Bond Rotation Research Articles

Highly Selective On‐Surface Dehydrogenative Aromatization of n‐Hexyl to Phenyl Substituents

Dehydrogenative aromatization of alkyl substituents represents a powerful approach to aryl‐substituted functional molecules. However, the inertness of alkyl groups and the need for harsh reaction conditions accompanied by low product selectivity hamper its widespread applications. Here, we demonstrate the highly selective on‐surface thermal aromatization of n‐hexyl substituents on polycyclic aromatic hydrocarbons (PAHs) to their phenyl‐substituted analogues under mild conditions. After depositing two representative precursor molecules, n‐hexyl‐substituted hexaphenylbenzene (HPB‐Hex) and bianthryl octacarboxylic tetraimide (BATI‐Hex), onto a pre‐heated Au(111) substrate, dehydroaromatization of the peripheral n‐hexyl groups into phenyl rings occurs, following the planarization of hexaphenylbenzene and bianthryl core. This process involves sequential intramolecular C‐C bond rotations, dehydrogenation, and cyclodehydrogenation reactions, yielding phenyl‐substituted hexabenzocoronene (HBC‐Ph) and bisanthene octacarboxylic tetraimides (BSTI‐Ph). The reaction sequences were monitored using scanning tunneling microscopy (STM) and bond‐resolved non‐contact atomic force microscopy (nc‐AFM), offering structural proof of both intermediates and final products. These experimental techniques were complemented by density functional theory (DFT) simulations, which facilitated the detection of crucial steps in the conversion of n‐hexyl to phenyl groups. Moreover, the effect of alkyl aromatization on the electronic properties of the newly formed aromatic hydrocarbons was elucidated using scanning tunneling spectroscopy (STS).

Highly Selective On‐Surface Dehydrogenative Aromatization of n‐Hexyl to Phenyl Substituents

Dehydrogenative aromatization of alkyl substituents represents a powerful approach to aryl‐substituted functional molecules. However, the inertness of alkyl groups and the need for harsh reaction conditions accompanied by low product selectivity hamper its widespread applications. Here, we demonstrate the highly selective on‐surface thermal aromatization of n‐hexyl substituents on polycyclic aromatic hydrocarbons (PAHs) to their phenyl‐substituted analogues under mild conditions. After depositing two representative precursor molecules, n‐hexyl‐substituted hexaphenylbenzene (HPB‐Hex) and bianthryl octacarboxylic tetraimide (BATI‐Hex), onto a pre‐heated Au(111) substrate, dehydroaromatization of the peripheral n‐hexyl groups into phenyl rings occurs, following the planarization of hexaphenylbenzene and bianthryl core. This process involves sequential intramolecular C‐C bond rotations, dehydrogenation, and cyclodehydrogenation reactions, yielding phenyl‐substituted hexabenzocoronene (HBC‐Ph) and bisanthene octacarboxylic tetraimides (BSTI‐Ph). The reaction sequences were monitored using scanning tunneling microscopy (STM) and bond‐resolved non‐contact atomic force microscopy (nc‐AFM), offering structural proof of both intermediates and final products. These experimental techniques were complemented by density functional theory (DFT) simulations, which facilitated the detection of crucial steps in the conversion of n‐hexyl to phenyl groups. Moreover, the effect of alkyl aromatization on the electronic properties of the newly formed aromatic hydrocarbons was elucidated using scanning tunneling spectroscopy (STS).

Machine learning‐based rational design for efficient discovery of allatostatin analogs as promising lead candidates for novel IGRs

AbstractBACKGROUNDInsect neuropeptide allatostatins (ASTs) play a vital role in regulating insect growth, development, and reproduction, making them potential candidates for new insect growth regulators (IGRs). However, the practical use of natural ASTs in pest management is constrained by their long sequences and high production costs, thus the development of AST analogs with shorter sequences and reduced cost is essential. Traditional methods for designing AST analogs are often time‐consuming and resource‐intensive. This study aims to employ new computational methodologies to understand the structure–activity relationship and efficiently discover potent AST analogs.RESULTSTwo machine learning models, utilizing multiple linear regression and support vector machine, were constructed to reveal the key structural factors that influence the juvenile hormone‐inhibiting activity of AST analogs. These models suggested that a potent AST analog should contain styrene, hydrophilic, and aromatic groups, and rotatable bonds at positions 1, 2, 3, and 4, respectively. Six analogs (A52‐A57) were designed and synthesized, and they exhibited potent juvenile hormone‐inhibiting activity (IC50 < 16 nM). Notably, analog A53 showed the best activity (IC50 = 2.07 nM), surpassing that of most natural Dippu‐ASTs, making it a potential lead candidate for IGRs.CONCLUSIONThese models promote the efficient design, screening, and prioritization of new or untested AST analogs. The study clarifies how a machine learning‐based strategy facilitates the development of AST analogs as novel IGR lead candidates, offering a useful reference for pest management. © 2024 Society of Chemical Industry.

Synthesis and antifungal activity of novel amide derivatives from quinic acid against the sweet potato pathogen Ceratocystis fimbriata.

Ceratocystis fimbriata is a fungal pathogen that infects sweet potato roots, producing enormous economic losses. Cyclic polyhydroxy compound quinic acid is a common metabolite synthesized in plant tissues, including sweet potato tubers, showing weak antifungal properties. Although several O-acylated quinic acid derivatives have been synthesized and found in nature and their antifungal properties have been explored, derivatives based on modification of the carboxylic acid have never been evaluated. In this study, amide derivatives were synthesized via linkage of amines with the carboxylic acid moiety of quinic acid. Derivatives with high dipolar moments and a low number of rotatable bonds showed greater antifungal activities toward C. fimbriata in vitro than quinic and chlorogenic acids. Derivative 5b, which was synthesized by coupling p-aminobenzoic acid (pABA) with quinic acid, had the greatest antifungal activity. 5b showed iron(II)-chelating properties and reduced ergosterol content in C. fimbriata cells, causing irregularities in the fungal cell wall and inhibiting conidia agglutination. Application of 3 mm 5b reduced black rot symptoms in sweet potatoes by 70.1%. Collectively, derivatization of the carboxylic acid from quinic acid was demonstrated to be a suitable strategy to improve the antifungal properties of this compound. This study reveals a new efficient strategy for management of the sweet potato pathogen C. fimbriata. © 2024 Society of Chemical Industry.

Competition & UV254 projection in odorants vs natural organic matter adsorption onto activated carbon surfaces: Is the chemistry right?

Powdered activated carbon (PAC) adsorption remains an indispensable method for addressing odor problems in drinking water. While natural organic matter (NOM) is ubiquitous and competes strongly in deteriorating odorant adsorption capacity, it can also serve as a promising indicator for predicting odorant adsorption through online measurement. However, the impact of PAC surface chemistry on NOM competition and feasibility of prediction across various adsorbents are not well understood. Here, we examined the role of PAC properties (pore structure and surface chemistry) in the competitive adsorption between odorants and NOM components, aligned with the applicability assessment of using NOM optical properties for odorant adsorption projection across various PAC samples. Chemical oxidation and thermal treatment achieved considerable changes in surface functional group composition, alongside minimal changes in pore structure, of two typical PAC products with microporous/mesoporous pore characteristics. The effect of NOM interference on the reduction of odorant adsorption exhibited a similar level regardless of the PACs with different pore structure (average pore size of 1.7 nm vs. 4.2 nm). Surface modification increased the equilibrium adsorption capacity (qe50) of odorants by 15.1% to 146.4% (thermal treatment) or decreased by -81.3% to -34.1% (chemical oxidation), respectively, but minimal changes in odorant-NOM selectivity. For various odorants, hydrophobicity (log D) influenced the adsorption capacity while the structural flexibility (reflected by the rotatable bonds) affected the vulnerability of odorant adsorption to NOM competition. It was found for the first time that four-parameter Richards model (RMSE = 2.6%) is superior to the linear model (RMSE = 12.5%) or logarithmic model (RMSE = 77.6%) to describe the S-shape UV254 projection curves associated with odorant adsorption on PAC. Moreover, the feasibility was confirmed to use UV254 projection curves of pristine PAC fitted with the Richard model to predict the odorant adsorption on surface-modified PAC in two different surface waters (RMSE 9.2% and 7.4%, respectively). This study provides insight into the role of PAC surface chemistry and pore characteristics in odorant adsorption in NOM-containing waters and enhances the feasibility of the NOM surrogate model for odorant monitor and control during PAC adsorption.

Assessing small molecule conformational sampling methods in molecular docking.

Small molecule conformational sampling plays a pivotal role in molecular docking. Recent advancements have led to the emergence of various conformational sampling methods, each employing distinct algorithms. This study investigates the impact of different small molecule conformational sampling methods in molecular docking using UCSF DOCK 3.7. Specifically, six traditional sampling methods (Omega, BCL::Conf, CCDC Conformer Generator, ConfGenX, Conformator, RDKit ETKDGv3) and a deep learning-based model (Torsional Diffusion) for generating conformational ensembles are evaluated. These ensembles are subsequently docked against the Platinum Diverse Dataset, the PoseBusters dataset and the DUDE-Z dataset to assess binding pose reproducibility and screening power. Notably, different sampling methods exhibit varying performance due to their unique preferences, such as dihedral angle sampling ranges on rotatable bonds. Combining complementary methods may lead to further improvements in docking performance.

Bidirectional Molecular Motors by Controlling Threading and Dethreading Pathways of a Linked Rotaxane

AbstractArtificial molecular motors have been presented as models for biological molecular motors. In contrast to the conventional artificial molecular motors that rely on covalent bond rotation, molecular motors with mechanically interlocked molecules (MIMs) have attracted considerable attention owing to their ability to generate significant rotational motion by dynamically shuttling macrocyclic components. The topology of MIM‐type rotational molecular motors is currently limited to catenane structures, which require intricate synthetic procedures that typically produce a low synthetic yield. In this study, we develop a novel class of MIM‐type molecular motors with a rotaxane‐type topology. The switching of the threading/dethreading pathways of the linked rotaxane by protecting/deprotecting the bulky stopper group and changing the solvent polarity enables a net unidirectional rotation of the molecular motor. The threading/dethreading reaction rates were quantitatively evaluated through detailed spectroscopic investigations. Repeated net unidirectional rotation and switching of the direction of rotation were also achieved. Our findings demonstrate that linked rotaxanes can serve as MIM‐type molecular motors with reversible rotational direction controlled by threading/dethreading reactions. These motors hold potential as components of molecular machinery.

How the Spacer Influences the Stability of 2D Perovskites?

Two-dimensionallead halide perovskites (2D HPs) represent as an emerging class of materials given their tunable optoelectronic properties and long-term stability in perovskite solar cells. However, the ever-growing field of optoelectronic devices using 2D HPs requires fundamental understanding of the influence of the spacer on the physiochemical properties and stability of perovskites as well as establish which cation properties are closely related to suppress the halogen ion mobility. This study focuses on investigating the influence of organic spacers with intrinsic properties (e.g., rigidity and flexibility, special groups) and variations of material dimensions on the stability of halogen ions and inorganic frameworks in 2D HPs. It is found that the perovskite structure composed of rigidity molecules owns better stability of halogen ion and inorganic framework than that of flexible molecules. The stability of ions exhibits a negative correlation with the dimensions of perovskite. More importantly, a simple descriptor for measuring the stability of halogen ions in 2D HPs is constructed. By causal discovery algorithms with more physical and chemical significance, the Kappa shape index, number of rotatable bonds, and aromatic carbocycles in organic spacers are identified as causal and important features for the stability of halogen ions in 2D HPs.

Heat diffusion coefficient study of polymers based on interpretable machine learning

Abstract. Polymers hold significant application value across various fields of modern society, with different application scenarios requiring specific thermal diffusivity coefficients. Finding polymer materials with targeted thermal diffusivities is crucial. However, due to the vast variety and complex structures of polymers, constructing a unified structured dataset for machine learning modeling is challenging. Although machine learning has shown great potential in materials science, it has rarely been applied to predict the heat diffusion coefficient of polymers. This paper constructs a dataset for predicting the thermal diffusion coefficient of polymers using a publicly available dataset by transforming the SMILES code of polymers into eight features with practical physical and chemical meanings. Using the Random Forest algorithm, training with 400 of these data and randomly selecting 200 of them for cross-validation, the accuracy of the test set reached 0.9. Additionally, through interpretability analysis, we found that the molecular weight of the polymer monomers, the TPSA (the polar surface area of the molecule), and the NRB (the number of rotatable bonds) are the main features affecting the polymer thermal diffusion coefficient. An increase in the TPSA and the NRB positively contributes to the thermal diffusivity, while an increase in molecular weight negatively contributes. Our study provides a new method for the prediction of polymer thermal diffusivity and creates a new paradigm for the study of polymer thermal diffusivity, promoting further development in this field.

Synthesis of new 1,4-dihydropyridine derivative, anti-cancer, bacterial activity, molecular docking and adsorption, distribution, metabolism and excretion analysis.

Aim: The amination and cyclization method developed a new strategy for designing and assembling new 1,4-dihydropyridine derivatives of compounds 3a-g and 4a-g.Methods & materials: Newly prepared pyridine compounds are more economical, and reduce the reaction time. FT-IR, 1H-NMR, 13C-NMR, mass spectroscopy and elemental analyses elucidated the synthesized derivatives. All the derivatives are subjected to in vitro assay against MCF-7 (breast) and anti-bacterial activity.Results: In the anti-bacterial activity compound 3c is moderately active against Escherichia coli (6.0g/ml), and 4c is extremely active against Lactiplantibacillus plantarum (10.0g/ml) compared with standard Erythromycin. In cytotoxic activity, the compound 3g (lC50 24.5μM) is slightly active against standard doxorubicin. We also present the outcome of a molecular docking study connecting the methoxsalen (Protein Data Bank, PDB ID: 1Z11). Compound 3g shows a higher binding affinity (-7.7Kcal/mol) matching up with doxorubicin(-9.0Kcal/mol). The synthesized analogs were predicted for their adsorption, distribution, metabolism and excretion profiles and pharmacokinetics. The compound 3g has three rotatable bonds (NROB≤10), five hydrogen bond acceptors (HBA≤10) and four hydrogen bond donors (HBD≤5). All the compounds follow Lipinski's rule.Conclusion: Therefore, the compounds 3c and 3g are used as cytotoxic and anti-bacterial drugs in feature.

Assessment of the exchange-hole dipole moment dispersion correction for the energy ranking stage of the seventh crystal structure prediction blind test.

The seventh blind test of crystal structure prediction (CSP) methods substantially increased the level of complexity of the target compounds relative to the previous tests organized by the Cambridge Crystallographic Data Centre. In this work, the performance of density-functional methods is assessed using numerical atomic orbitals and the exchange-hole dipole moment dispersion correction (XDM) for the energy-ranking phase of the seventh blind test. Overall, excellent performance was seen for the two rigid molecules (XXVII, XXVIII) and for the organic salt (XXXIII). However, for the agrochemical (XXXI) and pharmaceutical (XXXII) targets, the experimental polymorphs were ranked fairly high in energy amongst the provided candidate structures and inclusion of thermal free-energy corrections from the lattice vibrations was found to be essential for compound XXXI. Based on these results, it is proposed that the importance of vibrational free-energy corrections increases with the number of rotatable bonds.

Eliminating the Deadwood: A Machine Learning Model for CCS Knowledge-Based Conformational Focusing for Lipids.

Accurate elucidation of gas-phase chemical structures using collision cross section (CCS) values obtained from ion-mobility mass spectrometry benefits from a synergism between experimental and in silico results. We have shown in recent work that for a molecule of modest size with a proscribed conformational space we can successfully capture a conformation(s) that can match experimental CCS values. However, for flexible systems such as fatty acids that have many rotatable bonds and multiple intramolecular London dispersion interactions, it becomes necessary to sample a much greater conformational space. Sampling more conformers, however, accrues significant computational cost downstream in optimization steps involving quantum mechanics. To reduce this computational expense for lipids, we have developed a novel machine learning (ML) model to facilitate conformer filtering according to the estimated gas-phase CCS values. Herein we report that the implementation of our CCS knowledge-based approach for conformational sampling resulted in improved structure prediction agreement with experiment by achieving favorable average CCS prediction errors of ∼2% for lipid systems in both the validation set and the test set. Moreover, most of the gas-phase candidate conformations obtained by using CCS focusing achieved lower energy-minimum geometries than the candidate conformations without focusing. Altogether, the implementation of this ML model into our modeling workflow has proven to be beneficial for both the quality of the results and the turnaround time. Finally, while our approach is limited to lipids, it can be readily extended to other molecules of interest.

Cobalt-catalysed desymmetrization of malononitriles via enantioselective borohydride reduction.

The high nitrogen content and diverse reactivity of malononitrile are widely harnessed to access nitrogen-rich fine chemicals. Although the facile substitutions of malononitrile can give structurally diverse quaternary carbons, their access to enantioenriched molecules, particularly chiral amines that are prevalent in bioactive compounds, remains rare. Here we report a cobalt-catalysed desymmetric reduction of disubstituted malononitriles to give highly functionalized β-quaternary amines. The pair of cobalt salt and sodium borohydride is proposed to generate a cobalt-hydride intermediate and initiate the reduction. Meanwhile, the enantiocontrol of the dinitrile is achieved through a tailored bisoxazoline ligand with two large flanks that create a narrow gap to host the bystanding nitrile and thus restrict the C(ipso)-C(α) bond rotation of the complexed one. Combined with the extensive derivatization possibilities of all substituents on the quaternary carbon, this asymmetric reduction unlocks pathways from malononitrile as a bulk chemical feedstock to intricate, chiral nitrogen-containing molecules.

Comparative QSAR and q-RASAR modeling for aquatic toxicity of organic chemicals to three trout species: O. Clarkii, S. Namaycush, and S. Fontinalis

Oncorhynchus clarkii, Salvelinus fontinalis, and Salvelinus namaycush are vital trout species in North America, crucial for maintaining ecological balance, economic stability, and human health. These species thrive in cold, unpolluted waters and are highly vulnerable to contaminants. Given the rapid proliferation of industrial organic chemicals, traditional in vivo toxicity testing methods are inadequate to ensure timely and comprehensive risk assessments. Therefore, we employed in silico tools, namely Quantitative Structure-Activity Relationship (QSAR) and Quantitative Read-Across Structure-Activity Relationship (q-RASAR), to efficiently predict the aquatic toxicity of chemicals. Utilizing acute median lethal concentration (LC50) data from the US EPA’s ToxValDB, we developed the first-ever species-specific QSAR and q-RASAR models. The q-RASAR models outperformed traditional QSAR models by achieving higher internal and external statistical quality for each species. Key toxicity-determining descriptors included electrotopological state indices, autocorrelation descriptors, and similarity-based RASAR descriptors. For O. clarkii, the presence of chlorine atoms and rotatable bonds significantly influenced toxicity. S. fontinalis toxicity was strongly affected by polarizability, and van der Waals volumes, while S. namaycush showed sensitivity to weak hydrogen bond acceptors and topological complexity. The models predicted the toxicity of 1172 external compounds, identifying the most and least toxic chemicals for each species. This study not only offers the first comprehensive q-RASAR models for predicting trout species-specific toxicity but also provides novel insights into species-specific toxicological modes of action. The results contribute significantly to chemical screening and prioritization in aquatic risk assessments, effectively filling critical data gaps and advancing predictive modeling techniques.

Rapid Interpretation of Protein Backbone Rotation Dynamics Directly from Spin Relaxation Data.

Besides their structure, dynamics is pivotal for protein functions, particularly for intrinsically disordered proteins (IDPs) that do not fold into a fixed 3D structure. However, the detection of protein dynamics is difficult for IDPs and other disordered biomolecules. NMR spin relaxation rates are sensitive to the rapid rotations of chemical bonds, but their interpretation is arduous for IDPs or molecular assemblies with a complex dynamic landscape. Here we demonstrate numerically that the dynamics of a wide range of proteins, from short peptides to partially disordered proteins and peptides in micelles, can be characterized by calculating the total effective correlation times of protein backbone N-H bond rotations, τeff, from experimentally measured transverse 15N spin relaxation rates, R2, using a linear relation. Our results enable the determination of magnetic-field-independent and intuitively understandable parameters describing protein dynamics at different regions of the sequence directly from experiments. A practical advance of the approach is demonstrated by analyzing partially disordered proteins in which rotations of disordered regions occur with timescales of 1-2 ns, independent of their size, suggesting that rotations of disordered and folded regions are uncoupled in these proteins.

A peridynamic compensated critical energy density criterion for mixed-mode fracturing in quasi-brittle materials

This study integrates a compensated critical energy density criterion (CCED) into the ordinary state-based peridynamics framework to enable detailed analysis of mode I, mode II, and mixed-mode fracturing. The proposed bond failure criterion builds upon the advantages of the traditional critical energy density (CED) to enable precise calculation of strain energy density. Additionally, the critical bond rotation criterion (CR) is employed to account for shear bonds that the CED might overlook. Using the peridynamic differential operator (PDDO) format in formulating the entire computational framework enhances both accuracy and numerical stability. The model’s validation is conducted through benchmark examples, including the mode I double cantilever beam test, the mode II compact shear test, and a mixed-mode mechanical fracturing test in pre-notched specimens with central borehole loading. Convergence studies, comparative analyses with finite element method simulations and theoretical solutions thoroughly examine the proposed model’s performance and accuracy.

Nitrogen-doped amorphous monolayer carbon.

Monoatomic-layered carbon materials, such as graphene1 and amorphous monolayer carbon2,3, have stimulated intense fundamental and applied research owing to their unprecedented physical properties and a wide range of promising applications4,5. So far, such materials have mainly been produced by chemical vapour deposition, which typically requires stringent reaction conditions compared to solution-phase synthesis. Herein, we demonstrate the solution preparation of free-standing nitrogen-doped amorphous monolayer carbon with mixed five-, six- and seven-membered (5-6-7-membered) rings through the polymerization of pyrrole within the confined interlayer cavity of a removable layered-double-hydroxide template. Structural characterizations and first-principles calculations suggest that the nitrogen-doped amorphous monolayer carbon was formed by radical polymerization of pyrrole at the α, β and N sites subjected to confinement of the reaction space, which enables bond rearrangements through the Stone-Wales transformation. The spatial confinement inhibits the C-C bond rotation and chain entanglement during polymerization, resulting in an atom-thick continuous amorphous layer with an in-plane π-conjugation electronic structure. The spatially confined radical polymerization using solid templates and ion exchange strategy demonstrates potential as a universal synthesis approach for obtaining two-dimensional covalent networks, as exemplified by the successful synthesis of monolayers of polythiophene and polycarbazole.

Atomistic investigation on the anisotropic elastic and plastic responses of nanotwinned metals

Introducing nanotwins into materials is one of the important strengthening methods to achieve the synergistic improvement of strength and ductility. The anisotropic mechanical behaviors of nanotwinned materials have been widely studied by experimental and computational methods. The dominant deformation mechanisms about dislocation slippages can be effectively switched among three modes, and controlled by twin spacing and the angle between twin boundaries (TBs) orientation and loading direction. Particularly, most of previous researches mainly focused on the deformation mechanisms during the plastic flow stage and researchers paid little attention on the anisotropic characteristics of TBs at the elastic stage which are also essential to manufacture the high-performance materials. Therefore, this study is aiming to systematically investigate the anisotropic effect of TBs both at the elastic and plastic stages within the single crystalline or polycrystalline systems by molecular dynamics (MD) simulations. It is revealed that, when the loading direction is parallel to twin planes, the introduction of TBs in single crystalline models will significantly affect the characteristics of atomic bond rotation and elongation dominated elastic deformation, which can alter the Poisson’s ratio of materials, generate elastic-softening behavior and inhomogeneous elastic deformation. At the plastic flow stage, the deformation mechanism transforms from trans-twin dislocation slippage into the coexistence of Hard Mode I and threading dislocation slippage (Hard Mode II) when the loading direction changes from parallel to perpendicular direction with respect to TBs. Moreover, the dislocation segments at the conjunction of trans-twin dislocation play a momentous role in enhancing material strength. The results for polycrystalline models are consistent with that of single crystalline ones. These findings are expected to be beneficial for the development of high-performance nanostructured materials for structural and functional applications by strain engineering and defect regulation.

Structure-Based Design and Development of Phosphine Oxides as a Novel Chemotype for Antibiotics that Dysregulate Bacterial ClpP Proteases.

A series of arylsulfones and heteroarylsulfones have previously been demonstrated to dysregulate the conserved bacterial ClpP protease, causing the unspecific degradation of essential cellular housekeeping proteins and ultimately resulting in cell death. A cocrystal structure of a 2-β-sulfonylamide analog, ACP1-06, with Escherichia coli ClpP showed that its 2-pyridyl sulfonyl substituent adopts two orientations in the binding site related through a sulfone bond rotation. From this, a new bis-aryl phosphine oxide scaffold, designated as ACP6, was designed based on a "conformation merging" approach of the dual orientation of the ACP1-06 sulfone. One analog, ACP6-12, exhibited over a 10-fold increase in activity over the parent ACP1-06 compound, and a cocrystal X-ray structure with ClpP confirmed its predicted binding conformation. This allowed for a comparative analysis of how different ligand classes bind to the hydrophobic binding site. The study highlights the successful application of structure-based rational design of novel phosphine oxide-based antibiotics.

First report on regression-based QSAR addressing pesticide dissipation half-life in plants: A step towards sustainable public health

The excessive use of pesticides (an important group of chemicals) in the agricultural as well as public sectors raises a health concern. Pesticides affect humans and other living organisms via the food chain. Therefore, it is very necessary to calculate the dissipation half-life of pesticides in plants. Experimental prediction of pesticide dissipation half-lives requires complex environmental conditions, high cost, and a long time. Thus, in-silico half-life predictions are suitable and the best alternative. Herein, a total of six PLS (partial least squares) models namely, M1 (overall), M2 (fruit), M3 (plant interior), M4 (leaf), M5 (plant surface), and M6 (whole plant) alongside two MLR (multiple linear regression) models i.e. M7 (fruit surface) and model M8 (straw) were generated using dissipation half-lives (log10(T1/2)) of pesticides in plants and their different parts. Models were constructed in strict accordance with the guidelines outlined by the Organization for Economic Co-operation and Development (OECD) and extensively validated using globally accepted validation metrics (determination coefficient (R2) = 0.610–0.795, leave-one-out (LOO) cross-validated correlation coefficient (Q2LOO) = 0.520–0.660, MAE-FITTED TRAIN (mean absolute error fitted train) = 0.119–0.148, MAE-LOOTRAIN = 0.132–0.177, predictive R2 or Q2F1 = 0.538–0.567, Q2F2 = 0.500–0.565, MAETEST = 0.122–0.232), confirming their accuracy, reliability, predictivity, and robustness. Lipophilicity, the presence of a cyclomatic ring, suphur, aromatic amine fragments, and chlorine atom fragments are responsible (+ve contribution) for high dissipation half-lives of pesticides in plants. In contrast, hydrophilicity, pyrazine fragments, and rotatable bonds reduce (−ve negative contribution) the dissipation half-lives of pesticides in plants. To address the real-world applicability, the models were employed to screen the PPDB (Pesticide Properties Database) database, which revealed the top 10 pesticides with the highest log(T1/2) in the whole plant and respective parts of the plant body. The present work will aid in developing safer and novel pesticides, regulatory risk assessment, various risk assessments for the sustenance of public health, screening of databases, and data-gap filling.