HerbiPAD: a free web platform to comprehensively analyze constitutive property and herbicide-likeness to estimate chemical bioavailability

Computer-Aided Design and Pharmacophore-Based Screening of a Diverse Combinatorial Library of Phytoselective Aryloxyacetic Acid Derivatives as HPPD Inhibitors

We investigated the inhibitory potency of aryloxyacetic acid derivatives (AADs) on 4-hydroxyphenylpyruvate dioxygenase (HPPD), a crucial enzyme target for HPPD herbicide development. Developing a wide-ranging approach combining reported structure-activity relationships (SARs with the observed inhibitory potencies of the enzyme Kiexp), our simulations for molecular mechanics Poisson-Boltzmann (MM-PB) complexation quantitative SAR (QSAR) (computed relative Gibbs free energies of the HPPD-AADx complex formation ΔΔGcom), and three-dimensional (3D)-QSAR pharmacophore (PH4) models for screening the chemical subspace of aryloxyacetic acid derivatives (a virtual library of AADs, VL), we come out with a handful of novel AADs with promising predictive HPPD inhibitory potency and confirmed molecular dynamics (MD) conformational stability. The 3D-QSAR model revealed a correlation (pKiexp = a × ΔΔGcom + b) between computed data and observed inhibition ones: pKiexp = -0.0544 × ΔΔGcom + 6.93, R2 = 0.87 for a training set (TS) of 30 AAD (AAD1-30). The subsequent 3D-QSAR pharmacophore (PH4) of HPPD inhibition by AADs confirmed the correlation (pKiexp = 0.863 × pKipre + 7.92, R2 = 0.86) between PH4-predicted pKipre and the observed ones pKiexp. The structural information derived from these models suggested suitable substituents for building a virtual library (VL) of AAD analogues representing a chemical subspace of 79,500 compounds to be PH4-screened in search of more potent inhibitors; the best predicted Kipre of them reached 40 pM. Finally, the good stability of the AtHPPD-AADx complex and the flexibility of the active conformation of the inhibitor for selected top-ranked AAD analogues were checked with the help of molecular dynamics (MD, 200 ns runs). This computational study proposed a set of new predicted potent inhibitors with herbicidal effects.

Seaweed-based PPO inhibitors as a new frontier in biological weed control for sorghum cultivation: from ocean to field

Weed management, particularly of broad-leaf species, poses a significant challenge in sorghum production, potentially causing up to 70.9% yield loss. Conventionally, herbicides are the site-specific weed inhibitors often used to suppress the growth of these weeds. Protoporphyrinogen oxidase (PPO) is an important target for developing novel herbicides with remarkable selectivity against these broad-leaved weeds. In particular, saflufenacil is a selective PPO herbicide that can provide temporary relief. However, their prolonged use could lead to various environmental constraints and the evolution of resistant weeds. Recently, bioherbicides are emerging as an alternative to commercial herbicides with safer environmental profiles and combat resistance. On that note, this study aimed to identify novel and safer bioherbicides derived from seaweeds to outright bans on the use of most synthetic herbicides and address herbicide resistance. High-end computational screening strategies including molecular docking, binding free energy, in-house developed ML-based VS, and herbicide-likeness were used to pinpoint the lead molecule against the wild and mutant (∆G210) type AtPPO. Finally, molecular dynamics simulations were employed to validate the binding kinetics of the hit compound. Indeed, the bromophenol derivative BL003, isolated from brown algae, emerged as the hit compound from our analysis. Remarkably, BL003 demonstrated superior binding affinity and stability compared to saflufenacil against both wild-type AtPPO and the mutant AtPPOΔG210. Thus, our findings could open new avenues for developing sustainable, seaweed-derived herbicides for sorghum cultivation. This research could contribute to the ongoing efforts to create environmentally friendly weed management solutions in agriculture.

Assessment of inhibitory potentiality of natural compounds against worrisome rice blast fungus

Rice blast, a severe fungal disease, is a substantial threat to global food security, particularly in rice-oriented areas. The Magnaporthe oryzae fungus is increasingly resistant and fast developing in nature. However, chemical fungicides are not only detrimental to the environment but eventually also lose their efficiency. To Tackle this issue, we used an in silico based strategy to identify plant metabolites as bio-fungicides to combat rice blast. Therefore, we screened a total of 56 antifungal natural compounds for the ability to inhibit fungal development through the targeted inhibition of essential proteins in the rice blast pathogen. Molecular docking analysis identified curcumin, myricetin, sterigmatocystin, and versicolorin B as promising candidates with superior binding affinities compared to conventional fungicides like strobilurin, azoxystrobin, and tricyclazole. Notably, myricetin showed the docking score for the SD protein of -233.20, whereas versicolorin B demonstrated the highest binding affinity for the SD protein of -234.23. Among the control fungicides, azoxystrobin displayed the lowest docking score of -177.53. The docked complexes were found to be stable based on molecular dynamics simulations results; the binding free energies of SD-Versicolorin B (-156.018 ± 24.881 kJ/mol) and SD-Myricetin (-137.526 ± 19.977 kJ/mol) complexes were also found to be favorable. Taking everything considered, these naturally occurring substances showed strong fungicidal effects against rice blast causative agent while remaining non-toxic, providing encouraging substitutes for traditional fungicides. In conclusion, these non-toxic natural compounds exhibited strong fungicidal action against rice blast, suggesting promising alternatives to conventional fungicides.

Integrating classic AI and agriculture: A novel model for predicting insecticide-likeness to enhance efficiency in insecticide development

The integration of artificial intelligence (AI) into smart agriculture boosts production and management efficiency, facilitating sustainable agricultural development. In intensive agricultural management, adopting eco-friendly and effective pesticides is crucial to promote green agricultural practices. However, exploring new insecticides species is a difficult and time-consuming task that involves significant risks. Enhancing compound druggability in the lead discovery phase could considerably shorten the discovery cycle, accelerating insecticides research and development. The Insecticide Activity Prediction (IAPred) model, a novel classic artificial intelligence-based method for evaluating the potential insecticidal activity of unknown functional compounds, is introduced in this study. The IAPred model utilized 27 insecticide-likeness features from PaDEL descriptors and employed an ensemble of Support Vector Machine (SVM) and Random Forest (RF) algorithms using the hard-vote mechanism, achieving an accuracy rate of 86 %. Notably, the IAPred model outperforms current models by accurately predicting the efficacy of novel insecticides such as nicofluprole, overcoming the limitations inherent in existing insecticide structures. Our research presents a practical approach for discovering and optimizing novel insecticide lead compounds quickly and efficiently.

ChemFREE: a one-stop comprehensive platform for ecological and environmental risk evaluation of chemicals in one health world

Addressing health and safety crises stemming from various environmental and ecological issues is a core focus of One Health (OH), which aims to balance and optimize the health of humans, animals, and the environment. While many chemicals contribute significantly to our quality of life when properly used, others pose environmental and ecological health risks. Recently, assessing the ecological and environmental risks associated with chemicals has gained increasing significance in the OH world. In silico models may address time-consuming and costly challenges, and fill gaps in situations where no experimental data is available. However, despite their significant contributions, these assessment models are not web-integrated, leading to user inconvenience. In this study, we developed a one-stop comprehensive web platform for freely evaluating the eco-environmental risk of chemicals, named ChemFREE (Chemical Formula Risk Evaluation of Eco-environment, available in http://chemfree.agroda.cn/chemfree/). Inputting SMILES string of chemicals, users will obtain the assessment outputs of ecological and environmental risk, etc. A performance evaluation of 2935 external chemicals revealed that most classification models achieved an accuracy rate above 0.816. Additionally, the $Q_{F1}^2$ metric for regression models ranges from 0.618 to 0.898. Therefore, it will facilitate the eco-environmental risk evaluation of chemicals in the OH world.

Cheminformatics and artificial intelligence for accelerating agrochemical discovery

The global cost-benefit analysis of pesticide use during the last 30 years has been characterized by a significant increase during the period from 1990 to 2007 followed by a decline. This observation can be attributed to several factors including, but not limited to, pest resistance, lack of novelty with respect to modes of action or classes of chemistry, and regulatory action. Due to current and projected increases of the global population, it is evident that the demand for food, and consequently, the usage of pesticides to improve yields will increase. Addressing these challenges and needs while promoting new crop protection agents through an increasingly stringent regulatory landscape requires the development and integration of infrastructures for innovative, cost- and time-effective discovery and development of novel and sustainable molecules. Significant advances in artificial intelligence (AI) and cheminformatics over the last two decades have improved the decision-making power of research scientists in the discovery of bioactive molecules. AI- and cheminformatics-driven molecule discovery offers the opportunity of moving experiments from the greenhouse to a virtual environment where thousands to billions of molecules can be investigated at a rapid pace, providing unbiased hypothesis for lead generation, optimization, and effective suggestions for compound synthesis and testing. To date, this is illustrated to a far lesser extent in the publicly available agrochemical research literature compared to drug discovery. In this review, we provide an overview of the crop protection discovery pipeline and how traditional, cheminformatics, and AI technologies can help to address the needs and challenges of agrochemical discovery towards rapidly developing novel and more sustainable products.

Protein Kinases as Potential Targets Contribute to the Development of Agrochemicals

Using agrochemicals against pest insects, fungi, and weeds plays a major part in maintaining and improving crop yields, which helps to solve the issue of food security. Due to the limited targets and resistance of agrochemicals, protein kinases are regarded as attractive potential targets to develop new agrochemicals. Recently, a lot of investigations have shown the extension of agrochemicals by targeting protein kinases, implying an increasing concern for this kind of method. However, few people have summarized and discussed the targetability of protein kinases contributing to the development of agrochemicals. In this work, we introduce the research on protein kinases as potential targets used in crop protection and discuss the prospects of protein kinases in the field of agrochemical development. This study may not only provide guidance for the contribution of protein kinases to the development of agrochemicals but also help nonprofessionals such as students learn and understand the role of protein kinases quickly.

Modern Approaches for the Development of New Herbicides Based on Natural Compounds

Weeds are a permanent component of anthropogenic ecosystems. They require strict control to avoid the accumulation of their long-lasting seeds in the soil. With high crop infestation, many elements of crop production technologies (fertilization, productive varieties, growth stimulators, etc.) turn out to be practically meaningless due to high yield losses. Intensive use of chemical herbicides (CHs) has led to undesirable consequences: contamination of soil and wastewater, accumulation of their residues in the crop, and the emergence of CH-resistant populations of weeds. In this regard, the development of environmentally friendly CHs with new mechanisms of action is relevant. The natural phytotoxins of plant or microbial origin may be explored directly in herbicidal formulations (biorational CHs) or indirectly as scaffolds for nature-derived CHs. This review considers (1) the main current trends in the development of CHs that may be important for the enhancement of biorational herbicides; (2) the advances in the development and practical application of natural compounds for weed control; (3) the use of phytotoxins as prototypes of synthetic herbicides. Some modern approaches, such as computational methods of virtual screening and design of herbicidal molecules, development of modern formulations, and determination of molecular targets, are stressed as crucial to make the exploration of natural compounds more effective.

Computer-Assisted Proposition of Promising Aryloxyacetic Acid Derivatives as HPPD Inhibitors

A series of aryloxyacetic acid derivatives have demonstrated promising herbicidal performance by inhibition of the hydroxyphenylpyruvate deoxygenase (HPPD) enzyme. We hereby applied quantitative structure-activity relationship (QSAR) and docking strategies to model and chemically understand the bioactivities of these compounds and subsequently propose unprecedented analogues aiming at improving the herbicidal and environmental properties. Bulky halogens at the 2-, 3-, 4-, and 6-positions of an aromatic ring, CF₃ in 4-position, and the 2-NO₂ group in a phenyl ring appear to favor the HPPD inhibition. At the same time, Me and OMe substituents contribute to decreasing the pK_i values. Accordingly, a few compounds were proposed and the candidate with 2,4,6-triBr substituents demonstrated an estimated pK_i similar to those of the best library compounds. This finding was corroborated by the docking scores of the ligand-enzyme interactions. In addition, the high calculated lipophilicity of some proposed agrochemicals suggests that they should have low soil mobility and, therefore, are not prone to easily leach out and reach groundwater, despite causing other ecological issues.

Pesticide Informatics Platform (PIP): An International Platform for Pesticide Discovery, Residue, and Risk Evaluation

Pesticides are widely used agrochemicals for crop protection. The need for novel pesticides becomes urgent as a result of the emergence of resistance and environmental toxicity. Pesticide informatics has been applied in different phase processes of pesticide target identification, active ingredient design, and impact evaluation. However, these valuable resources are scattered over the literature and web, limiting their availability. Here, we summarize and connect research on pesticide informatics resources. A pesticide informatics platform (PIP) was constructed to share these tools. We finally discuss the future direction of pesticide informatics, including pesticide contamination. We expect to share the pesticide informatics approaches and stimulate further research.

In Silico Resources of Drug-Likeness as a Mirror: What Are We Lacking in Pesticide-Likeness?

Unfavorable bioavailability is an important aspect underlying the failure of drug candidates. Computational approaches for evaluating drug-likeness can minimize these risks. Over the past decades, computational approaches for evaluating drug-likeness have sped up the process of drug development and were also quickly derived to pesticide-likeness. As a result of many critical differences between drugs and pesticides, many kinds of methods for drug-likeness cannot be used for pesticide-likeness. Therefore, it is crucial to comprehensively compare and analyze the differences between drug-likeness and pesticide-likeness, which may provide a basis for solving the problems encountered during the evaluation of pesticide-likeness. Here, we systematically collected the recent advances of drug-likeness and pesticide-likeness and compared their characteristics. We also evaluated the current lack of studies on pesticide-likeness, the molecular descriptors and parameters adopted, the pesticide-likeness model on pesticide target organisms, and comprehensive analysis tools. This work may guide researchers to use appropriate methods for developing pesticide-likeness models. It may also aid non-specialists to understand some important concepts in drug-likeness and pesticide-likeness.

Synthesis and Herbicidal Activity of Triketone-Aminopyridines as Potent p-Hydroxyphenylpyruvate Dioxygenase Inhibitors

Exploring novel p-hydroxyphenylpyruvate dioxygenase (EC 1.13.11.27, HPPD) inhibitors has become one of the most promising research directions in herbicide innovation. On the basis of our tremendous interest in exploiting more powerful HPPD inhibitors, we designed a family of benzyl-containing triketone-aminopyridines via a structure-based drug design (SBDD) strategy and then synthesized them. Among these prepared derivatives, the best active 3-hydroxy-2-(3,5,6-trichloro-4-((4-isopropylbenzyl)amino)picolinoyl)cyclohex-2-en-1-one (23, IC₅₀ = 0.047 μM) exhibited a 5.8-fold enhancement in inhibiting Arabidopsis thaliana (At) HPPD activity over that of commercial mesotrione (IC₅₀ = 0.273 μM). The predicted docking models and calculated energy contributions of the key residues for small molecules suggested that an additional π-π stacking interaction with Phe-392 and hydrophobic contacts with Met-335 and Pro-384 were detected in AtHPPD upon the binding of the best active compound 23 compared with that of the reference mesotrione. Such a molecular mechanism and the resulting binding affinities coincide with the proposed design scheme and experimental values. It is noteworthy that inhibitors 16 (3-hydroxy-2-(3,5,6-trichloro-4-((4-chlorobenzyl)amino)picolinoyl)cyclohex-2-en-1-one), 22 (3-hydroxy-2-(3,5,6-trichloro-4-((4-methylbenzyl)amino)picolinoyl)cyclohex-2-en-1-one), and 23 displayed excellent greenhouse herbicidal effects at 150 g of active ingredient (ai)/ha after postemergence treatment. Furthermore, compound 16 showed superior weed-controlling efficacy against Setaria viridis (S. viridis) versus that of the positive control mesotrione at multiple test dosages (120, 60, and 30 g ai/ha). These findings imply that compound 16, as a novel lead of HPPD inhibitors, possesses great potential for application in specifically combating the malignant weed S. viridis.

Improved herbicide discovery using physico-chemical rules refined by antimalarial library screening

Herbicides have physico-chemical properties not unlike orally-delivered human drugs, but are known to diverge in their limits for proton donors, partition coefficients and molecular weight. To further refine rules specific for herbicides, we exploited the close evolutionary relationship between Plasmodium falciparum and plants by screening the entire Malaria Box, a chemical library of novel chemical scaffolds with activity against the blood stage of P. falciparum. Initial screening against Arabidopsis thaliana on agar media and subsequently on soil demonstrated the crucial nature of log P and formal charge are to active molecules. Using this information, a weighted scoring system was applied to a large chemical library of liver-stage effective antimalarial leads, and of the six top-scoring compounds, one had potency comparable to that of commercial herbicides. This novel compound, MMV1206386, has no close structural analogues among commercial herbicides. Physiological profiling suggested that MMV1206386 has a new mode of action and overall demonstrates how weighted rules can help during herbicide discovery programs.