forty-three compounds with known activities were used to generate comparative molecular field analysis (CoMFA) and comparative molecular similarity indices analysis (CoMSIA) models based on common framework and molecular docking. The structural contribution to the activity was determined, which provided further information for the design of novel inhibitors. Molecular docking was used to explain the changes in activity caused by the binding mode between ligand and protein. The molecular dynamics (MD) results indicated that the electrostatic energy was the major driving force for ligand–protein interaction and the Phe403 made the greatest contribution to the binding. The present work has provided useful information for the rational design of novel HPPD inhibitors with improved activity.

3D Pharmacophore-Based Virtual Screening and Docking Approaches toward the Discovery of Novel HPPD Inhibitors

p-Hydroxyphenylpyruvate dioxygenase (HPPD) is not only the useful molecular target in treating life-threatening tyrosinemia type I, but also an important target for chemical herbicides. A combined in silico structure-based pharmacophore and molecular docking-based virtual screening were performed to identify novel potential HPPD inhibitors. The complex-based pharmacophore model (CBP) with 0.721 of ROC used for screening compounds showed remarkable ability to retrieve known active ligands from among decoy molecules. The ChemDiv database was screened using CBP-Hypo2 as a 3D query, and the best-fit hits subjected to molecular docking with two methods of LibDock and CDOCKER in Accelrys Discovery Studio 2.5 (DS 2.5) to discern interactions with key residues at the active site of HPPD. Four compounds with top rankings in the HipHop model and well-known binding model were finally chosen as lead compounds with potential inhibitory effects on the active site of target. The results provided powerful insight into the development of novel HPPD inhibitors herbicides using computational techniques.

Synthesis of isoquinoline-1,3(2H,4H)-dione derivatives via cascade reactions of N-alkyl-N-methacryloyl benzamide with aryl aldehydes

A cascade reaction between N-alkyl-N-methacryloylbenzamide and aryl aldehydes was developed to generate isoquinoline-1,3(2H,4H)-dione derivatives.

Rigorous incorporation of tautomers, ionization species, and different binding modes into ligand-based and receptor-based 3D-QSAR methods

Speciation of drug candidates and receptors caused by ionization, tautomerism, and/or covalent hydration complicates ligandand receptor-based predictions of binding affinities by 3-dimensional structure-activity relationships (3D-QSAR). The speciation problem is exacerbated by tendency of tautomers to bind in multiple conformations or orientations (modes) in the same binding site. New forms of the 3D-QSAR correlation equations, capable of capturing this complexity, can be developed using the time hierarchy of all steps that lie behind the monitored biological process - binding, enzyme inhibition or receptor activity. In most cases, reversible interconversions of individual ligand and receptor species can be treated as quickly established equilibria because they are finished in a small fraction of the exposure time that is used to determine biological effects. The speciation equilibria are satisfactorily approximated by invariant fractions of individual ligand and receptor species for buffered experimental or in vivo conditions. For such situations, the observed drug-receptor association constant of a ligand is expressed as the sum of products, for each ligand and receptor species pair, of the association microconstant and the fractions of involved species. For multiple binding modes, each microconstant is expressed as the sum of microconstants of individual modes. This master equation leads to new 3D-QSAR correlation equations integrating the results of all molecular simulations or calculations, which are run for each ligand-receptor species pair separately. The multispecies, multimode 3D-QSAR approach is illustrated by a ligand-based correlation of transthyretin binding of thyroxine analogs and by a receptor-based correlation of inhibition of MK2 by benzothiophenes and pyrrolopyrimidines.

A perspective on the role of quantitative structure-activity and structure-property relationships in herbicide discovery

BACKGROUND

For the last 15 years the agrochemical industry has focused on using genetic modification to put genes that confer resistance to existing commercial herbicides into crop plants rather than on discovering new herbicides with novel modes of action. The widespread appearance of weeds resistant to those herbicides is now causing the industry to revive their herbicide discovery programs.

RESULTS

Elucidation of quantitative structure-activity relationships (QSARs) played a major role in the discovery and development of existing commercial herbicides, but the advent of genetically modified crops has caused published work (at least) in the area to drift from the industrial arena into academic studies. The focus has also turned inward, to refining models for established herbicide targets instead of elucidating new ones.

CONCLUSION

This perspective highlights the importance of QSARs and quantitative structure-property relationships (QSPRs) to herbicide discovery in an historical context and provides some guidance as to how they might profitably be applied going forward.

QSAR modeling of anxiolytic activity taking into account the presence of keto- and enol-tautomers by balance of correlations with ideal slopes

Optimal descriptors calculated with simplified molecular input line entry system (SMILES) have been examined as a tool for prediction of anxiolytic activity. Descriptors calculated with SMILES (a) of keto-isomers; (b) of enol-isomers; and (c) of both keto-isomers together with enol-isomers have been studied. Three approaches have been compared: 1. classic’ training-test’ system 2. balance of correlations and 3. balance of correlations with ideal slopes. The best statistical characteristics for the external validation set took place for optimal descriptors calculated with SMILES of both keto-form and enol-form (i.e., molecular structure was represented in the format: ’sMILES of keto-form. SMILES of enol-form’) by means of balance of correlations with ideal slopes. The predictive potential of this model was checked with three random splits.