MolGpka: A Web Server for Small Molecule pKa Prediction Using a Graph-Convolutional Neural Network

Spectral-fluorescent study of substituted trimethine cyanine dyes in solutions and in complexes with DNA. Effects of aggregation, moderate heating, and decreasing pH

Trimethine cyanine dyes are widely used as probes for the detection, study and quantification of biomolecules. In particular, cationic trimethine cyanines noncovalently interact with DNA with growing fluorescence. However, their use is often limited by the tendency to self-association - to the formation of aggregates. Disubstituted trimethine cyanines with hydrophobic substituents are especially prone to aggregation. In this work, we studied the interaction of a number of substituted trimethine cyanines with DNA (in aqueous buffer solutions) and showed that their aggregation strongly interfered with their use as fluorescent probes for DNA. To eliminate this drawback, preliminary heating of dye solutions with DNA to 60-70 °C was used, followed by cooling to room temperature. Compared to the experiments without heating, an increase in the dye fluorescence intensity was observed due to the partial thermal decomposition of the aggregates and the interaction of the resulting monomers with DNA. To decompose aggregates, another method was also used - protonation of the dyes with amino substituents in buffer solutions with pH 5.0, which also led to growing the dye fluorescence intensity in the presence of DNA. Complexes of the dyes with DNA were modeled using molecular docking. Effective binding constants of the dyes to DNA and detection limits when using the dyes as probes for DNA (LOD and LOQ) were determined. It is shown that dye 3 with heating in neutral buffer and dye 1 in acidic buffer may be recommended as sensitive probes for DNA. It is concluded that the method of preliminary heating may be applied to dyes prone to aggregation, for improving their properties as biomolecular probes. Another possible means to reduce the interfering effects of dye aggregates is to use easily protonated dyes (with amino substituents) in slightly acidic media.

ChemXTree: A Feature-Enhanced Graph Neural Network-Neural Decision Tree Framework for ADMET Prediction

The rapid progression of machine learning, especially deep learning (DL), has catalyzed a new era in drug discovery, introducing innovative approaches for predicting molecular properties. Despite the many methods available for feature representation, efficiently utilizing rich, high-dimensional information remains a significant challenge. Our work introduces ChemXTree, a novel graph-based model that integrates a Gate Modulation Feature Unit (GMFU) and neural decision tree (NDT) in the output layer to address this challenge. Extensive evaluations on benchmark data sets, including MoleculeNet and eight additional drug databases, have demonstrated ChemXTree's superior performance, surpassing or matching the current state-of-the-art models. Visualization techniques clearly demonstrate that ChemXTree significantly improves the separation between substrates and nonsubstrates in the latent space. In summary, ChemXTree demonstrates a promising approach for integrating advanced feature extraction with neural decision trees, offering significant improvements in predictive accuracy for drug discovery tasks and opening new avenues for optimizing molecular properties.

Slow release of a synthetic auxin induces formation of adventitious roots in recalcitrant woody plants

Clonal propagation of plants by induction of adventitious roots (ARs) from stem cuttings is a requisite step in breeding programs. A major barrier exists for propagating valuable plants that naturally have low capacity to form ARs. Due to the central role of auxin in organogenesis, indole-3-butyric acid is often used as part of commercial rooting mixtures, yet many recalcitrant plants do not form ARs in response to this treatment. Here we describe the synthesis and screening of a focused library of synthetic auxin conjugates in Eucalyptus grandis cuttings and identify 4-chlorophenoxyacetic acid-L-tryptophan-OMe as a competent enhancer of adventitious rooting in a number of recalcitrant woody plants, including apple and argan. Comprehensive metabolic and functional analyses reveal that this activity is engendered by prolonged auxin signaling due to initial fast uptake and slow release and clearance of the free auxin 4-chlorophenoxyacetic acid. This work highlights the utility of a slow-release strategy for bioactive compounds for more effective plant growth regulation.

Modular Point-of-Need Tropane Alkaloid Detection at Regulatory Levels: Combining Solid-Liquid Extraction from Buckwheat with a Paper-Immobilized Liquid-Phase Microextraction and Immuno-Detection in Interconnectable 3D-Printed Devices

Contamination with tropane alkaloids in cereals is expected to increase globally. However, current identification tools (e.g., liquid chromatography-mass spectrometry) for tropane alkaloids are time-consuming and expensive. Furthermore, their miniaturized alternatives lack sensitivity and robustness. Therefore, there is a pressing need for inexpensive and effective screening methods. Here, an on-site applicable modular workflow for tropane alkaloid detection in buckwheat is presented. The modular workflow combines paper microfluidics and interconnectable 3D-printed sample preparation tools and was evaluated for different tropane alkaloids, including atropine and scopolamine. Furthermore, integration with an indirect competitive lateral flow immunoassay (icLFIA) for atropine detection at relevant levels was demonstrated. In the modular workflow, to minimize matrix coextraction, tropane alkaloids were extracted from the milled buckwheat cereals by a mixture of alkaline aqueous and immiscible organic solvents (extraction recoveries: 66-79%). The tropane alkaloids were subsequently concentrated with a newly developed paper-immobilized liquid-phase microextraction (PI-LPME, extraction recoveries: 34-60%, concentration factor to immobilized solution in paper: 60-108×). After the PI-LPME, with an integrated 3D-printed setup, the tropane alkaloids were directly eluted (elution recoveries: 83-93%) and detected with the icLFIA. Digital read-out of the icLFIA, by employing a hand-held reader, enabled semiquantification of atropine (IC50 = 0.56 ng mL-1 in standard solutions). The modular workflow was validated by analyzing 24 blank and spiked buckwheat cereal samples with 5 and 10 μg kg-1 atropine. A cutoff value was established with an estimated false negative rate of 1% and estimated false positive rate of 0.68%. Therefore, the modular workflow can aid in fast, inexpensive, and on-site atropine detection by nonexperts, and when integrated with a scopolamine-specific icLFIA expanded toward scopolamine detection. Moreover, the developed sample extraction and concentration method (PI-LPME) is suitable for the analysis of many other compounds with pH-dependent polarity.

Attentive graph neural network models for the prediction of blood brain barrier permeability

The blood brain barrier's (BBB) unique endothelial cells and tight junctions selectively regulate passage of molecules to the central nervous system (CNS) to prevent pathogen entry and maintain neural homeostasis. Various neurological conditions and neurodegenerative diseases benefit from small molecules capable of BBB penetration (BBBP) to elicit a therapeutic effect. Predicting BBBP often involves in silico assessment of molecular properties such as lipophilicity (log P ) and polar surface area (PSA) using the CNS multiparameter optimization (MPO) method. This study curated an open-source dataset to benchmark rigorously machine learning (ML) and neural network (NN) models with each other and with MPO for predicting BBBP. Our analysis demonstrated that AI models, especially attentive NNs using stereochemical features, significantly outperform MPO in predicting BBBP. An attentive graph neural network (GNN), we refer to as CANDID-CNS™, achieved a 0.23-0.26 higher AUROC score than MPO on full test sets, and a 0.17-0.19 higher score on stereoisomers filtered subsets. Regarding stereoisomers that differ in BBBP, which MPO cannot distinguish, attentive GNNs correctly classify these with AUROC and MCC metrics comparable to or better than MPO's AUROC and MCC on less difficult test molecules. These findings suggest that integrating attentive GNN models into pharmaceutical drug discovery processes can substantially improve prediction rates, and thereby reduce the timeline, cost, and increase probability of success of designing brain penetrant therapeutics for the treatment of a wide variety of neurological and neurodegenerative diseases.

Protocol for protein modification using oxalyl thioester-mediated chemoselective ligation

The development of fast ligation chemistries for the site-specific modification of proteins has become a major focus in chemical biology. We describe steps for preparing an oxalyl thioester precursor in the form of an N-oxalyl perhydro-1,2,5-dithiazepine handle, i.e., the oxoSEA group, and incorporating it into a peptide modifier using solid phase peptide synthesis. We then detail procedures for its application for the modification of an N-terminal Cys-containing B1 domain of the streptococcal G protein using the native chemical ligation. For complete details on the use and execution of this protocol, please refer to Snella et al.1.

Distinct binding hotspots for natural and synthetic agonists of FFA4 from in silico approaches

FFA4 has gained interest in recent years since its deorphanization in 2005 and the characterization of the Free Fatty Acids receptors family for their therapeutic potential in metabolic disorders. The expression of FFA4 (also known as GPR120) in numerous organs throughout the human body makes this receptor a highly potent target, particularly in fat sensing and diet preference. This offers an attractive approach to tackle obesity and related metabolic diseases. Recent cryo-EM structures of the receptor have provided valuable information for a potential active state although the previous studies of FFA4 presented diverging information. We performed molecular docking and molecular dynamics simulations of four agonist ligands, TUG-891, Linoleic acid, α-Linolenic acid, and Oleic acid, based on a homology model. Our simulations, which accumulated a total of 2 μs of simulation, highlighted two binding hotspots at Arg992.64 and Lys293 (ECL3). The results indicate that the residues are located in separate areas of the binding pocket and interact with various types of ligands, implying different potential active states of FFA4 and a highly adaptable binding intra-receptor pocket. This article proposes additional structural characteristics and mechanisms for agonist binding that complement the experimental structures.

Bridging Machine Learning and Thermodynamics for Accurate pK a Prediction

Integrating scientific principles into machine learning models to enhance their predictive performance and generalizability is a central challenge in the development of AI for Science. Herein, we introduce Uni-pK a, a novel framework that successfully incorporates thermodynamic principles into machine learning modeling, achieving high-precision predictions of acid dissociation constants (pK a), a crucial task in the rational design of drugs and catalysts, as well as a modeling challenge in computational physical chemistry for small organic molecules. Uni-pK a utilizes a comprehensive free energy model to represent molecular protonation equilibria accurately. It features a structure enumerator that reconstructs molecular configurations from pK a data, coupled with a neural network that functions as a free energy predictor, ensuring high-throughput, data-driven prediction while preserving thermodynamic consistency. Employing a pretraining-finetuning strategy with both predicted and experimental pK a data, Uni-pK a not only achieves state-of-the-art accuracy in chemoinformatics but also shows comparable precision to quantum mechanics-based methods.

Data-driven discovery of potent small molecule ice recrystallisation inhibitors

Controlling the formation and growth of ice is essential to successfully cryopreserve cells, tissues and biologics. Current efforts to identify materials capable of modulating ice growth are guided by iterative changes and human intuition, with a major focus on proteins and polymers. With limited data, the discovery pipeline is constrained by a poor understanding of the mechanisms and the underlying structure-activity relationships. In this work, this barrier is overcome by constructing machine learning models capable of predicting the ice recrystallisation inhibition activity of small molecules. We generate a new dataset via experimental measurements of ice growth, then harness predictive models combining state-of-the-art descriptors with domain-specific features derived from molecular simulations. The models accurately identify potent small molecule ice recrystallisation inhibitors within a commercial compound library. Identified hits can also mitigate cellular damage during transient warming events in cryopreserved red blood cells, demonstrating how data-driven approaches can be used to discover innovative cryoprotectants and enable next-generation cryopreservation solutions for the cold chain.

Adsorption of Staphylococcus aureus biofilm associated compounds on silica probed with molecular dynamics simulations

Staphylococcus aureus (S. aureus) is a potentially pathogenic bacterium that commonly colonizes surfaces through the formation of biofilms. Silica glass is a common material in the built environment, especially in laboratory and medical spaces. The chemical and physical mechanisms by which S. aureus initially adheres to surfaces are unclear. In this study, the adsorption of several S. aureus biofilm associated compounds on silica is probed using molecular dynamics simulations. Model compounds containing a phosphorylated backbone, N-acetylglucosamine (GlcNAc), or D-alanine (D-Ala) were simulated across a range of pH. GlcNAc adsorption is unfavorable and insensitive to pH. D-Ala adsorption is unfavorable across the range of tested pH. Phosphorylated backbone adsorption is unfavorable at low pH but favorable at high pH. Adsorbate titration and solution salt concentration were probed to establish effects of molecular charge and charge screening. Hydrogen bonding between compounds and the silica surface is a key factor for stronger adsorption. The findings of this study are important for the rational design of improved silica surfaces through chemical functionalization or through the application of optimal chemical disinfectants that discourage the initial stages of biofilm growth.

Reconstruction of the unbinding pathways of new inhibitors of the SARS-CoV-2 papain-like protease using molecular dynamics simulation

Developing novel antiviral drugs against the SARS-CoV-2 virus and COVID-19 disease is imperative as the vaccines may not offer absolute protection. PLpro plays a crucial role in the viral life cycle, making it an attractive target for drug development. Several PLpro inhibitors have been developed, and their 3D structures in complex with PLpro are available. In this work, we employed Supervised Molecular Dynamics (SuMD), a specific Unbiased Molecular Dynamics (UMD) method, to investigate unbinding pathways of the novel inhibitors of PLpro (PDB IDs: 7LBR, 7RZC, 7SDR and 7E35) and GRL0617 (PDB ID: 7JRN) as a reference. We conducted three simulations for each ligand and achieved unbinding events in the nanosecond timescale in all simulations. We found that unbinding events are commonly affected by altering the conformation of the BL2 loop, which is caused by the natural fluctuations of the loop that are required to trap the substrate and throw out the product. BL2 loop is crucial for keeping the ligand and unbinding and acts as a double-edged sword. Any inhibitor designed to be effective must prevent the loop's natural fluctuations. We perceived that increasing ligands interactions with the binding pocket interior and the BL2 loop will help prevent natural fluctuation of the BL2 loop, Although the interactions with the binding pocket's inner side are more critical than the BL2 loop. These findings may be helpful in developing more potent inhibitors against SARS-CoV-2.Communicated by Ramaswamy H. Sarma.

Structural and Electronic Properties of Novel Azothiophene Dyes: A Multilevel Study Incorporating Explicit Solvation Effects

Among azobenzene derivatives, azothiophenes represent a relatively recent family of compounds that exhibit similar characteristics as dyes and photoreactive systems. Their technological applications are extensive thanks to the additional design flexibility conferred by the heteroaromatic ring. In this study, we present a comprehensive investigation of the structural and electronic properties of novel dyes derived from 3-thiophenamine, utilizing a multilevel approach. We thoroughly examined the potential energy surfaces of the E and Z isomers for three molecules, each bearing different substituents on the phenyl ring at the para position relative to the diazo group. This exploration was conducted through quantum chemistry calculations at various levels of theory, employing a continuum solvent model. Subsequently, we incorporated an explicit solvent (a dimethyl sulfoxide-water mixture) to simulate the most stable isomers using classical molecular dynamics, delivering a clear picture of the local solvation structure and intermolecular interactions. Finally, a hybrid quantum mechanics/molecular mechanics (QM/MM) approach was employed to accurately describe the evolution of the solutes' properties within their environment, accounting for finite temperature effects.

QupKake: Integrating Machine Learning and Quantum Chemistry for Micro-pKa Predictions

Accurate prediction of micro-pKa values is crucial for understanding and modulating the acidity and basicity of organic molecules, with applications in drug discovery, materials science, and environmental chemistry. This work introduces QupKake, a novel method that combines graph neural network models with semiempirical quantum mechanical (QM) features to achieve exceptional accuracy and generalization in micro-pKa prediction. QupKake outperforms state-of-the-art models on a variety of benchmark data sets, with root-mean-square errors between 0.5 and 0.8 pKa units on five external test sets. Feature importance analysis reveals the crucial role of QM features in both the reaction site enumeration and micro-pKa prediction models. QupKake represents a significant advancement in micro-pKa prediction, offering a powerful tool for various applications in chemistry and beyond.

An Atomistic View on the Mechanism of Diatom Peptide-Guided Biomimetic Silica Formation

Deciphering nature's remarkable way of encoding functions in its biominerals holds the potential to enable the rational development of nature-inspired materials with tailored properties. However, the complex processes that convert solution-state precursors into solid biomaterials remain largely unknown. In this study, an unconventional approach is presented to characterize these precursors for the diatom-derived peptides R5 and synthetic Silaffin-1A1 (synSil-1A1). These molecules can form defined supramolecular assemblies in solution, which act as templates for solid silica structures. Using a tailored structural biology toolbox, the structure-function relationships of these self-assemblies are unveiled. NMR-derived constraints are employed to enable a recently developed fractal-cluster formalism and then reveal the architecture of the peptide assemblies in atomistic detail. Finally, by monitoring the self-assembly activities during silica formation at simultaneous high temporal and residue resolution using real-time spectroscopy, the mechanism is elucidated underlying template-driven silica formation. Thus, it is demonstrated how to exercise morphology control over bioinorganic solids by manipulating the template architectures. It is found that the morphology of the templates is translated into the shape of bioinorganic particles via a mechanism that includes silica nucleation on the solution-state complexes' surfaces followed by complete surface coating and particle precipitation.

Synthesis of new non-natural L-glycosidic flavonoid derivatives and their evaluation as inhibitors of Trypanosoma cruzi ecto-nucleoside triphosphate diphosphohydrolase 1 (TcNTPDase1)

Trypanosoma cruzi is the pathogen of Chagas disease, a neglected tropical disease that affects more than 6 million people worldwide. There are no vaccines to prevent infection, and the therapeutic arsenal is very minimal and toxic. The unique E-NTPDase of T. cruzi (TcNTPDase1) plays essential roles in adhesion and infection and is a virulence factor. Quercetin is a flavonoid with antimicrobial, antiviral, and antitumor activities. Its potential as a partial inhibitor of NTPDases has also been demonstrated. In this work, we synthesized the non-natural L-glycoside derivatives of quercetin and evaluated them as inhibitors of recombinant TcNTPDase1 (rTcNTPDase1). These compounds, and quercetin and miquelianin, a natural quercetin derivative, were also tested. Compound 16 showed the most significant inhibitory effect (94%). Quercetin, miquelianin, and compound 14 showed inhibition close to 50%. We thoroughly investigated the inhibitory effect of 16. Our data suggested a competitive inhibition with a Ki of 8.39 μM (± 0.90). To better understand the interaction of compound 16 and rTcNTPDase1, we performed molecular dynamics simulations of the enzyme and docking analyses with the compounds. Our predictions show that compound 16 binds to the enzyme's catalytic site and interacts with important residues for NTPDase activity. As an inhibitor of a critical T. cruzi enzyme, (16) could be helpful as a starting point in the developing of a future treatment for Chagas disease. Furthermore, the discovery of (16) as an inhibitor of TcNTPDase1 may open new avenues in the study and development of new inhibitors of E-NTPDases.

GR-pKa: a message-passing neural network with retention mechanism for pKa prediction

During the drug discovery and design process, the acid-base dissociation constant (pKa) of a molecule is critically emphasized due to its crucial role in influencing the ADMET (absorption, distribution, metabolism, excretion, and toxicity) properties and biological activity. However, the experimental determination of pKa values is often laborious and complex. Moreover, existing prediction methods exhibit limitations in both the quantity and quality of the training data, as well as in their capacity to handle the complex structural and physicochemical properties of compounds, consequently impeding accuracy and generalization. Therefore, developing a method that can quickly and accurately predict molecular pKa values will to some extent help the structural modification of molecules, and thus assist the development process of new drugs. In this study, we developed a cutting-edge pKa prediction model named GR-pKa (Graph Retention pKa), leveraging a message-passing neural network and employing a multi-fidelity learning strategy to accurately predict molecular pKa values. The GR-pKa model incorporates five quantum mechanical properties related to molecular thermodynamics and dynamics as key features to characterize molecules. Notably, we originally introduced the novel retention mechanism into the message-passing phase, which significantly improves the model's ability to capture and update molecular information. Our GR-pKa model outperforms several state-of-the-art models in predicting macro-pKa values, achieving impressive results with a low mean absolute error of 0.490 and root mean square error of 0.588, and a high R2 of 0.937 on the SAMPL7 dataset.

AttenGpKa: A Universal Predictor of Solvation Acidity Using Graph Neural Network and Molecular Topology

Rapid and accurate calculation of acid dissociation constant (pKa) is crucial for designing chemical synthesis routes, optimizing catalysts, and predicting chemical behavior. Despite recent progress in machine learning, predicting solvation acidity, especially in nonaqueous solvents, remains challenging due to limited experimental data. This challenge arises from treating experimental values in different solvents as distinct data domains and modeling them separately. In this work, we treat both the solutes and solvents equally from a perspective of molecular topology and propose a highly universal framework called AttenGpKa for predicting solvation acidity. AttenGpKa is trained using 26,522 experimental pKa values from 60 pure and mixed solvents in the iBonD database. As a result, our model can simultaneously predict the pKa values of a compound in various solvents, including pure water, pure nonaqueous, and mixed solvents. AttenGpKa achieves universality by using graph neural networks and attention mechanisms to learn complex effects within solute and solvent molecules. Furthermore, encodings of both solute and solvent molecules are adaptively fused to simulate the influence of the solvent on acid dissociation. AttenGpKa demonstrates robust generalization in extensive validations. The interpretability studies further indicate that our model has effectively learnt electronic and solvent effects. A free-to-use software is provided to facilitate the use of AttenGpKa for pKa prediction.

Controlling rates and reversibilities of elimination reactions of hydroxybenzylammoniums by tuning dearomatization energies

Hydroxybenzylammonium compounds can undergo a reversible 1,4- or 1,6-elimination to afford quinone methide intermediates after release of the amine. These molecules are useful for the reversible conjugation of payloads to amines. We hypothesized that aromaticity could be used to alter the rate of reversibility as a distinct thermodynamic driving force. We describe the use of density functional theory (DFT) calculations to determine the effect of aromaticity on the rate of release of the amine from hydroxybenzylammonium compounds. Namely, the aromatic scaffold affects the dearomatization reaction to reduce the kinetic barrier and prevent the reversibility of the amine elimination. We consequently synthesized a small library of polycyclic hydroxybenzylammoniums, which resulted in a range of release half-lives from 18 minutes to 350 hours. The novel mechanistic insight provided herein significantly expands the range of release rates amenable to hydroxybenzylammonium-containing compounds. This work provides another way to affect the rate of payload release in hydroxybenzylammoniums.

ChemoDOTS: a web server to design chemistry-driven focused libraries

In drug discovery, the successful optimization of an initial hit compound into a lead molecule requires multiple cycles of chemical modification. Consequently, there is a need to efficiently generate synthesizable chemical libraries to navigate the chemical space surrounding the primary hit. To address this need, we introduce ChemoDOTS, an easy-to-use web server for hit-to-lead chemical optimization freely available at https://chemodots.marseille.inserm.fr/. With this tool, users enter an activated form of the initial hit molecule then choose from automatically detected reactive functions. The server proposes compatible chemical transformations via an ensemble of encoded chemical reactions widely used in the pharmaceutical industry during hit-to-lead optimization. After selection of the desired reactions, all compatible chemical building blocks are automatically coupled to the initial hit to generate a raw chemical library. Post-processing filters can be applied to extract a subset of compounds with specific physicochemical properties. Finally, explicit stereoisomers and tautomers are computed, and a 3D conformer is generated for each molecule. The resulting virtual library is compatible with most docking software for virtual screening campaigns. ChemoDOTS rapidly generates synthetically feasible, hit-focused, large, diverse chemical libraries with finely-tuned physicochemical properties via a user-friendly interface providing a powerful resource for researchers engaged in hit-to-lead optimization.

"AquaF" Building Blocks for Water-Compatible SN2 18F-Fluorination of Small-Molecule Radiotracers

Despite the widespread use of hydrophilic building blocks to incorporate 18F and improve tracer pharmacokinetics, achieving effective leaving group-mediated nucleophilic 18F-fluorination in water (excluding 18F/19F-exchange) remains a formidable challenge. Here, we present a water-compatible SN2 leaving group-mediated 18F-fluorination method employing preconjugated "AquaF" (phosphonamidic fluorides) building blocks. Among 19 compact tetracoordinated pentavalent P(V)-F candidates, the "AquaF" building blocks exhibit superior water solubility, sufficient capacity for 18F-fluorination in water, and excellent in vivo metabolic properties. Two nitropyridinol leaving groups, identified from a pool of leaving group candidates that further enhance the precursor water solubility, enable 18F-fluorination in water with a 10-2 M-1 s-1 level reaction rate constant (surpassing the 18F/19F-exchange) at room temperature. With the exergonic concerted SN2 18F-fluorination mechanism confirmed, this 18F-fluorination method achieves ∼90% radiochemical conversions and reaches a molar activity of 175 ± 40 GBq/μmol (using 12.2 GBq initial activity) in saline for 12 "AquaF"-modified proof-of-concept functional substrates and small-molecule 18F-tracers. [18F]AquaF-Flurpiridaz demonstrates significantly improved radiochemical yield and molar activity compared to 18F-Flurpiridaz, alongside enhanced cardiac uptake and heart/liver ratio in targeted myocardial perfusion imaging, providing a comprehensive illustration of "AquaF" building blocks-assisted water-compatible SN2 18F-fluorination of small-molecule radiotracers.

Exploring transmembrane allostery in the MexB: DB08385 variant as a promising inhibitor-like candidate against Pseudomonas aeruginosa antibiotic resistance: a computational study

Pseudomonas aeruginosa, a formidable pathogen renowned for its antimicrobial resistance, poses a significant threat to immunocompromised individuals. In this regard, the MexAB-OprM efflux pump acts as a pivotal line of defense by extruding antimicrobials from bacterial cells. The inner membrane homotrimeric protein MexB captures antibiotics and translocates them into the outer membrane OprM channel protein connected through the MexA adaptor protein. Despite extensive efforts, competitive inhibitors targeting the tight (T) protomer of the MexB protein have not received FDA approval for medical use. Over the past few years, allosteric inhibitors have become popular as alternatives to the classical competitive inhibitor-based approach because of their higher specificity, lower dosage, and reduced toxicological effects. Hence, in this study, we unveiled the existence of a transmembrane allosteric binding pocket of MexB inspired by the recent discovery of an important allosteric inhibitor, BDM88855, for the homolog AcrB protein. While repurposing BDM88855 proved ineffective in controlling the MexB loose (L) protomer, our investigation identified a promising alternative: a chlorine-containing variant of DB08385 (2-Cl DB08385 or Variant 1). Molecular dynamics simulations, including binding free energy estimation coupled with heterogeneous dielectric implicit membrane model (implicit-membrane MM/PBSA), interaction entropy (IE) analysis and potential of mean force (PMF) calculation, demonstrated Variant 1's superior binding affinity to the transmembrane pocket, displaying the highest energy barrier in the ligand unbinding process. To elucidate the allosteric crosstalk between the transmembrane and porter domain of MexB, we employed the 'eigenvector centrality' measure in the linear mutual information obtained from the protein correlation network. Notably, this study confirmed the presence of an allosteric transmembrane site in the MexB L protomer. In addition to this, Variant 1 emerged as a potent regulator of allosteric crosstalk, inducing an 'O-L intermediate state' in the MexB L protomer. This induced state might hold the potential to diminish substrate intake into the access pocket, leading to the ineffective efflux of antibiotics.

Unveiling the Cleavage Mechanism of an RNA Model Compound on the whole pH Scale: Computations Meet Experiments in the Determination of Reaction Rates

The knowledge of the mechanism of reactions occurring in solution is a primary research line both in the context of theoretical-computational chemistry and in the field of organic and bio-organic chemistry. Given the importance of the hydrolysis of nucleic acids in life-related phenomena, here we present a combined experimental and computational study on the cleavage of an RNA model compound. This phosphodiester features a cleavage rate strictly dependent on the pH with three different dependence domains. Such experimental evidence, highlighted by an in-depth kinetic investigation, unequivocally suggests a change in the reaction mechanism along the pH scale. In order to interpret the data and to explain the experimental behavior, we have applied a theoretical-computational procedure, involving a hybrid quantum/classical approach, able to model chemical reactions in complex environments, i. e. in solution. This study turns out to quantitatively reproduce the experimental data with accuracy and, in addition, provides useful mechanistic insight into the transesterification process of the investigated compound. The study indicates that the cleavage can occur through anA N D N ${A_N D_N }$ , anA N + D N ${A_N + D_N }$ , and aD N A N ${D_N A_N }$ mechanism depending on the pH values.

Assessment of Nucleobase Protomeric and Tautomeric States in Nucleic Acid Structures for Interaction Analysis and Structure-Based Ligand Design

With increasing interest in RNA as a therapeutic and a potential target, the role of RNA structures has become more important. Even slight changes in nucleobases, such as modifications or protomeric and tautomeric states, can have a large impact on RNA structure and function, while local environments in turn affect protonation and tautomerization. In this work, the application of empirical tools for pKa and tautomer prediction for RNA modifications was elucidated and compared with ab initio quantum mechanics (QM) methods and expanded toward macromolecular RNA structures, where QM is no longer feasible. In this regard, the Protonate3D functionality within the molecular operating environment (MOE) was expanded for nucleobase protomer and tautomer predictions and applied to reported examples of altered protonation states depending on the local environment. Overall, observations of nonstandard protomers and tautomers were well reproduced, including structural C+G:C(A) and A+GG motifs, several mismatches, and protonation of adenosine or cytidine as the general acid in nucleolytic ribozymes. Special cases, such as cobalt hexamine-soaked complexes or the deprotonation of guanosine as the general base in nucleolytic ribozymes, proved to be challenging. The collected set of examples shall serve as a starting point for the development of further RNA protonation prediction tools, while the presented Protonate3D implementation already delivers reasonable protonation predictions for RNA and DNA macromolecules. For cases where higher accuracy is needed, like following catalytic pathways of ribozymes, incorporation of QM-based methods can build upon the Protonate3D-generated starting structures. Likewise, this protonation prediction can be used for structure-based RNA-ligand design approaches.

Unveiling Liquid-Phase Exfoliation of Graphite and Boron Nitride Using Fluorescent Dyes Through Combined Experiments and Simulations

Liquid-phase exfoliation (LPE) in aqueous solutions provides a simple, scalable, and green approach to produce 2D materials. By combining atomistic simulations with exfoliation experiments, the interaction between a surfactant and a 2D layer at the molecular scale can be better understood. In this work, two different dyes, corresponding to rhodamine B base (Rbb) and to a phenylboronic acid BODIPY (PBA-BODIPY) derivative, are employed as dispersants to exfoliate graphene and hexagonal boron nitride (hBN) through sonication-assisted LPE. The exfoliated 2D sheets, mostly as few-layers, exhibit good quality and high loading of dyes. Using molecular dynamics (MD) simulations, the binding free energies are calculated and the arrangement of both dyes on the layers are predicted. It has been found that the dyes show a higher affinity toward hBN than graphene, which is consistent with the higher yields of exfoliated hBN. Furthermore, it is demonstrated that the adsorption behavior of Rbb molecules on graphene and hBN is quite different compared to PBA-BODIPY.

Structural and Interactional Analysis of the Flavonoid Pathway Proteins: Chalcone Synthase, Chalcone Isomerase and Chalcone Isomerase-like Protein

Chalcone synthase (CHS) and chalcone isomerase (CHI) catalyze the first two committed steps of the flavonoid pathway that plays a pivotal role in the growth and reproduction of land plants, including UV protection, pigmentation, symbiotic nitrogen fixation, and pathogen resistance. Based on the obtained X-ray crystal structures of CHS, CHI, and chalcone isomerase-like protein (CHIL) from the same monocotyledon, Panicum virgatum, along with the results of the steady-state kinetics, spectroscopic/thermodynamic analyses, intermolecular interactions, and their effect on each catalytic step are proposed. In addition, PvCHI's unique activity for both naringenin chalcone and isoliquiritigenin was analyzed, and the observed hierarchical activity for those type-I and -II substrates was explained with the intrinsic characteristics of the enzyme and two substrates. The structure of PvCHS complexed with naringenin supports uncompetitive inhibition. PvCHS displays intrinsic catalytic promiscuity, evident from the formation of p-coumaroyltriacetic acid lactone (CTAL) in addition to naringenin chalcone. In the presence of PvCHIL, conversion of p-coumaroyl-CoA to naringenin through PvCHS and PvCHI displayed ~400-fold increased Vmax with reduced formation of CTAL by 70%. Supporting this model, molecular docking, ITC (Isothermal Titration Calorimetry), and FRET (Fluorescence Resonance Energy Transfer) indicated that both PvCHI and PvCHIL interact with PvCHS in a non-competitive manner, indicating the plausible allosteric effect of naringenin on CHS. Significantly, the presence of naringenin increased the affinity between PvCHS and PvCHIL, whereas naringenin chalcone decreased the affinity, indicating a plausible feedback mechanism to minimize spontaneous incorrect stereoisomers. These are the first findings from a three-body system from the same species, indicating the importance of the macromolecular assembly of CHS-CHI-CHIL in determining the amount and type of flavonoids produced in plant cells.

Enhancing oral bioavailability of an antifungal thiazolylhydrazone derivative: Development and characterization of a self-emulsifying drug delivery system

RN104 (2-[2-(cyclohexylmethylene)hydrazinyl)]-4-phenylthiazole) is a thiazolylhydrazone derivative with prominent antifungal activity. This work aimed to develop a self-emulsifying drug delivery system (SEDDS) loaded with RN104 to improve its biopharmaceutical properties and enhance its oral bioavailability. Medium chain triglycerides, sorbitan monooleate, and polysorbate 80 were selected as components for the SEDDS formulation based on solubility determination and a pseudo-ternary phase diagram. The formulation was optimized using the central composite design in response surface methodology. The optimized condition consisted of medium chain triglycerides, sorbitan monooleate, and polysorbate 80 in a mass ratio of 65.5:23.0:11.5, achieving maximum drug loading (10 mg/mL) and minimum particle size (118.4 ± 0.7 nm). The developed RN104-SEDDS was fully characterized using dynamic light scattering, in vitro release studies, stability assessments, polarized light microscopy, and transmission electron microscopy. In vivo pharmacokinetic studies in mice demonstrated that RN104-SEDDS significantly improved oral bioavailability compared to free RN104 (the relative bioavailability was 2133 %). These results clearly indicated the successful application of SEDDS to improve the pharmacokinetic profile and to enhance the oral bioavailability of RN104, substantiating its potential as a promising antifungal drug candidate.

Difluoroacetic acid: an alternative acid in the detritylation reaction for the solid-phase synthesis of oligonucleotides

Dichloroacetic acid or trichloroacetic acid are commonly used in the detritylation reaction of the automated solid-phase synthesis of oligonucleotides. Dichloroacetic acid, however, is often contaminated with trichloroacetaldehyde (chloral), leading to the formation of inseparable impurities in the final oligonucleotide product. In this work, three different sequences, namely T18, d(TAA)6, and an 18-mer mixed sequence, were used as models to compare the deprotection efficiency of three acids: trichloroacetic acid, dichloroacetic acid, and difluoroacetic acid. Comparable purities of full-length products were obtained for the synthesis of the three model sequences when dichloroacetic acid or difluoroacetic acid were used during the detritylation reaction, however, conditions need to be optimized for the synthesis of purine-rich sequences. Therefore, difluoroacetic acid is a potential alternative to dichloroacetic acid in the solid-phase synthesis of oligonucleotides to avoid the impurity formation due to presence of chloral.

Design of Modified Polymer Membranes Using Machine Learning

Surface modification is an attractive strategy to adjust the properties of polymer membranes. Unfortunately, predictive structure-processing-property relationships between the modification strategies and membrane performance are often unknown. One possibility to tackle this challenge is the application of data-driven methods such as machine learning. In this study, we applied machine learning methods to data sets containing the performance parameters of modified membranes. The resulting machine learning models were used to predict performance parameters, such as the pure water permeability and the zeta potential of membranes modified with new substances. The predictions had low prediction errors, which allowed us to generalize them to similar membrane modifications and processing conditions. Additionally, machine learning methods were able to identify the impact of substance properties and process parameters on the resulting membrane properties. Our results demonstrate that small data sets, as they are common in materials science, can be used as training data for predictive machine learning models. Therefore, machine learning shows great potential as a tool to expedite the development of high-performance membranes while reducing the time and costs associated with the development process at the same time.

Explainable Graph Neural Networks with Data Augmentation for Predicting pKa of C-H Acids

The pKa of C-H acids is an important parameter in the fields of organic synthesis, drug discovery, and materials science. However, the prediction of pKa is still a great challenge due to the limit of experimental data and the lack of chemical insight. Here, a new model for predicting the pKa values of C-H acids is proposed on the basis of graph neural networks (GNNs) and data augmentation. A message passing unit (MPU) was used to extract the topological and target-related information from the molecular graph data, and a readout layer was utilized to retrieve the information on the ionization site C atom. The retrieved information then was adopted to predict pKa by a fully connected network. Furthermore, to increase the diversity of the training data, a knowledge-infused data augmentation technique was established by replacing the H atoms in a molecule with substituents exhibiting different electronic effects. The MPU was pretrained with the augmented data. The efficacy of data augmentation was confirmed by visualizing the distribution of compounds with different substituents and by classifying compounds. The explainability of the model was studied by examining the change of pKa values when a specific atom was masked. This explainability was used to identify the key substituents for pKa. The model was evaluated on two data sets from the iBonD database. Dataset1 includes the experimental pKa values of C-H acids measured in DMSO, while dataset2 comprises the pKa values measured in water. The results show that the knowledge-infused data augmentation technique greatly improves the predictive accuracy of the model, especially when the number of samples is small.

Compound Absorption in Polymer Devices Impairs the Translatability of Preclinical Safety Assessments

Organotypic and microphysiological systems (MPS) that can emulate the molecular phenotype and function of human tissues, such as liver, are increasingly used in preclinical drug development. However, despite their improved predictivity, drug development success rates have remained low with most compounds failing in clinical phases despite promising preclinical data. Here, it is tested whether absorption of small molecules to polymers commonly used for MPS fabrication can impact preclinical pharmacological and toxicological assessments and contribute to the high clinical failure rates. To this end, identical devices are fabricated from eight different MPS polymers and absorption of prototypic compounds with different physicochemical properties are analyzed. It is found that overall absorption is primarily driven by compound hydrophobicity and the number of rotatable bonds. However, absorption can differ by >1000-fold between polymers with polydimethyl siloxane (PDMS) being most absorptive, whereas polytetrafluoroethylene (PTFE) and thiol-ene epoxy (TEE) absorbed the least. Strikingly, organotypic primary human liver cultures successfully flagged hydrophobic hepatotoxins in lowly absorbing TEE devices at therapeutically relevant concentrations, whereas isogenic cultures in PDMS devices are resistant, resulting in false negative safety signals. Combined, these results can guide the selection of MPS materials and facilitate the development of preclinical assays with improved translatability.

Recent Progress in the Endosomal Escape Mechanism and Chemical Structures of Polycations for Nucleic Acid Delivery

Nucleic acid-based therapies are seeing a spiralling surge. Stimuli-responsive polymers, especially pH-responsive ones, are gaining widespread attention because of their ability to efficiently deliver nucleic acids. These polymers can be synthesized and modified according to target requirements, such as delivery sites and the nature of nucleic acids. In this regard, the endosomal escape mechanism of polymer-nucleic acid complexes (polyplexes) remains a topic of considerable interest owing to various plausible escape mechanisms. This review describes current progress in the endosomal escape mechanism of polyplexes and state-of-the-art chemical designs for pH-responsive polymers. The importance is also discussed of the acid dissociation constant (i.e., pKa) in designing the new generation of pH-responsive polymers, along with assays to monitor and quantify the endosomal escape behavior. Further, the use of machine learning is addressed in pKa prediction and polymer design to find novel chemical structures for pH responsiveness. This review will facilitate the design of new pH-responsive polymers for advanced and efficient nucleic acid delivery.

Unlocking the Potential of High-Quality Dopamine Transporter Pharmacological Data: Advancing Robust Machine Learning-Based QSAR Modeling

The dopamine transporter (DAT) plays a critical role in the central nervous system and has been implicated in numerous psychiatric disorders. The ligand-based approaches are instrumental to decipher the structure-activity relationship (SAR) of DAT ligands, especially the quantitative SAR (QSAR) modeling. By gathering and analyzing data from literature and databases, we systematically assemble a diverse range of ligands binding to DAT, aiming to discern the general features of DAT ligands and uncover the chemical space for potential novel DAT ligand scaffolds. The aggregation of DAT pharmacological activity data, particularly from databases like ChEMBL, provides a foundation for constructing robust QSAR models. The compilation and meticulous filtering of these data, establishing high-quality training datasets with specific divisions of pharmacological assays and data types, along with the application of QSAR modeling, prove to be a promising strategy for navigating the pertinent chemical space. Through a systematic comparison of DAT QSAR models using training datasets from various ChEMBL releases, we underscore the positive impact of enhanced data set quality and increased data set size on the predictive power of DAT QSAR models.

Design, synthesis and antiviral evaluation of novel conjugates of the 1,7,7-trimethylbicyclo[2.2.1]heptane scaffold and saturated N-heterocycles via 1,2,3-triazole linker

A new series of heterocyclic derivatives with a 1,7,7-trimethylbicyclo[2.2.1]heptane fragment was designed, synthesised and biologically evaluated. Synthesis of the target compounds was performed using the Cu(I) catalysed cycloaddition reaction. The key starting substances in the click reaction were an alkyne containing a 1,7,7-trimethylbicyclo[2.2.1]heptane fragment and a series of azides with saturated nitrogen-containing heterocycles. Some of the derivatives were found to exhibit strong antiviral activity against Marburg and Ebola pseudotype viruses. Lysosomal trapping assays revealed the derivatives to possess lysosomotropic properties. The molecular modelling study demonstrated the binding affinity between the compounds investigated and the possible active site to be mainly due to hydrophobic interactions. Thus, combining a natural hydrophobic structural fragment and a lysosome-targetable heterocycle may be an effective strategy for designing antiviral agents.

Leveraging DFT and Molecular Fragmentation for Chemically Accurate pKa Prediction Using Machine Learning

We present a quantum mechanical/machine learning (ML) framework based on random forest to accurately predict the pKas of complex organic molecules using inexpensive density functional theory (DFT) calculations. By including physics-based features from low-level DFT calculations and structural features from our connectivity-based hierarchy (CBH) fragmentation protocol, we can correct the systematic error associated with DFT. The generalizability and performance of our model are evaluated on two benchmark sets (SAMPL6 and Novartis). We believe the carefully curated input of physics-based features lessens the model's data dependence and need for complex deep learning architectures, without compromising the accuracy of the test sets. As a point of novelty, our work extends the applicability of CBH, employing it for the generation of viable molecular descriptors for ML.

Resolution of trans-1,2-cyclohexanedicarboxylic acid containing two carboxylic groups by forming diastereomeric salts based on feeding molar ratio control

To investigate the thermodynamic and molecular self-assembly mechanism of trans-1,2-cyclohexane dicarboxylic acid containing two carboxylic acid groups in the chiral resolution process, (S)-phenylethylamine was used as the chiral resolving agent. Two stoichiometric salts were formed when the raw materials were fed at different molar ratios: cyclohexane dicarboxylate monophenylethylamine salt and cyclohexane dicarboxylate diphenylethylamine salt. When the molar ratio of the (S)-phenylethylamine to trans-1,2-cyclohexane dicarboxylic acid was less than 3:1, trans-(1S,2S)-cyclohexane dicarboxylic acid was obtained with 97 e.e% purity. But when the molar ratio exceeded 3:1, the product was the racemic trans-(1,2)-cyclohexane dicarboxylic acid. In addition, single crystal structures of more soluble mono-salt, less soluble mono-salt, and less soluble di-salt were obtained. The weak intermolecular interactions and the way of the molecules packing in the crystals were analyzed. The hydrogen bond was stronger in the less soluble salt than that in the more soluble salt. And a "lock-and-key" structure in the hydrophobic layers makes it more tightly packed through the van der Waals interaction, which is responsible for the stability of less soluble salts.

Combining Experimental and Computational Methods to Produce Conjugates of Anticholinesterase and Antioxidant Pharmacophores with Linker Chemistries Affecting Biological Activities Related to Treatment of Alzheimer's Disease

Effective therapeutics for Alzheimer's disease (AD) are in great demand worldwide. In our previous work, we responded to this need by synthesizing novel drug candidates consisting of 4-amino-2,3-polymethylenequinolines conjugated with butylated hydroxytoluene via fixed-length alkylimine or alkylamine linkers (spacers) and studying their bioactivities pertaining to AD treatment. Here, we report significant extensions of these studies, including the use of variable-length spacers and more detailed biological characterizations. Conjugates were potent inhibitors of acetylcholinesterase (AChE, the most active was 17d IC50 15.1 ± 0.2 nM) and butyrylcholinesterase (BChE, the most active was 18d: IC50 5.96 ± 0.58 nM), with weak inhibition of off-target carboxylesterase. Conjugates with alkylamine spacers were more effective cholinesterase inhibitors than alkylimine analogs. Optimal inhibition for AChE was exhibited by cyclohexaquinoline and for BChE by cycloheptaquinoline. Increasing spacer length elevated the potency against both cholinesterases. Structure-activity relationships agreed with docking results. Mixed-type reversible AChE inhibition, dual docking to catalytic and peripheral anionic sites, and propidium iodide displacement suggested the potential of hybrids to block AChE-induced β-amyloid (Aβ) aggregation. Hybrids also exhibited the inhibition of Aβ self-aggregation in the thioflavin test; those with a hexaquinoline ring and C8 spacer were the most active. Conjugates demonstrated high antioxidant activity in ABTS and FRAP assays as well as the inhibition of luminol chemiluminescence and lipid peroxidation in mouse brain homogenates. Quantum-chemical calculations explained antioxidant results. Computed ADMET profiles indicated favorable blood-brain barrier permeability, suggesting the CNS activity potential. Thus, the conjugates could be considered promising multifunctional agents for the potential treatment of AD.

Adding α,α-disubstituted and β-linked monomers to the genetic code of an organism

The genetic code of living cells has been reprogrammed to enable the site-specific incorporation of hundreds of non-canonical amino acids into proteins, and the encoded synthesis of non-canonical polymers and macrocyclic peptides and depsipeptides1-3. Current methods for engineering orthogonal aminoacyl-tRNA synthetases to acylate new monomers, as required for the expansion and reprogramming of the genetic code, rely on translational readouts and therefore require the monomers to be ribosomal substrates4-6. Orthogonal synthetases cannot be evolved to acylate orthogonal tRNAs with non-canonical monomers (ncMs) that are poor ribosomal substrates, and ribosomes cannot be evolved to polymerize ncMs that cannot be acylated onto orthogonal tRNAs-this co-dependence creates an evolutionary deadlock that has essentially restricted the scope of translation in living cells to α-L-amino acids and closely related hydroxy acids. Here we break this deadlock by developing tRNA display, which enables direct, rapid and scalable selection for orthogonal synthetases that selectively acylate their cognate orthogonal tRNAs with ncMs in Escherichia coli, independent of whether the ncMs are ribosomal substrates. Using tRNA display, we directly select orthogonal synthetases that specifically acylate their cognate orthogonal tRNA with eight non-canonical amino acids and eight ncMs, including several β-amino acids, α,α-disubstituted-amino acids and β-hydroxy acids. We build on these advances to demonstrate the genetically encoded, site-specific cellular incorporation of β-amino acids and α,α-disubstituted amino acids into a protein, and thereby expand the chemical scope of the genetic code to new classes of monomers.

Small Molecule Inhibitors Targeting the "Undruggable" Survivin: The Past, Present, and Future from a Medicinal Chemist's Perspective

Survivin, a homodimeric protein and a member of the IAP family, plays a vital function in cell survival and cycle progression by interacting with various proteins and complexes. Its expression is upregulated in cancers but not detectable in normal tissues. Thus, it has been regarded and validated as an ideal cancer target. However, survivin is "undruggable" due to its lack of enzymatic activities or active sites for small molecules to bind/inhibit. Academic and industrial laboratories have explored different strategies to overcome this hurdle over the past two decades, with some compounds advanced into clinical testing. These strategies include inhibiting survivin expression, its interaction with binding partners and homodimerization. Here, we provide comprehensive analyses of these strategies and perspective on different small molecule survivin inhibitors to help drug discovery targeting "undruggable" proteins in general and survivin specifically with a true survivin inhibitor that will prevail in the foreseeable future.

Uncovering the Structural and Binding Insights of Dual Inhibitors Simultaneously Targeting Two Distinct Sites on EGFR Kinase

Epidermal growth factor receptor (EGFR) is the first growth factor receptor identified in normal cells that is related to the receptor tyrosine kinase, which causes regular cell division. A point mutation in EGFR intracellular kinase domain forces the abnormal cell divisions throughout time, leading to non-small cell lung cancer (NSCLC) transformation. Thus, competitive inhibitors that bind to the ATP binding pocket have been developed as a targeted therapy for NSCLC. The third-generation kinase inhibitor Osimertinib is currently playing a very vital role in the treatment of NSCLC. However, it is not effective toward the C797S kinase domain mutation. For this reason, fourth-generation kinase noncompetitive inhibitors are introduced which work through binding to an allosteric pocket near the ATP binding region and act as a better binding agent for this mutated kinase domain. However, the problem is that these single fourth-generation kinase inhibitors may not be as effective as a single agent. The aim of this work was to apply combinations of these two inhibitors together in different binding regions of EGFR without overlapping the resistance mechanism to obtain the key direct and indirect interactions occurring between them. Moreover, the free energy of dissociation of an inhibitor from its binding sites in the presence of a second inhibitor immobilized in another binding site was also the focus of the study. To realize this aim, we performed conventional molecular dynamics simulations and principal component analysis and dynamic cross-correlation matrices along with umbrella sampling. Our results demonstrated that binding of dual inhibitors triggered conformational changes of the protein more toward the inactive state. Furthermore, allosteric inhibitors bound more strongly to protein kinase EGFR than the orthosteric inhibitors in the presence of dual inhibitors. Finally, the binding mechanism and important hydrogen-bonding residues during unbinding of the inhibitors were fully elucidated. This study provides insight into the binding of the receptor-orthosteric inhibitor-allosteric inhibitor, which can be helpful for further design of novel inhibitors that have a better inhibitory action.

Acetylcholinesterase Inhibition in Rats and Humans Following Acute Fenitrothion Exposure Predicted by Physiologically Based Kinetic Modeling-Facilitated Quantitative In Vitro to In Vivo Extrapolation

Worldwide use of organophosphate pesticides as agricultural chemicals aims to maintain a stable food supply, while their toxicity remains a major public health concern. A common mechanism of acute neurotoxicity following organophosphate pesticide exposure is the inhibition of acetylcholinesterase (AChE). To support Next Generation Risk Assessment for public health upon acute neurotoxicity induced by organophosphate pesticides, physiologically based kinetic (PBK) modeling-facilitated quantitative in vitro to in vivo extrapolation (QIVIVE) approach was employed in this study, with fenitrothion (FNT) as an exemplary organophosphate pesticide. Rat and human PBK models were parametrized with data derived from in silico predictions and in vitro incubations. Then, PBK model-based QIVIVE was performed to convert species-specific concentration-dependent AChE inhibition obtained from in vitro blood assays to corresponding in vivo dose-response curves, from which points of departure (PODs) were derived. The obtained values for rats and humans were comparable with reported no-observed-adverse-effect levels (NOAELs). Humans were found to be more susceptible than rats toward erythrocyte AChE inhibition induced by acute FNT exposure due to interspecies differences in toxicokinetics and toxicodynamics. The described approach adequately predicts toxicokinetics and acute toxicity of FNT, providing a proof-of-principle for applying this approach in a 3R-based chemical risk assessment paradigm.

Formulation Graphs for Mapping Structure-Composition of Battery Electrolytes to Device Performance

Advanced computational methods are being actively sought to address the challenges associated with the discovery and development of new combinatorial materials, such as formulations. A widely adopted approach involves domain-informed high-throughput screening of individual components that can be combined together to form a formulation. This manages to accelerate the discovery of new compounds for a target application but still leaves the process of identifying the right "formulation" from the shortlisted chemical space largely a laboratory experiment-driven process. We report a deep learning model, the Formulation Graph Convolution Network (F-GCN), that can map the structure-composition relationship of the formulation constituents to the property of liquid formulation as a whole. Multiple GCNs are assembled in parallel that featurize formulation constituents domain-intuitively on the fly. The resulting molecular descriptors are scaled based on the respective constituent's molar percentage in the formulation, followed by integration into a combined formulation descriptor that represents the complete formulation to an external learning architecture. The use case of the proposed formulation learning model is demonstrated for battery electrolytes by training and testing it on two exemplary data sets representing electrolyte formulations vs battery performance: one data set is sourced from the literature about Li/Cu half-cells, while the other is obtained by lab experiments related to lithium-iodide full-cell chemistry. The model is shown to predict performance metrics such as Coulombic efficiency (CE) and specific capacity of new electrolyte formulations with the lowest reported errors. The best-performing F-GCN model uses molecular descriptors derived from molecular graphs (GCNs) that are informed with HOMO-LUMO and electric moment properties of the molecules using a knowledge transfer technique.

Optimizing the Composition of the Substrate Enhances the Performance of Peroxidase-like Nanozymes in Colorimetric Assays: A Case Study of Prussian Blue and 3,3'-Diaminobenzidine

One of the emerging trends in modern analytical and bioanalytical chemistry involves the substitution of enzyme labels (such as horseradish peroxidase) with nanozymes (nanoparticles possessing enzyme-like catalytic activity). Since enzymes and nanozymes typically operate through different catalytic mechanisms, it is expected that optimal reaction conditions will also differ. The optimization of substrates for nanozymes usually focuses on determining the ideal pH and temperature. However, in some cases, even this step is overlooked, and commercial substrate formulations designed for enzymes are utilized. This paper demonstrates that not only the pH but also the composition of the substrate buffer, including the buffer species and additives, significantly impact the analytical signal generated by nanozymes. The presence of enhancers such as imidazole in commercial substrates diminishes the catalytic activity of nanozymes, which is demonstrated herein through the use of 3,3'-diaminobenzidine (DAB) and Prussian Blue as a model chromogenic substrate and nanozyme. Conversely, a simple modification to the substrate buffer greatly enhances the performance of nanozymes. Specifically, in this paper, it is demonstrated that buffers such as citrate, MES, HEPES, and TRIS, containing 1.5-2 M NaCl or NH4Cl, substantially increase DAB oxidation by Prussian Blue and yield a higher signal compared to commercial DAB formulations. The central message of this paper is that the optimization of substrate composition should be an integral step in the development of nanozyme-based assays. Herein, a step-by-step optimization of the DAB substrate composition for Prussian Blue nanozymes is presented. The optimized substrate outperforms commercial formulations in terms of efficiency. The effectiveness of the optimized DAB substrate is affirmed through its application in several commonly used immunostaining techniques, including tissue staining, Western blotting assays of immunoglobulins, and dot blot assays of antibodies against SARS-CoV-2.

AI & experimental-based discovery and preclinical IND-enabling studies of selective BMX inhibitors for development of cancer therapeutics

The current work aims to design and provide a preliminary IND-enabling study of selective BMX inhibitors for cancer therapeutics development. BMX is an emerging target, more notably in oncological and immunological diseases. In this work, we have employed a predictive AI-based platform to design the selective inhibitors considering the novelty, IP prior protection, and drug-likeness properties. Furthermore, selected top candidates from the initial iteration of the design were synthesized and chemically characterized utilizing 1H NMR and LC-MS. Employing a panel of biochemical (enzymatic) and cancer cell lines, the selected molecules were tested against these assays. In addition, we used artificial intelligence to predict and evaluate several critical IND-focused physicochemical and pharmacokinetics values of the selected molecules. A secondary objective of the current work was also to validate the sole role of BMX in animal models known to be mediated by BMX. More than 50 molecules were designed in the present study employing five novel discovered scaffolds. Two molecules were nominated for further IND-focused studies. Compound II showed promising in-vitro activity against BMX in both enzymatic assays compared to other kinases and in cancer cell lines with known BMX overexpression. Interestingly, compound II showed very favorable physicochemical and pharmacokinetics properties as predicted by the used platforms. The animal study further confirmed the sole role of BMX in the disease model. The current work provides promising data on a selective BMX inhibitor as a potential lead for therapeutics development, and the asset is currently in the optimization stage. Notably, the current study shows a framework for a combined approach employing both AI and experimentation that can be used by academic labs in their research programs to more streamline programs into IND-focused to be bridged easily for further clinical development with industrial partners.

pKa prediction of per- and polyfluoroalkyl acids in water using in silico gas phase stretching vibrational frequencies and infrared intensities

To successfully understand and model the environmental fate of per- and polyfluoroalkyl substances (PFAS), it is necessary to know key physicochemical properties (PChPs) such as pKa; however, measured PChPs of PFAS are scarce and of uncertain reliability. In this study, quantitative structure-activity relationships (QSARs) were developed by correlating calculated (M062-X/aug-cc-pVDZ) vibrational frequencies (VF) and corresponding infrared intensities (IRInt) to the pKa of carboxylic acids, sulfonic acids, phosphonic acids, sulfonamides, betaines, and alcohols. Antisymmetric stretching VF of the anionic species were used for all subclasses except for alcohols where the OH stretching VF performed better. The individual QSARs predicted the pKa for each subclass mostly within 0.5 pKa units from the experimental values. The inclusion of IRInt as a pKa predictor for carboxylic acids improved the results by decreasing the root-mean-square error from 0.35 to 0.25 (n > 100). Application of the developed QSARs to estimate the pKa of PFAS within each subclass revealed that the length of the perfluoroalkyl chain has minimal effect on the pKa, consistent with other models but in stark contrast with the limited experimental data available.

A SIFSIX-MOF constructed from a metalloligand yields enhanced stability for selective CO2 adsorption

The first example of a metalloligand(ML)-based non-interpenetrated SIFSIX MOF [Cu(ML)2(SiF6)]n (ML = Cu(pyac)2 = bis[3-(4-pyridyl)pentane-2,4-dionato]copper(II)) exhibits one-dimensional pore channels decorated with accessible Cu2+ sites that provide superior water vapor stability and CO2 selectivity over CH4vs. similar materials constructed from non-metal containing organic ligands.

Open-Source Machine Learning in Computational Chemistry

The field of computational chemistry has seen a significant increase in the integration of machine learning concepts and algorithms. In this Perspective, we surveyed 179 open-source software projects, with corresponding peer-reviewed papers published within the last 5 years, to better understand the topics within the field being investigated by machine learning approaches. For each project, we provide a short description, the link to the code, the accompanying license type, and whether the training data and resulting models are made publicly available. Based on those deposited in GitHub repositories, the most popular employed Python libraries are identified. We hope that this survey will serve as a resource to learn about machine learning or specific architectures thereof by identifying accessible codes with accompanying papers on a topic basis. To this end, we also include computational chemistry open-source software for generating training data and fundamental Python libraries for machine learning. Based on our observations and considering the three pillars of collaborative machine learning work, open data, open source (code), and open models, we provide some suggestions to the community.

Derivatives of 9-phosphorylated acridine as butyrylcholinesterase inhibitors with antioxidant activity and the ability to inhibit β-amyloid self-aggregation: potential therapeutic agents for Alzheimer's disease

We investigated the inhibitory activities of novel 9-phosphoryl-9,10-dihydroacridines and 9-phosphorylacridines against acetylcholinesterase (AChE), butyrylcholinesterase (BChE), and carboxylesterase (CES). We also studied the abilities of the new compounds to interfere with the self-aggregation of β-amyloid (Aβ42) in the thioflavin test as well as their antioxidant activities in the ABTS and FRAP assays. We used molecular docking, molecular dynamics simulations, and quantum-chemical calculations to explain experimental results. All new compounds weakly inhibited AChE and off-target CES. Dihydroacridines with aryl substituents in the phosphoryl moiety inhibited BChE; the most active were the dibenzyloxy derivative 1d and its diphenethyl bioisostere 1e (IC50 = 2.90 ± 0.23 µM and 3.22 ± 0.25 µM, respectively). Only one acridine, 2d, an analog of dihydroacridine, 1d, was an effective BChE inhibitor (IC50 = 6.90 ± 0.55 μM), consistent with docking results. Dihydroacridines inhibited Aβ42 self-aggregation; 1d and 1e were the most active (58.9% ± 4.7% and 46.9% ± 4.2%, respectively). All dihydroacridines 1 demonstrated high ABTS•+-scavenging and iron-reducing activities comparable to Trolox, but acridines 2 were almost inactive. Observed features were well explained by quantum-chemical calculations. ADMET parameters calculated for all compounds predicted favorable intestinal absorption, good blood-brain barrier permeability, and low cardiac toxicity. Overall, the best results were obtained for two dihydroacridine derivatives 1d and 1e with dibenzyloxy and diphenethyl substituents in the phosphoryl moiety. These compounds displayed high inhibition of BChE activity and Aβ42 self-aggregation, high antioxidant activity, and favorable predicted ADMET profiles. Therefore, we consider 1d and 1e as lead compounds for further in-depth studies as potential anti-AD preparations.

G4-QuadScreen: A Computational Tool for Identifying Multi-Target-Directed Anticancer Leads against G-Quadruplex DNA

The study presents 'G4-QuadScreen', a user-friendly computational tool for identifying MTDLs against G4s. Also, it offers a few hit MTDLs based on in silico and in vitro approaches. Multi-tasking QSAR models were developed using linear discriminant analysis and random forest machine learning techniques for predicting the responses of interest (G4 interaction, G4 stabilization, G4 selectivity, and cytotoxicity) considering the variations in the experimental conditions (e.g., G4 sequences, endpoints, cell lines, buffers, and assays). A virtual screening with G4-QuadScreen and molecular docking using YASARA (AutoDock-Vina) was performed. G4 activities were confirmed via FRET melting, FID, and cell viability assays. Validation metrics demonstrated the high discriminatory power and robustness of the models (the accuracy of all models is ~>90% for the training sets and ~>80% for the external sets). The experimental evaluations showed that ten screened MTDLs have the capacity to selectively stabilize multiple G4s. Three screened MTDLs induced a strong inhibitory effect on various human cancer cell lines. This pioneering computational study serves a tool to accelerate the search for new leads against G4s, reducing false positive outcomes in the early stages of drug discovery. The G4-QuadScreen tool is accessible on the ChemoPredictionSuite website.

Chiral Separation of Oxazolidinone Analogs by Capillary Electrophoresis Using Anionic Cyclodextrins as Chiral Selectors: Emphasis on Enantiomer Migration Order

Comparative chiral separations of enantiomeric pairs of four oxazolidinone and two related thio-derivatives were performed by capillary electrophoresis, using cyclodextrins (CDs) as chiral selectors. Since the selected analytes are neutral, the enantiodiscrimination capabilities of nine anionic CD derivatives were determined, in 50 mM phosphate buffer pH = 6. Unanimously, the most successful chiral selector was the single isomeric heptakis-(6-sulfo)-β-cyclodextrin (HS-β-CD), which resulted in the highest enantioresolution values out of the CDs applied for five of the six enantiomeric pairs. The enantiomer migration order (EMO) was the same for two enantiomeric pairs, irrespective of the CD applied. However, several examples of EMO reversals were obtained in the other cases. Interestingly, changing from randomly substituted, multi-component mixtures of sulfated-β-CD to the single isomeric chiral selector, enantiomer migration order reversal occurred for two enantiomeric pairs and similar observations were made when comparing heptakis-(2,3-di-O-methyl-6-O-sulfo)-β-CD, (HDMS-β-CD) with HS-β-CD. In several cases, cavity-size-dependent, and substituent-dependent EMO reversals were also observed. Minute differences in the structure of the analytes were also responsible for several cases of EMO reversal. The present study offers a complex overview of the chiral separation of structurally related oxazolidinones, and thio-analogs, highlighting the importance of the adequate choice of chiral selector in this group of compounds, where enantiomeric purity is of utmost importance.

Nitrobenzoates and Nitrothiobenzoates with Activity against M. tuberculosis

Esters of weak acids have shown improved antimycobacterial activity over the corresponding free acids and nitro benzoates in particular have previously shown to have a very intriguing activity. To expand the potential of nitro-derivatives of benzoic acid as antimycobacterial drugs and explore the effects of various structural features on the activity of these compounds, we have obtained a library of 64 derivatives containing esters and thioesters of benzoates and studied their activity against M. tuberculosis, the stability of the compounds, their activation by mycobacterial enzymes and the potential cytotoxicity against human monocytic THP-1 cell line. Our results showed that the most active compounds are those with an aromatic nitro substitution, with the 3,5-dinitro esters series being the most active. Also, the greater antitubercular activity for the nitro derivatives was shown to be unrelated to their pKa values or hydrolysis rates. Given the conventional relationship between nitro-containing substances and toxicity, one might anticipate that the great antimicrobial activity of nitro compounds would be associated with high toxicity; yet, we have not found such a relationship. The nitrobenzoate scaffold, particularly the 3,5-dinitrobenzoate scaffold, merits further investigation, because it has the potential to generate future antimycobacterial agents with improved activity.