Enhancing Glucose Biosensing with Graphene Oxide and Ferrocene-Modified Linear Poly(ethylenimine).

We designed and optimized a glucose biosensor system based on a screen-printed electrode modified with the NAD-GDH enzyme. To enhance the electroactive surface area and improve the electron transfer efficiency, we introduced graphene oxide (GO) and ferrocene-modified linear poly(ethylenimine) (LPEI-Fc) onto the biosensor surface. This strategic modification exploits the electrostatic interaction between graphene oxide, which possesses a negative charge, and LPEI-Fc, which is positively charged. This interaction results in increased catalytic current during glucose oxidation and helps improve the overall glucose detection sensitivity by amperometry. We integrated the developed glucose sensor into a flow injection (FI) system. This integration facilitates a swift and reproducible detection of glucose, and it also mitigates the risk of contamination during the analyses. The incorporation of an FI system improves the efficiency of the biosensor, ensuring precise and reliable results in a short time. The proposed sensor was operated at a constant applied potential of 0.35 V. After optimizing the system, a linear calibration curve was obtained for the concentration range of 1.0-40 mM (R2 = 0.986). The FI system was successfully applied to determine the glucose content of a commercial sports drink.

AutoDock-SS: AutoDock for Multiconformational Ligand-Based Virtual Screening.

Ligand-based virtual screening (LBVS) can be pivotal for identifying potential drug leads, especially when the target protein's structure is unknown. However, current LBVS methods are limited in their ability to consider the ligand conformational flexibility. This study presents AutoDock-SS (Similarity Searching), which adapts protein-ligand docking for use in LBVS. AutoDock-SS integrates novel ligand-based grid maps and AutoDock-GPU into a novel three-dimensional LBVS workflow. Unlike other approaches based on pregenerated conformer libraries, AutoDock-SS's built-in conformational search optimizes conformations dynamically based on the reference ligand, thus providing a more accurate representation of relevant ligand conformations. AutoDock-SS supports two modes: single and multiple ligand queries, allowing for the seamless consideration of multiple reference ligands. When tested on the Directory of Useful Decoys─Enhanced (DUD-E) data set, AutoDock-SS surpassed alternative 3D LBVS methods, achieving a mean AUROC of 0.775 and an EF1% of 25.72 in single-reference mode. The multireference mode, evaluated on the augmented DUD-E+ data set, demonstrated superior accuracy with a mean AUROC of 0.843 and an EF1% of 34.59. This enhanced performance underscores AutoDock-SS's ability to treat compounds as conformationally flexible while considering the ligand's shape, pharmacophore, and electrostatic potential, expanding the potential of LBVS methods.

From intuition to AI: evolution of small molecule representations in drug discovery.

Within drug discovery, the goal of AI scientists and cheminformaticians is to help identify molecular starting points that will develop into safe and efficacious drugs while reducing costs, time and failure rates. To achieve this goal, it is crucial to represent molecules in a digital format that makes them machine-readable and facilitates the accurate prediction of properties that drive decision-making. Over the years, molecular representations have evolved from intuitive and human-readable formats to bespoke numerical descriptors and fingerprints, and now to learned representations that capture patterns and salient features across vast chemical spaces. Among these, sequence-based and graph-based representations of small molecules have become highly popular. However, each approach has strengths and weaknesses across dimensions such as generality, computational cost, inversibility for generative applications and interpretability, which can be critical in informing practitioners' decisions. As the drug discovery landscape evolves, opportunities for innovation continue to emerge. These include the creation of molecular representations for high-value, low-data regimes, the distillation of broader biological and chemical knowledge into novel learned representations and the modeling of up-and-coming therapeutic modalities.

SCORCH: Improving structure-based virtual screening with machine learning classifiers, data augmentation, and uncertainty estimation.

INTRODUCTION: The discovery of a new drug is a costly and lengthy endeavour. The computational prediction of which small molecules can bind to a protein target can accelerate this process if the predictions are fast and accurate enough. Recent machine-learning scoring functions re-evaluate the output of molecular docking to achieve more accurate predictions. However, previous scoring functions were trained on crystalised protein-ligand complexes and datasets of decoys. The limited availability of crystal structures and biases in the decoy datasets can lower the performance of scoring functions. OBJECTIVES: To address key limitations of previous scoring functions and thus improve the predictive performance of structure-based virtual screening. METHODS: A novel machine-learning scoring function was created, named SCORCH (Scoring COnsensus for RMSD-based Classification of Hits). To develop SCORCH, training data is augmented by considering multiple ligand poses and labelling poses based on their RMSD from the native pose. Decoy bias is addressed by generating property-matched decoys for each ligand and using the same methodology for preparing and docking decoys and ligands. A consensus of 3 different machine learning approaches is also used to improve performance. RESULTS: We find that multi-pose augmentation in SCORCH improves its docking power and screening power on independent benchmark datasets. SCORCH outperforms an equivalent scoring function trained on single poses, with a 1 % enrichment factor (EF) of 13.78 vs. 10.86 on 18 DEKOIS 2.0 targets and a mean native pose rank of 5.9 vs 30.4 on CSAR 2014. Additionally, SCORCH outperforms widely used scoring functions in virtual screening and pose prediction on independent benchmark datasets. CONCLUSION: By rationally addressing key limitations of previous scoring functions, SCORCH improves the performance of virtual screening. SCORCH also provides an estimate of its uncertainty, which can help reduce the cost and time required for drug discovery.

Ligand-based discovery of coronavirus main protease inhibitors using MACAW molecular embeddings.

Ligand-based drug design methods are thought to require large experimental datasets to become useful for virtual screening. In this work, we propose a computational strategy to design novel inhibitors of coronavirus main protease, Mpro. The pipeline integrates publicly available screening and binding affinity data in a two-stage machine-learning model using the recent MACAW embeddings. Once trained, the model can be deployed to rapidly screen large libraries of molecules in silico. Several hundred thousand compounds were virtually screened and 10 of them were selected for experimental testing. From these 10 compounds, 8 showed a clear inhibitory effect on recombinant Mpro, with half-maximal inhibitory concentration values (IC50) in the range 0.18-18.82 µM. Cellular assays were also conducted to evaluate cytotoxic, haemolytic, and antiviral properties. A promising lead compound against coronavirus Mpro was identified with dose-dependent inhibition of virus infectivity and minimal toxicity on human MRC-5 cells.

Experimental and In Silico Analysis of TEM ß-Lactamase Adaptive Evolution.

Multiple mutations often have non-additive (epistatic) phenotypic effects. Epistasis is of fundamental biological relevance but is not well understood mechanistically. Adaptive evolution, i.e., the evolution of new biochemical activities, is rich in epistatic interactions. To better understand the principles underlying epistasis during genetic adaptation, we studied the evolution of TEM-1 ß-lactamase variants exhibiting cefotaxime resistance. We report the collection of a library of 487 observed evolutionary trajectories for TEM-1 and determine the epistasis status based on cefotaxime resistance phenotype for 206 combinations of 2-3 TEM-1 mutations involving 17 positions under adaptive selective pressure. Gain-of-function (GOF) mutations are gatekeepers for adaptation. To see if GOF phenotypes can be inferred based solely on sequence data, we calculated the enrichment of GOF mutations in the different categories of epistatic pairs. Our results suggest that this is possible because GOF mutations are particularly enriched in sign and reciprocal sign epistasis, which leave a major imprint on the sequence space accessible to evolution. We also used FoldX to explore the relationship between thermodynamic stability and epistasis. We found that mutations in observed evolutionary trajectories tend to destabilize the folded structure of the protein, albeit their cumulative effects are consistently below the protein's free energy of folding. The destabilizing effect is stronger for epistatic pairs, suggesting that modest or local alterations in folding stability can modulate catalysis. Finally, we report a significant relationship between epistasis and the degree to which two protein positions are structurally and dynamically coupled, even in the absence of ligand.

A facile method for generating polypyrrole microcapsules and their application in electrochemical sensing.

A facile and rapid strategy to generate polypyrrole microcapsules is reported. The strategy is compatible with a vortex mixer and with a microfluidic chip for droplet generation, allowing a > 100-fold reduction in particle size. The sub-micron particle sizes obtained can also be tuned to some extent based on the chip geometry. The capsules can be kept stably in solution and can be transferred onto electrochemical devices. As an application example, we casted the polypyrrole capsules generated onto screen-printed electrodes, leading to a significant increase in their electroactive surface area and capacitance. The electrodes were further modified with glucose dehydrogenase (GDH) to fabricate glucose biosensors. The introduction of polypyrrole microcapsules increased the dynamic range of the glucose sensor to ca. 300% compared with that of the electrode without polypyrrole microcapsules. The resulting glucose sensor is operated at a constant applied potential of 0.20 V vs. Ag/AgCl (3 M KCl) in an air-equilibrated electrolyte. At this potential, the sensor showed a linear range from 1.0 to 9.0 mM glucose with a sensitivity of 3.23 µA cm-2 mM-1 (R2 = 0.993). The limit of detection obtained was 0.09 mM, and the reproducibility was 3.6%. The method allows generating polypyrrole microcapsules without surfactants or organic solvents and may enable new opportunities in the design of biosensors, electronic devices, and molecular delivery.

Combining DELs and machine learning for toxicology prediction.

DNA-encoded libraries (DELs) allow starting chemical matter to be identified in drug discovery. The volume of experimental data generated also makes DELs an attractive resource for machine learning (ML). ML allows modeling complex relationships between compounds and numerical endpoints, such as the binding to a target measured by DELs. DELs could also empower other areas of drug discovery. Here, we propose that DELs and ML could be combined to model binding to off-targets, enabling better predictive toxicology. With enough data, ML models can make accurate predictions across a vast chemical space, and they can be reused and expanded across projects. Although there are limitations, more general toxicology models could be applied earlier during drug discovery, illuminating safety liabilities at a lower cost.

MACAW: An Accessible Tool for Molecular Embedding and Inverse Molecular Design.

The growing capabilities of synthetic biology and organic chemistry demand tools to guide syntheses toward useful molecules. Here, we present Molecular AutoenCoding Auto-Workaround (MACAW), a tool that uses a novel approach to generate molecules predicted to meet a desired property specification (e.g., a binding affinity of 50 nM or an octane number of 90). MACAW describes molecules by embedding them into a smooth multidimensional numerical space, avoiding uninformative dimensions that previous methods often introduce. The coordinates in this embedding provide a natural choice of features for accurately predicting molecular properties, which we demonstrate with examples for cetane and octane numbers, flash points, and histamine H1 receptor binding affinity. The approach is computationally efficient and well-suited to the small- and medium-size datasets commonly used in biosciences. We showcase the utility of MACAW for virtual screening by identifying molecules with high predicted binding affinity to the histamine H1 receptor and limited affinity to the muscarinic M2 receptor, which are targets of medicinal relevance. Combining these predictive capabilities with a novel generative algorithm for molecules allows us to recommend molecules with a desired property value (i.e., inverse molecular design). We demonstrate this capability by recommending molecules with predicted octane numbers of 40, 80, and 120, which is an important characteristic of biofuels. Thus, MACAW augments classical retrosynthesis tools by providing recommendations for molecules on specification.

Comparison of ATP-binding pockets and discovery of homologous recombination inhibitors.

The ATP binding sites of many enzymes are structurally related, which complicates their development as therapeutic targets. In this work, we explore a diverse set of ATPases and compare their ATP binding pockets using different strategies, including direct and indirect structural methods, in search of pockets attractive for drug discovery. We pursue different direct and indirect structural strategies, as well as ligandability assessments to help guide target selection. The analyses indicate human RAD51, an enzyme crucial in homologous recombination, as a promising, tractable target. Inhibition of RAD51 has shown promise in the treatment of certain cancers but more potent inhibitors are needed. Thus, we design compounds computationally against the ATP binding pocket of RAD51 with consideration of multiple criteria, including predicted specificity, drug-likeness, and toxicity. The molecules designed are evaluated experimentally using molecular and cell-based assays. Our results provide two novel hit compounds against RAD51 and illustrate a computational pipeline to design new inhibitors against ATPases.

Electrodeposition of Cobalt Oxides on Carbon Nanotubes for Sensitive Bromhexine Sensing.

We develop an electrochemical sensor for the determination of bromhexine hydrochloride (BHC), a widely use mucolytic drug. The sensor is prepared by electrodeposition of cobalt oxides (CoOx) on a glassy carbon electrode modified with carboxylated single-walled carbon nanotubes (SWCNT). A synergistic effect between CoOx and SWCNT is observed, leading to a significant improvement in the BHC electrooxidation current. Based on cyclic voltammetry studies at varying scan rates, we conclude that the electrochemical oxidation of BHC is under mixed diffusion-adsorption control. The proposed sensor allows the amperometric determination of BHC in a linear range of 10-500 µM with a low applied voltage of 0.75 V. The designed sensor provides reproducible measurements, is not affected by common interfering substances, and shows excellent performance for the analysis of BHC in pharmaceutical preparations.

Development of a Sensitive Self-Powered Glucose Biosensor Based on an Enzymatic Biofuel Cell.

Biofuel cells allow for constructing sensors that leverage the specificity of enzymes without the need for an external power source. In this work, we design a self-powered glucose sensor based on a biofuel cell. The redox enzymes glucose dehydrogenase (NAD-GDH), glucose oxidase (GOx), and horseradish peroxidase (HRP) were immobilized as biocatalysts on the electrodes, which were previously engineered using carbon nanostructures, including multi-wall carbon nanotubes (MWCNTs) and reduced graphene oxide (rGO). Additional polymers were also introduced to improve biocatalyst immobilization. The reported design offers three main advantages: (i) by using glucose as the substrate for the both anode and cathode, a more compact and robust design is enabled, (ii) the system operates under air-saturating conditions, with no need for gas purge, and (iii) the combination of carbon nanostructures and a multi-enzyme cascade maximizes the sensitivity of the biosensor. Our design allows the reliable detection of glucose in the range of 0.1-7.0 mM, which is perfectly suited for common biofluids and industrial food samples.

Design of drug-like hepsin inhibitors against prostate cancer and kidney stones.

Hepsin, a transmembrane serine protease abundant in renal endothelial cells, is a promising therapeutic target against several cancers, particularly prostate cancer. It is involved in the release and polymerization of uromodulin in the urine, which plays a role in kidney stone formation. In this work, we design new potential hepsin inhibitors for high activity, improved specificity towards hepsin, and promising ADMET properties. The ligands were developed in silico through a novel hierarchical pipeline. This pipeline explicitly accounts for off-target binding to the related serine proteases matriptase and HGFA (human hepatocyte growth factor activator). We completed the pipeline incorporating ADMET properties of the candidate inhibitors into custom multi-objective optimization functions. The ligands designed show excellent prospects for targeting hepsin via the blood stream and the urine and thus enable key experimental studies. The computational pipeline proposed is remarkably cost-efficient and can be easily adapted for designing inhibitors against new drug targets.

Solving the Competitive Binding Equilibria between Many Ligands: Application to High-Throughput Screening and Affinity Optimization.

Modern small-molecule drug discovery relies on the selective targeting of biological macromolecules by low-molecular weight compounds. Therefore, the binding affinities of candidate drugs to their targets are key for pharmacological activity and clinical use. For drug discovery methods where multiple drug candidates can simultaneously bind to the same target, a competition is established, and the resulting equilibrium depends on the dissociation constants and concentration of all the species present. Such coupling between all equilibrium-governing parameters complicates analysis and development of improved mixture-based, high-throughput drug discovery techniques. In this work, we present an iterative computational algorithm to solve coupled equilibria between an arbitrary number of ligands and a biomolecular target that is efficient and robust. The algorithm does not require the estimation of initial values to rapidly converge to the solution of interest. We explored binding equilibria under ligand/receptor conditions used in mixture-based library screening by affinity selection-mass spectrometry (AS-MS). Our studies support a facile method for affinity-ranking hits. The ranking method involves varying the receptor-to-ligand concentration ratio in a pool of candidate ligands in two sequential AS-MS analyses. The ranking is based on the relative change in bound ligand concentration. The method proposed does not require a known reference ligand and produces a ranking that is insensitive to variations in the concentration of individual compounds, thereby enabling the use of unpurified compounds generated by mixture-based combinatorial synthesis techniques.

High-Throughput Screening: today's biochemical and cell-based approaches.

High-throughput screening (HTS) provides starting chemical matter in the adventure of developing a new drug. In this review, we survey several HTS methods used today for hit identification, organized in two main flavors: biochemical and cell-based assays. Biochemical assays discussed include fluorescence polarization and anisotropy, FRET, TR-FRET, and fluorescence lifetime analysis. Binding-based methods are also surveyed, including NMR, SPR, mass spectrometry, and DSF. On the other hand, cell-based assays discussed include viability, reporter gene, second messenger, and high-throughput microscopy assays. We devote some emphasis to high-content screening, which is becoming very popular. An advisable stage after hit discovery using phenotypic screens is target deconvolution, and we provide an overview of current chemical proteomics, in silico, and chemical genetics tools. Emphasis is made on recent CRISPR/dCas-based screens. Lastly, we illustrate some of the considerations that inform the choice of HTS methods and point to some areas with potential interest for future research.

Separation of air components and pollutants by the Earth's gravitational field.

In this work, we develop a model able to predict the equilibrium separation of gases due to differences in their molecular weights and the action of gravity. The separation of H, He, O, and N2 with altitude is a characteristic phenomenon of the heterosphere. The model is able to qualitatively recreate the compositional profile of the whole heterosphere from a single composition measurement. The model is applied to the separation of air components and pollutants by empty wells drilled on the planet surface. It predicts that the separation of gases would be possible in wells deep enough under equilibrium. The high molecular weight of some anthropogenic pollutants (SO2, O3, NO2, CO2, etc.) would facilitate their segregation along shorter distances compared to those involved in the heterosphere. The simulations indicate that deep wells could concentrate some air components and pollutants by orders of magnitude over the levels at the Earth's surface without external energy input. For instance, argon molar fractions of >40% and >60% could be achievable at 44 km and 55 km depth, respectively. Finally, we discuss the feasibility of gravitational separation as a potential pollution abatement technology.

Perturbation Theory-Machine Learning Study of Zeolite Materials Desilication.

Zeolites are important materials for research and industrial applications. Mesopores are often introduced by desilication but other properties are also affected, making its optimization difficult. In this work, we demonstrate that Perturbation Theory and Machine Learning can be combined in a PTML multioutput model describing the effects of desilication. The PTML model achieves a notable accuracy ( R2 = 0.98) in the external validation and can be useful for the rational design of novel materials.