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The intricate involvement of Peroxisome Proliferator-Activated Receptor Gamma (PPARγ) in glucose homeostasis and adipogenesis is well-established. However, its role in cancer, particularly luminal bladder cancer, remains debated. The overexpression and activation of PPARγ are implicated in tumorigenesis. Specific gain-of-function mutations (M280I, I290M, and T475M) within the ligand-binding domain of PPARγ are associated with bladder cancer and receptor activation. The underlying molecular pathways prompted by these mutations remain unclear. We employed a dual-basin structure-based model (db-SBM) to explore the conformational dynamics between the inactive and active states of PPARγ and examined the effects of the M280I, I290M, and T475M mutations. Our findings, consistent with the existing literature, reveal heightened ligand-independent transcriptional activity in the I290M and T475M mutants. Both mutants showed enhanced stabilization of the active state compared to the wild-type receptor, with the I290M mutation promoting a specific transition route, making it a prime candidate for further study. Electrostatic analysis identified residues K303 and E488 as pivotal in the I290M activation cascade. Biophysical assays confirmed that disrupting the K303–E488 interaction reduced the thermal stabilization characteristic of the I290M mutation. Our study demonstrates the predictive capabilities of combining simulation and cheminformatics methods, validated by biochemical experiments, to gain insights into molecular activation mechanisms and identify target residues for protein modulation. [ABSTRACT FROM AUTHOR]

Molecular machine learning (ML) has proven important for tackling various molecular problems, such as predicting molecular properties based on molecular descriptors or fingerprints. Since relatively recently, graph neural network (GNN) algorithms have been implemented for molecular ML, showing comparable or superior performance to descriptor or fingerprint-based approaches. Although various tools and packages exist to apply GNNs in molecular ML, a new GNN package, named MolGraph, was developed in this work with the motivation to create GNN model pipelines highly compatible with the TensorFlow and Keras application programming interface (API). MolGraph also implements a module to accommodate the generation of small molecular graphs, which can be passed to a GNN algorithm to solve a molecular ML problem. To validate the GNNs, benchmarking was conducted using the datasets from MoleculeNet, as well as three chromatographic retention time datasets. The benchmarking results demonstrate that the GNNs performed in line with expectations. Additionally, the GNNs proved useful for molecular identification and improved interpretability of chromatographic retention time data. MolGraph is available at https://github.com/akensert/molgraph. Installation, tutorials and implementation details can be found at https://molgraph.readthedocs.io/en/latest/. [ABSTRACT FROM AUTHOR]

The domain of computational chemistry has experienced a significant evolution due to the introduction of Machine Learning (ML) technologies. Despite its potential to revolutionize the field, researchers are often encumbered by obstacles, such as the complexity of selecting optimal algorithms, the automation of data pre-processing steps, the necessity for adaptive feature engineering, and the assurance of model performance consistency across different datasets. Addressing these issues head-on, DeepMol stands out as an Automated ML (AutoML) tool by automating critical steps of the ML pipeline. DeepMol rapidly and automatically identifies the most effective data representation, pre-processing methods and model configurations for a specific molecular property/activity prediction problem. On 22 benchmark datasets, DeepMol obtained competitive pipelines compared with those requiring time-consuming feature engineering, model design and selection processes. As one of the first AutoML tools specifically developed for the computational chemistry domain, DeepMol stands out with its open-source code, in-depth tutorials, detailed documentation, and examples of real-world applications, all available at https://github.com/BioSystemsUM/DeepMol and https://deepmol.readthedocs.io/en/latest/. By introducing AutoML as a groundbreaking feature in computational chemistry, DeepMol establishes itself as the pioneering state-of-the-art tool in the field. Scientific contribution DeepMol aims to provide an integrated framework of AutoML for computational chemistry. DeepMol provides a more robust alternative to other tools with its integrated pipeline serialization, enabling seamless deployment using the fit, transform, and predict paradigms. It uniquely supports both conventional and deep learning models for regression, classification and multi-task, offering unmatched flexibility compared to other AutoML tools. DeepMol's predefined configurations and customizable objective functions make it accessible to users at all skill levels while enabling efficient and reproducible workflows. Benchmarking on diverse datasets demonstrated its ability to deliver optimized pipelines and superior performance across various molecular machine-learning tasks. [ABSTRACT FROM AUTHOR]

Building reliable and robust quantitative structure–property relationship (QSPR) models is a challenging task. First, the experimental data needs to be obtained, analyzed and curated. Second, the number of available methods is continuously growing and evaluating different algorithms and methodologies can be arduous. Finally, the last hurdle that researchers face is to ensure the reproducibility of their models and facilitate their transferability into practice. In this work, we introduce QSPRpred, a toolkit for analysis of bioactivity data sets and QSPR modelling, which attempts to address the aforementioned challenges. QSPRpred's modular Python API enables users to intuitively describe different parts of a modelling workflow using a plethora of pre-implemented components, but also integrates customized implementations in a "plug-and-play" manner. QSPRpred data sets and models are directly serializable, which means they can be readily reproduced and put into operation after training as the models are saved with all required data pre-processing steps to make predictions on new compounds directly from SMILES strings. The general-purpose character of QSPRpred is also demonstrated by inclusion of support for multi-task and proteochemometric modelling. The package is extensively documented and comes with a large collection of tutorials to help new users. In this paper, we describe all of QSPRpred's functionalities and also conduct a small benchmarking case study to illustrate how different components can be leveraged to compare a diverse set of models. QSPRpred is fully open-source and available at https://github.com/CDDLeiden/QSPRpred. Scientific Contribution QSPRpred aims to provide a complex, but comprehensive Python API to conduct all tasks encountered in QSPR modelling from data preparation and analysis to model creation and model deployment. In contrast to similar packages, QSPRpred offers a wider and more exhaustive range of capabilities and integrations with many popular packages that also go beyond QSPR modelling. A significant contribution of QSPRpred is also in its automated and highly standardized serialization scheme, which significantly improves reproducibility and transferability of models. [ABSTRACT FROM AUTHOR]

Over the past ~ 25 years, chemoinformatics has evolved as a scientific discipline, with a strong foundation in pharmaceutical research and scientific roots that can be traced back to the late 1950s. It covers a wide methodological spectrum and is perhaps best positioned in the greater context of chemical information science. Herein, the chemoinformatics discipline is delineated, characteristic (and partly problematic) features are discussed, and a global view of the field is provided, emphasizing key developments. [ABSTRACT FROM AUTHOR]

Approaches based on graph descriptors have been used in cheminformatics and bioinformatics for molecular property prediction. In this article, we apply partition technique and simple counting schemes to describe the structure of a two-dimensional (2D) kagome graphene and then derive the closed form of various degree-based topological indices (graph descriptor) of 2D kagome graphene, and its tubular and toroidal forms. [ABSTRACT FROM AUTHOR]

Determining whether a molecule can be synthesized is crucial in chemistry and drug discovery, as it guides experimental prioritization and molecule ranking in de novo design tasks. Existing scoring approaches to assess synthetic feasibility struggle to extrapolate to new chemical spaces or fail to discriminate based on subtle differences such as chirality. This work addresses these limitations by introducing the Focused Synthesizability score (FSscore), which uses machine learning to rank structures based on their relative ease of synthesis. First, a baseline trained on an extensive set of reactant‐product pairs is established, which is then refined with expert human feedback tailored to specific chemical spaces. This targeted fine‐tuning improves performance on these chemical scopes, enabling more accurate differentiation between molecules that are hard and easy to synthesize. The FSscore showcases how a human‐in‐the‐loop framework can be utilized to optimize the assessment of synthetic feasibility for various chemical applications. [ABSTRACT FROM AUTHOR]

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Hitherto virtual screening (VS) has been typically performed using a structure-based drug design paradigm. Such methods typically require the use of molecular docking on high-resolution three-dimensional structures of a target protein—a computationally-intensive and time-consuming exercise. This work demonstrates that by employing protein language models and molecular graphs as inputs to a novel graph-to-transformer cross-attention mechanism, a screening power comparable to state-of-the-art structure-based models can be achieved. The implications thereof include highly expedited VS due to the greatly reduced compute required to run this model, and the ability to perform early stages of computer-aided drug design in the complete absence of 3D protein structures. [ABSTRACT FROM AUTHOR]

Background/Objectives: Drug-Induced Kidney Injury (DIKI) presents a significant challenge in drug development, often leading to clinical-stage failures. The early prediction of DIKI risk can improve drug safety and development efficiency. Existing models tend to focus on physicochemical properties alone, often overlooking drug–target interactions crucial for DIKI. This study introduces an AI/ML (artificial intelligence/machine learning) model that integrates both physicochemical properties and off-target interactions to enhance DIKI prediction. Methods: We compiled a dataset of 360 FDA-classified compounds (231 non-nephrotoxic and 129 nephrotoxic) and predicted 6064 off-target interactions, 59% of which were validated in vitro. We also calculated 55 physicochemical properties for these compounds. Machine learning (ML) models were developed using four algorithms: Ridge Logistic Regression (RLR), Support Vector Machine (SVM), Random Forest (RF), and Neural Network (NN). These models were then combined into an ensemble model for enhanced performance. Results: The ensemble model achieved an ROC-AUC of 0.86, with a sensitivity and specificity of 0.79 and 0.78, respectively. The key predictive features included 38 off-target interactions and physicochemical properties such as the number of metabolites, polar surface area (PSA), pKa, and fraction of Sp3-hybridized carbons (fsp3). These features effectively distinguished DIKI from non-DIKI compounds. Conclusions: The integrated model, which combines both physicochemical properties and off-target interaction data, significantly improved DIKI prediction accuracy compared to models that rely on either data type alone. This AI/ML model provides a promising early screening tool for identifying compounds with lower DIKI risk, facilitating safer drug development. [ABSTRACT FROM AUTHOR]

Skin sensitization is a significant concern for chemical safety assessments. Traditional animal assays often fail to predict human responses accurately, and ethical constraints limit the collection of human data, necessitating a need for reliable in silico models of skin sensitization prediction. This study introduces HuSSPred, an in silico tool based on the Human Predictive Patch Test (HPPT). HuSSPred aims to enhance the reliability of predicting human skin sensitization effects for chemical agents to support their regulatory assessment. We have curated an extensive HPPT database and performed chemical space analysis and grouping. Binary and multiclass QSAR models were developed with Bayesian hyperparameter optimization. Model performance was evaluated via five-fold cross-validation. We performed model validation with reference data from the Defined Approaches for Skin Sensitization (DASS) app. HuSSPred models demonstrated strong predictive performance with CCR ranging from 55 to 88%, sensitivity between 48 and 89%, and specificity between 37 and 92%. The positive predictive value (PPV) ranged from 84 to 97%, versus negative predictive value (NPV) from 22 to 65%, and coverage was between 75 and 93%. Our models exhibited comparable or improved performance compared to existing tools, and the external validation showed the high accuracy and sensitivity of the developed models. HuSSPred provides a reliable, open-access, and ethical alternative to traditional testing for skin sensitization. Its high accuracy and reasonable coverage make it a valuable resource for regulatory assessments, aligning with the 3Rs principles. The publicly accessible HuSSPred web tool offers a user-friendly interface for predicting skin sensitization based on chemical structure. [ABSTRACT FROM AUTHOR]

This research delves into a comprehensive investigation of a specific group of alkanes, with the primary objective of establishing meaningful associations between their inherent physical attributes and a set of graphical parameters ranging from topological indices to their associated graphical entropies. Though there are countless techniques to relate molecular structure and physical properties of chemical compounds, the pursuit has often been pricey and arduous. With a view to curb the expense and intense labour, this work focuses on attaining some of these properties using graphical interpretations and statistical interventions. Driven by this objective, we compute Sombor index, a recently developed topological tool and some of its variants for all structural isomers of alkanes with carbon range four to nine. Furthermore, our study employs multiple regression analysis to explore their connections with some physical attributes of alkanes while concurrently addressing the challenge posed by multicollinearity. We have adopted the technique of robust regression to scale down the impact of outliers in the dataset, while generating regression models. Further, the established results are justified with a 10-fold cross validation process and a comparison with the results obtained from different topological indices. The results of this research contribute a valuable insight to the fields of chemical informatics and structural analysis, offering an enhanced comprehension of the interplay between molecular structure and physical attributes within alkanes. [ABSTRACT FROM AUTHOR]

This paper reviews the application of machine learning to the inhibition of corrosion by organic molecules. The methodologies considered include quantitative structure‐property relationships (QSPR) and related data‐driven approaches. The characteristic features of their key components are considered as applied to corrosion inhibition, including datasets, response properties, molecular descriptors, machine learning methods, and structure‐property models. It is shown that the most important factors determining their choice and application features are: (1) the small or very small size of datasets, (2) the mechanism of corrosion inhibition associated with the adsorption of inhibitor molecules on the metal surface, and (3) multifactorial conditioning and noisiness of response property. On this basis, the application of machine learning to the inhibition of corrosion of materials based on iron, aluminum, and magnesium is considered. The main trends in the development of QSPR and related data‐driven modeling of corrosion inhibition are discussed, the shortcomings and common errors are considered, and the prospects for their further development are outlined. [ABSTRACT FROM AUTHOR]

Neural processes (NPs) are models for meta-learning which output uncertainty estimates. So far, most studies of NPs have focused on low-dimensional datasets of highly-correlated tasks. While these homogeneous datasets are useful for benchmarking, they may not be representative of realistic transfer learning. In particular, applications in scientific research may prove especially challenging due to the potential novelty of meta-testing tasks. Molecular property prediction is one such research area that is characterized by sparse datasets of many functions on a shared molecular space. In this paper, we study the application of graph NPs to molecular property prediction with DOCKSTRING, a diverse dataset of docking scores. Graph NPs show competitive performance in few-shot learning tasks relative to supervised learning baselines common in chemoinformatics, as well as alternative techniques for transfer learning and meta-learning. In order to increase meta-generalization to divergent test functions, we propose fine-tuning strategies that adapt the parameters of NPs. We find that adaptation can substantially increase NPs' regression performance while maintaining good calibration of uncertainty estimates. Finally, we present a Bayesian optimization experiment which showcases the potential advantages of NPs over Gaussian processes in iterative screening. Overall, our results suggest that NPs on molecular graphs hold great potential for molecular property prediction in the low-data setting. Scientific contribution: Neural processes are a family of meta-learning algorithms which deal with data scarcity by transferring information across tasks and making probabilistic predictions. We evaluate their performance on regression and optimization molecular tasks using docking scores, finding them to outperform classical single-task and transfer-learning models. We examine the issue of generalization to divergent test tasks, which is a general concern of meta-learning algorithms in science, and propose strategies to alleviate it. [ABSTRACT FROM AUTHOR]

Antimicrobial peptides (AMPs) are promising cationic and amphipathic molecules to fight antibiotic resistance. To search for novel AMPs, we applied a computational strategy to identify peptide sequences within the organisms' proteome, including in-house developed software and artificial intelligence tools. After analyzing 150.450 proteins from eight proteomes of bacteria, plants, a protist, and a nematode, nine peptides were selected and modified to increase their antimicrobial potential. The 18 resulting peptides were validated by bioassays with four pathogenic bacterial species, one yeast species, and two cancer cell-lines. Fourteen of the 18 tested peptides were antimicrobial, with minimum inhibitory concentrations (MICs) values under 10 µM against at least three bacterial species; seven were active against Candida albicans with MICs values under 10 µM; six had a therapeutic index above 20; two peptides were active against A549 cells, and eight were active against MCF-7 cells under 30 µM. This study's most active antimicrobial peptides damage the bacterial cell membrane, including grooves, dents, membrane wrinkling, cell destruction, and leakage of cytoplasmic material. The results confirm that the proposed approach, which uses bioinformatic tools and rational modifications, is highly efficient and allows the discovery, with high accuracy, of potent AMPs encrypted in proteins. [ABSTRACT FROM AUTHOR]

Substances of unknown or variable composition, complex reaction products, and biological materials (UVCBs) are commonly found in the environment. However, assessing their human toxicological risk is challenging due to their variable composition and many constituents. Metal naphthenate salts are one such category of UVCBs that are the reaction products of naphthenic acids with metals to form complex mixtures. Metal naphthenates are often found or used in household and industrial materials with potential for human exposure, but very few of these materials have been evaluated for causing human health hazards. Herein, we evaluate metal naphthenates using predictions derived from read-across and quantitative structure-activity/property relationship (QSAR/QSPR) models. Accordingly, we first built a computational chemistry library by enumerating the structures of naphthenic acids and derived 11,850 QSAR-acceptable structures; then, we used open and commercial in silico tools on these structures to predict a set of physicochemical properties and toxicity endpoints. We then compared the QSAR/QSPR predictions with available experimental data on naphthenic acids to provide a more complete picture of the contributions of the components to the toxicity profiles of metal naphthenate mixtures. The available systematic acute oral toxicity values (LD50) and QSAR LD50 predictions of all the naphthenic acid components indicated low concern for toxicity. The point of departure predictions for chronic repeated dose toxicity for the naphthenic acid components using QSAR models developed from studies on rats ranged from 25 to 50 mg/kg/day. These values are in good agreement with findings fromstudies on copper and zinc naphthenates, which had no observed adverse effect levels of 30 and 118 mg/kg/day, respectively. Hence, this study demonstrates how published in silico approaches can be used to identify the potential components of metal naphthenates for further testing, inform groupings of UVCBs such as naphthenates, as well as fill the data gaps using read-across and QSAR models to inform risk assessment. [ABSTRACT FROM AUTHOR]

Focused screening on target-prioritized compound sets can be an efficient alternative to high throughput screening (HTS). For most biomolecular targets, compound prioritization models depend on prior screening data or a target structure. For phenotypic or multi-protein pathway targets, it may not be clear which public assay records provide relevant data. The question also arises as to whether data collected from disparate assays might be usefully consolidated. Here, we report on the development and application of a data mining pipeline to examine these issues. To illustrate, we focus on identifying inhibitors of oxidative phosphorylation, a druggable metabolic process in epithelial ovarian tumors. The pipeline compiled 8415 available OXPHOS-related bioassays in the PubChem data repository involving 312,093 unique compound records. Application of PubChem assay activity annotations, PAINS (Pan Assay Interference Compounds), and Lipinski-like bioavailability filters yields 1852 putative OXPHOS-active compounds that fall into 464 clusters. These chemotypes are diverse but have relatively high hydrophobicity and molecular weight but lower complexity and drug-likeness. These chemotypes show a high abundance of bicyclic ring systems and oxygen containing functional groups including ketones, allylic oxides (alpha/beta unsaturated carbonyls), hydroxyl groups, and ethers. In contrast, amide and primary amine functional groups have a notably lower than random prevalence. UMAP representation of the chemical space shows strong divergence in the regions occupied by OXPHOS-inactive and -active compounds. Of the six compounds selected for biological testing, 4 showed statistically significant inhibition of electron transport in bioenergetics assays. Two of these four compounds, lacidipine and esbiothrin, increased in intracellular oxygen radicals (a major hallmark of most OXPHOS inhibitors) and decreased the viability of two ovarian cancer cell lines, ID8 and OVCAR5. Finally, data from the pipeline were used to train random forest and support vector classifiers that effectively prioritized OXPHOS inhibitory compounds within a held-out test set (ROCAUC 0.962 and 0.927, respectively) and on another set containing 44 documented OXPHOS inhibitors outside of the training set (ROCAUC 0.900 and 0.823). This prototype pipeline is extensible and could be adapted for focus screening on other phenotypic targets for which sufficient public data are available. Scientific contribution Here, we describe and apply an assay data mining pipeline to compile, process, filter, and mine public bioassay data. We believe the procedure may be more broadly applied to guide compound selection in early-stage hit finding on novel multi-protein mechanistic or phenotypic targets. To demonstrate the utility of our approach, we apply a data mining strategy on a large set of public assay data to find drug-like molecules that inhibit oxidative phosphorylation (OXPHOS) as candidates for ovarian cancer therapies. [ABSTRACT FROM AUTHOR]

This article aims to provide a comprehensive critical, yet readable, review of general interest to the chemistry community on molecular similarity as applied to chemical informatics and predictive modeling with a special focus on read-across (RA) and read-across structure–activity relationships (RASAR). Molecular similarity-based computational tools, such as quantitative structure–activity relationships (QSARs) and RA, are routinely used to fill the data gaps for a wide range of properties including toxicity endpoints for regulatory purposes. This review will explore the background of RA starting from how structural information has been used through to how other similarity contexts such as physicochemical, absorption, distribution, metabolism, and elimination (ADME) properties, and biological aspects are being characterized. More recent developments of RA's integration with QSAR have resulted in the emergence of novel models such as ToxRead, generalized read-across (GenRA), and quantitative RASAR (q-RASAR). Conventional QSAR techniques have been excluded from this review except where necessary for context. [ABSTRACT FROM AUTHOR]

The article discusses the creation of the World Wide Approved Drug (WWAD) database, which aims to provide comprehensive information on medicines approved for therapeutic use worldwide. The database collects data from National Medicine Registers (NMRs) and official documentation from medicines regulatory authorities (MRAs) of different countries. The article emphasizes the need for a unified resource that aggregates information on worldwide approved medicines and highlights the challenges in accessing and processing this data. The World Wide Approved Drugs (WWAD) database is a comprehensive resource that provides information on low molecular weight pharmaceutical substances from 71 countries and regions. It includes data on drug names, chemical structures, therapeutic indications, molecular targets, and biological activities. The database can be used for various purposes, including searching for information on approved drugs, drug repositioning, data enrichment, and assessing the novelty of investigated compounds. The WWAD database is freely available for academic use through the Way2Drug platform. This document contains a list of references and citations from various scientific articles and databases related to topics such as computational chemistry, drug discovery, drug repurposing, and regulatory approval of drugs. The references cover a range of subjects including the use of artificial intelligence and machine learning in drug discovery, the development of databases for drug repurposing, and the analysis of drug approvals by regulatory agencies. The document provides a comprehensive list of sources that library patrons can use to further their research on these topics. [Extracted from the article]

The increasing prevalence of diabetes and dyslipidemia poses significant health challenges, impacting millions of people globally and leading to high rates of illness and death. This study aimed to explore the potential antidiabetic and hypolipidemic effects of Plu kaow (Houttuynia cordata Thunb.) ethanolic extract (PK) in streptozotocin (STZ) induced diabetic rats, focusing on its molecular mechanisms. Diabetes was induced in fasting Long Evans rats using streptozotocin (65 mg/kg b. w.), with glibenclamide (5 mg/kg/day) used as the standard experimental drug. The treated groups received oral supplementation of PK (500 mg/kg/day) for 28 days. The study evaluated blood glucose levels, lipid status, body weight, liver, kidney, and heart function biomarkers, antioxidant activity, and histological examination of various organs. Additionally, untargeted metabolomics, cheminformatics, and molecular docking were employed to elucidate the probable mechanisms of action of PK. Based on metabolomic profiling data, the PK was found to contain various putative antidiabetic agents such as kaempferol 7-neohesperidoside, isochlorogenic acid C, rutin, datiscin, and diosmin and they have been proposed to significantly (p < 0.001) reduce blood glucose levels and modulated hyperlipidemia. PK also improved the tested liver, kidney, and heart function biomarkers and reversed damage to normal pancreatic, liver, kidney, and heart cells in histological analysis. In conclusion, PK shows promise as a potential treatment or management option for diabetes and hyperlipidemia, as well as their associated complications in diabetic rats. [ABSTRACT FROM AUTHOR]

The Glycine Transporter Type 1 (GlyT1) significantly impacts central nervous system functions, influencing glycinergic and glutamatergic neurotransmission. Bitopertin, the first GlyT1 inhibitor in clinical trials, was developed for schizophrenia treatment but showed limited efficacy. Despite this, bitopertin's repositioning could advance treating various pathologies. This study aims to understand bitopertin's mechanism of action using computational methods, exploring off-target effects, and providing a comprehensive pharmacological profile. Similarity Ensemble Approach (SEA) and SwissTargetPrediction initially predicted targets, followed by molecular modeling on SWISS-MODEL and GalaxyWeb servers. Binding sites were identified using PrankWeb, and molecular docking was performed with DockThor and GOLD software. Molecular dynamics analyses were conducted on the Visual Dynamics platform. Reverse screening on SEA and SwissTargetPrediction identified GlyT1 (SLC6A9), GlyT2 (SLC6A5), PROT (SLC6A7), and DAT (SLC6A3) as potential bitopertin targets. Homology modeling on SwissModel generated high-resolution models, optimized further on GalaxyWeb. PrankWeb identified similar binding sites in GlyT1, GlyT2, PROT, and DAT, indicating potential interaction. Docking studies suggested bitopertin's interaction with GlyT1 and proximity to GlyT2 and PROT. Molecular dynamics confirmed docking results, highlighting bitopertin's target stability beyond GlyT1. The study concludes that bitopertin potentially interacts with multiple SLC6 family targets, indicating a broader pharmacological property. [ABSTRACT FROM AUTHOR]

Background: Tyrosyl-DNA phosphodiesterase 1 (Tdp1) repairs damages in DNA induced by abortive topoisomerase 1 activity; however, maintenance of genetic integrity may sustain cellular division of neoplastic cells. It follows that Tdp1-targeting chemical inhibitors could synergize well with existing chemotherapy drugs to deny cancer growth; therefore, identification of Tdp1 inhibitors may advance precision medicine in oncology. Objective: Current computational research efforts focus primarily on molecular docking simulations, though datasets involving three-dimensional molecular structures are often hard to curate and computationally expensive to store and process. We propose the use of simplified molecular input line entry system (SMILES) chemical representations to train supervised machine learning (ML) models, aiming to predict potential Tdp1 inhibitors. Methods: An open-sourced consensus dataset containing the inhibitory activity of numerous chemicals against Tdp1 was obtained from Kaggle. Various ML algorithms were trained, ranging from simple algorithms to ensemble methods and deep neural networks. For algorithms requiring numerical data, SMILES were converted to chemical descriptors using RDKit, an open-sourced Python cheminformatics library. Results: Out of 13 optimized ML models with rigorously tuned hyperparameters, the random forest model gave the best results, yielding a receiver operating characteristics-area under curve of 0.7421, testing accuracy of 0.6815, sensitivity of 0.6444, specificity of 0.7156, precision of 0.6753, and F1 score of 0.6595. Conclusions: Ensemble methods, especially the bootstrap aggregation mechanism adopted by random forest, outperformed other ML algorithms in classifying Tdp1 inhibitors from non-inhibitors using SMILES. The discovery of Tdp1 inhibitors could unlock more treatment regimens for cancer patients, allowing for therapies tailored to the patient's condition. [ABSTRACT FROM AUTHOR]

Atom-to-atom maps (AAMs) are bijections that establish the correspondence of reactant and product atoms across chemical reactions. They capture crucial features of the reaction mechanism and thus play a central role in modeling chemistry at the level of graph transformations. AAMs are equivalent to so-called "imaginary transition state" (ITS) graphs, making it possible to reduce tasks such as the computational comparison of AAMs to testing graph isomorphisms. In many application scenarios, nonetheless, only partial information is available, i.e., only partial maps or, equivalently, only subgraphs of the ITS graphs, are known. Here, we investigate whether and how, and to what extent, such partial chemical data can be completed and compared. The focus of this contribution is entirely on the development of a solid mathematical foundation for the analysis of partial AAMs and their associated partial ITS graphs. [ABSTRACT FROM AUTHOR]

Natural products have long been an important source of inspiration for medicinal chemistry and drug discovery. In the cosmetic field, they remain the major elements of the composition and serve as marketing asset. Recent research showed the implication of salt‐inducible kinases on the melanin production in skin via MITF regulation. Finding new potent modulators on such target could open the way to several cosmetic applications to attenuate visible signs of photoaging and improve the tan without sun. Since virtual screening can be a powerful tool for detecting hit compounds in the early stages of a drug discovery process, we applied this method on salt‐inducible kinase 2 to discover potential interesting compounds. Here, we present the different steps from the construction of a database of natural products, to the validation of a docking protocol and the results of the virtual screening. Hits from the screening were tested in vitro to confirm their efficiency and results are discussed. [ABSTRACT FROM AUTHOR]

This study explores the application of fine-tuned large language models for predicting physicochemical properties, specifically focusing on Abraham model solute descriptors (E, S, A, B, V) and modified solvent parameters (e0, s0, a0, b0, v0). By leveraging ChemLLaMA, a specialized version of the LLaMA model for cheminformatics tasks, we developed the AbraLlama-Solvent and AbraLlama-Solute models using curated datasets of experimentally derived solute descriptors and solvent parameters. Our findings demonstrate that AbraLlama-Solvent and AbraLlama-Solute predict modified solvent parameters and solute descriptors with high accuracy, comparable to existing methods. The AbraLlama-Solvent model shows varying prediction accuracy across different solvents, influenced by their position within the chemical space, while the AbraLlama-Solute model consistently predicts solute descriptors with high accuracy. Both models are available as applications on Hugging Face, facilitating easy predictions from SMILES strings. This research highlights the potential of LLMs in chemistry applications, offering practical tools for solvent comparison and expanding the applicability of Abraham solvation equations to a broader range of organic solvents. [ABSTRACT FROM AUTHOR]

Beyond addressing technological demands, the integration of machine learning (ML) into human societies has also promoted sustainability through the adoption of digitalized protocols. Despite these advantages and the abundance of available toolkits, a substantial implementation gap is preventing the widespread incorporation of ML protocols into the computational and experimental chemistry communities. In this work, we introduce ROBERT, a software carefully crafted to make ML more accessible to chemists of all programming skill levels, while achieving results comparable to those of field experts. We conducted benchmarking using six recent ML studies in chemistry containing from 18 to 4149 entries. Furthermore, we demonstrated the program's ability to initiate workflows directly from SMILES strings, which simplifies the generation of ML predictors for common chemistry problems. To assess ROBERT's practicality in real‐life scenarios, we employed it to discover new luminescent Pd complexes with a modest dataset of 23 points, a frequently encountered scenario in experimental studies. [ABSTRACT FROM AUTHOR]

The exponential growth of data is challenging for humans because their ability to analyze data is limited. Especially in chemistry, there is a demand for tools that can visualize molecular datasets in a convenient graphical way. We propose a new, ready-to-use, multi-tool, and open-source framework for visualizing and navigating chemical space. This framework adheres to the low-code/no-code (LCNC) paradigm, providing a KNIME node, a web-based tool, and a Python package, making it accessible to a broad cheminformatics community. The core technique of the MolCompass framework employs a pre-trained parametric t-SNE model. We demonstrate how this framework can be adapted for the visualisation of chemical space and visual validation of binary classification QSAR/QSPR models, revealing their weaknesses and identifying model cliffs. All parts of the framework are publicly available on GitHub, providing accessibility to the broad scientific community. Scientific contribution We provide an open-source, ready-to-use set of tools for the visualization of chemical space. These tools can be insightful for chemists to analyze compound datasets and for the visual validation of QSAR/QSPR models. [ABSTRACT FROM AUTHOR]

Marine natural products (MNPs) continue to be tested primarily in cellular toxicity assays, both mammalian and microbial, despite most being inactive at concentrations relevant to drug discovery. These MNPs become missed opportunities and represent a wasteful use of precious bioresources. The use of cheminformatics aligned with published bioactivity data can provide insights to direct the choice of bioassays for the evaluation of new MNPs. Cheminformatics analysis of MNPs found in MarinLit (n = 39,730) up to the end of 2023 highlighted indol-3-yl-glyoxylamides (IGAs, n = 24) as a group of MNPs with no reported bioactivities. However, a recent review of synthetic IGAs highlighted these scaffolds as privileged structures with several compounds under clinical evaluation. Herein, we report the synthesis of a library of 32 MNP-inspired brominated IGAs (25–56) using a simple one-pot, multistep method affording access to these diverse chemical scaffolds. Directed by a meta-analysis of the biological activities reported for marine indole alkaloids (MIAs) and synthetic IGAs, the brominated IGAs 25–56 were examined for their potential bioactivities against the Parkinson's Disease amyloid protein alpha synuclein (α-syn), antiplasmodial activities against chloroquine-resistant (3D7) and sensitive (Dd2) parasite strains of Plasmodium falciparum, and inhibition of mammalian (chymotrypsin and elastase) and viral (SARS-CoV-2 3CLpro) proteases. All of the synthetic IGAs tested exhibited binding affinity to the amyloid protein α-syn, while some showed inhibitory activities against P. falciparum, and the proteases, SARS-CoV-2 3CLpro, and chymotrypsin. The cellular safety of the IGAs was examined against cancerous and non-cancerous human cell lines, with all of the compounds tested inactive, thereby validating cheminformatics and meta-analyses results. The findings presented herein expand our knowledge of marine IGA bioactive chemical space and advocate expanding the scope of biological assays routinely used to investigate NP bioactivities, specifically those more suitable for non-toxic compounds. By integrating cheminformatics tools and functional assays into NP biological testing workflows, we can aim to enhance the potential of NPs and their scaffolds for future drug discovery and development. [ABSTRACT FROM AUTHOR]

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Information entropy is widely used in discrete mathematical chemistry as the descriptor to quantify the structural complexity of molecules. This review covers opportunities to use the information entropy parameters to evaluate changes in the complexity of molecules in both simple and multi-step chemical reactions. [ABSTRACT FROM AUTHOR]

Background: Rearranged during transfection (RET), an oncogenic protein, is associated with various cancers, including non-small-cell lung cancer (NSCLC), papillary thyroid cancer (PTC), pancreatic cancer, medullary thyroid cancer (MTC), breast cancer, and colorectal cancer. Dysregulation of RET contributes to cancer development, highlighting the importance of identifying lead compounds targeting this protein due to its pivotal role in cancer progression. Therefore, this study aims to discover effective lead compounds targeting RET across different cancer types and evaluate their potential to inhibit cancer progression. Methods: This study used a range of computational techniques, including Phase database creation, high-throughput virtual screening (HTVS), molecular docking, molecular mechanics with generalized Born surface area (MM-GBSA) solvation, assessment of pharmacokinetic (PK) properties, and molecular dynamics (MD) simulations, to identify potential lead compounds targeting RET. Results: Initially, a high-throughput virtual screening of the ZINC database identified 2,550 compounds from a pool of 170,269. Subsequent molecular docking studies revealed 10 compounds with promising negative binding scores ranging from -8.458 to -7.791 kcal/mol. MM-GBSA analysis further confirmed the potential of four compounds to exhibit negative binding scores. MD simulations demonstrated the stability of CID 95842900, CID 137030374, CID 124958150, and CID 110126793 with the target receptors. Conclusion: These findings suggest that these selected four compounds have the potential to inhibit phosphorylated RET (pRET) tyrosine kinase activity and may represent promising candidates for the treatment of various cancers. [ABSTRACT FROM AUTHOR]

Wood is considered to be one of the most important materials in the global economy. Wood loses shelf life in countries with severe winters. Pentachlorophenol (PCP) was predominantly used as a wood preservative because of its efficacy in controlling wood decay. This study discussed the overall impact of PCP across various environmental matrices, including soil, plants, water, air, and humans. Pentachlorophenol is a phenol substituted with five chloro-groups. High chlorination levels and stability of PCP make it hazardous to the environment, and persistent, and it also interferes with human, aquatic, and soil microbial health. PCP is volatile; thus, it is constantly discharged into the atmosphere and ingested by the human population. Owing to its hazardous nature, it was added to the Stockholm Convention's list of persistent organic pollutants (POPs) and phased out of the industry in 2023. This review has summarized PCP properties, usage, production volume, different transformation pathways, and its harmful effects on different environmental matrices such as air, water, soil, crops, and human health which have helped to outline the expected features of the new chemical compared with PCP. The improved chemical is intended to have quick degradability, fewer chlorine atoms, no aromatic structure, be non-toxic, environmentally benign, and efficient against wood deterioration while also penetrating the wood better. [ABSTRACT FROM AUTHOR]

Chemical information has become increasingly ubiquitous and has outstripped the pace of analysis and interpretation. We have developed an R package, uafR, that automates a grueling retrieval process for gas -chromatography coupled mass spectrometry (GC -MS) data and allows anyone interested in chemical comparisons to quickly perform advanced structural similarity matches. Our streamlined cheminformatics workflows allow anyone with basic experience in R to pull out component areas for tentative compound identifications using the best published understanding of molecules across samples (pubchem.gov). Interpretations can now be done at a fraction of the time, cost, and effort it would typically take using a standard chemical ecology data analysis pipeline. The package was tested in two experimental contexts: (1) A dataset of purified internal standards, which showed our algorithms correctly identified the known compounds with R2 values ranging from 0.827–0.999 along concentrations ranging from 1 × 10−5 to 1 × 103 ng/μl, (2) A large, previously published dataset, where the number and types of compounds identified were comparable (or identical) to those identified with the traditional manual peak annotation process, and NMDS analysis of the compounds produced the same pattern of significance as in the original study. Both the speed and accuracy of GC -MS data processing are drastically improved with uafR because it allows users to fluidly interact with their experiment following tentative library identifications [i.e. after the m/z spectra have been matched against an installed chemical fragmentation database (e.g. NIST)]. Use of uafR will allow larger datasets to be collected and systematically interpreted quickly. Furthermore, the functions of uafR could allow backlogs of previously collected and annotated data to be processed by new personnel or students as they are being trained. This is critical as we enter the era of exposomics, metabolomics, volatilomes, and landscape level, high-throughput chemotyping. This package was developed to advance collective understanding of chemical data and is applicable to any research that benefits from GC -MS analysis. It can be downloaded for free along with sample datasets from Github at github.org/castratton/uafR or installed directly from R or RStudio using the developer tools: 'devtools::install_github("castratton/uafR")'. [ABSTRACT FROM AUTHOR]

In the past decade, computational tools have become integral to catalyst design. They continue to offer significant support to experimental organic synthesis and catalysis researchers aiming for optimal reaction outcomes. More recently, data‐driven approaches utilizing machine learning have garnered considerable attention for their expansive capabilities. This Perspective provides an overview of diverse initiatives in the realm of computational catalyst design and introduces our automated tools tailored for high‐throughput in silico exploration of the chemical space. While valuable insights are gained through methods for high‐throughput in silico exploration and analysis of chemical space, their degree of automation and modularity are key. We argue that the integration of data‐driven, automated and modular workflows is key to enhancing homogeneous catalyst design on an unprecedented scale, contributing to the advancement of catalysis research. [ABSTRACT FROM AUTHOR]

Growing energy demand drives the rapid development of clean and reliable energy sources. In the past years, the exploration of novel materials with considerable efficiency and durability has drawn attention in the area of electrochemical energy conversion. Transition metal macrocyclic metallophthalocyanines (MPcs)‐based catalysts with a peculiar 2D constitution have emerged with a promising future account of their highly structural tailorability and molecular functionality which greatly extend their functionalities as electrocatalytic materials for energy conversion. This review summarizes the systematic engineering of synthesis of MPcs and their analogs in detail, and mostly pays attention to the frontier research of MPc‐based high‐performance catalysts toward different electrocatalytic processes concerning hydrogen, oxygen, water, carbon dioxide, and nitrogen, with a particular focus on discussing the interrelationship between the electrocatalytic activity and component/structure, as well as functional applications of MPcs. Finally, we give the gaps that need to be addressed after much thought. [ABSTRACT FROM AUTHOR]

Solid‐state lithium batteries (SSLBs) with high safety have emerged to meet the increasing energy density demands of electric vehicles, hybrid electric vehicles, and portable electronic devices. However, the dendrite formation, high interfacial resistance, and deleterious interfacial reactions caused by solid–solid contact between electrode and electrolyte have hindered the commercialization of SSLBs. Thus, in this review, the state‐of‐the‐art developments in the rational design of solid‐state electrolyte and their progression toward practical applications are reviewed. First, the origin of interface instability and the sluggish charge carrier transportation in solid–solid interface are presented. Second, various strategies toward stabilizing interfacial stability (reducing interfacial resistance, suppressing lithium dendrites, and side reactions) are summarized from the physical and chemical perspective, including building protective layer, constructing 3D and gradient structures, etc. Finally, the remaining challenges and future development trends of solid‐state electrolyte are prospected. This review provides a deep insight into solving the interfacial instability issues and promising solutions to enable practical high‐energy‐density lithium metal batteries. [ABSTRACT FROM AUTHOR]

Introduction: For effective and safe pharmacotherapy of pain, it is important to evaluate the mechanisms and spectrum of action of non-steroidal anti-inflammatory drugs (NSAIDs), including their effect on the proteom, central effect, as well as pain relieving and anti-inflammatory effects. The aim of the study was to evaluate the complex of differences between the promising candidate-molecule of indole derivative SV-1010 and the well-known NSAIDs. Material and Methods: Chemoproteomic moduling of pharmacological effects of SV-1010 and NSAID diclofenac, nimesulide and ketorolac on the rat proteom by means of topological analysis of chemographs. Results: The significant differences in the effects of the studied molecules were found for 820 proteins of the rat proteom. SV-1010, to a lesser degree than the other molecules, can inhibit dopamine D1- and D2- type receptors and, at the same time, stimulate the release of dopamine in the neostriatum (EC50 = 27 nM). SV-1010, to a greater extent than the other molecules, can inhibit the GABA conveyor (EC50 = 65 nM) and the NMDA receptors Grin1/Grin2b (IC50 175 nM). SV-1010 can activate Cannabinoid CB2 receptors, inhibit enzymes of leukotriene biosynthesis, CC receptors of pro-inflammatory chemokines and leukotrienes. Conclusion: The chemoreactomic and chemoproteomic profiling of SV-1010 indicated its potential central effect through dopaminergic and GABA-neurotransmission and additional anti-inflammatory mechanisms, which can help increase pain-relieving effects. [ABSTRACT FROM AUTHOR]

This article highlights research from the last century that has provided the basis for the searching techniques that are used in present-day cheminformatics systems, and thus provides an acknowledgement of the contributions made by early pioneers in the field. [ABSTRACT FROM AUTHOR]

The present study was focused on exploring the efficient inhibitors of closed state (form) of type III effector Xanthomonas outer protein Q (XopQ) (PDB: 4P5F) from the 44 phytochemicals of Picrasma quassioides using cutting-edge computational analysis. Among them, Kumudine B showed excellent binding energy (−11.0 kcal/mol), followed by Picrasamide A, Quassidine I and Quassidine J with the targeted closed state of XopQ protein compared to the reference standard drug (Streptomycin). The molecular dynamics (MD) simulations performed at 300 ns validated the stability of top lead ligands (Kumudine B, Picrasamide A, and Quassidine I)-bound XopQ protein complex with slightly lower fluctuation than Streptomycin. The MM-PBSA calculation confirmed the strong interactions of top lead ligands (Kumudine B and QuassidineI) with XopQ protein, as they offered the least binding energy. The results of absorption, distribution, metabolism, excretion, and toxicity (ADMET) analysis confirmed that Quassidine I, Kumudine B and Picrasamide A were found to qualify most of the drug-likeness rules with excellent bioavailability scores compared to Streptomycin. Results of the computational studies suggested that Kumudine B, Picrasamide A, and Quassidine I could be considered potential compounds to design novel antibacterial drugs against X. oryzae infection. Further in vitro and in vivo antibacterial activities of Kumudine B, Picrasamide A, and Quassidine I are required to confirm their therapeutic potentiality in controlling the X. oryzae infection. [ABSTRACT FROM AUTHOR]

Intracellular tau fibrils are sources of neurotoxicity and oxidative stress in Alzheimer's. Current drug discovery efforts have focused on molecules with tau fibril disaggregation and antioxidation functions. However, recent studies suggest that membrane-bound tau-containing oligomers (mTCOs), smaller and less ordered than tau fibrils, are neurotoxic in the early stage of Alzheimer's. Whether tau fibril-targeting molecules are effective against mTCOs is unknown. The binding of epigallocatechin-3-gallate (EGCG), CNS-11, and BHT-CNS-11 to in silico mTCOs and experimental tau fibrils was investigated using machine learning-enhanced docking and molecular dynamics simulations. EGCG and CNS-11 have tau fibril disaggregation functions, while the proposed BHT-CNS-11 has potential tau fibril disaggregation and antioxidation functions like EGCG. Our results suggest that the three molecules studied may also bind to mTCOs. The predicted binding probability of EGCG to mTCOs increases with the protein aggregate size. In contrast, the predicted probability of CNS-11 and BHT-CNS-11 binding to the dimeric mTCOs is higher than binding to the tetrameric mTCOs for the homo tau but not for the hetero tau–amylin oligomers. Our results also support the idea that anionic lipids may promote the binding of molecules to mTCOs. We conclude that tau fibril-disaggregating and antioxidating molecules may bind to mTCOs, and that mTCOs may also be useful targets for Alzheimer's drug design. [ABSTRACT FROM AUTHOR]

Synthetic efforts toward complex natural product (NP) scaffolds are useful ones, particularly those aimed at expanding their bioactive chemical space. Here, we utilised an orthogonal cheminformatics-based approach to predict the potential biological activities for a series of synthetic bis-indole alkaloids inspired by elusive sponge-derived NPs, echinosulfone A (1) and echinosulfonic acids A–D (2–5). Our work includes the first synthesis of desulfato-echinosulfonic acid C, an α-hydroxy bis(3′-indolyl) alkaloid (17), and its full NMR characterisation. This synthesis provides corroborating evidence for the structure revision of echinosulfonic acids A-C. Additionally, we demonstrate a robust synthetic strategy toward a diverse range of α-methine bis(3′-indolyl) acids and acetates (11–16) without the need for silica-based purification in either one or two steps. By integrating our synthetic library of bis-indoles with bioactivity data for 2048 marine indole alkaloids (reported up to the end of 2021), we analyzed their overlap with marine natural product chemical diversity. Notably, the C-6 dibrominated α-hydroxy bis(3′-indolyl) and α-methine bis(3′-indolyl) analogues (11, 14, and 17) were found to contain significant overlap with antibacterial C-6 dibrominated marine bis-indoles, guiding our biological evaluation. Validating the results of our cheminformatics analyses, the dibrominated α-methine bis(3′-indolyl) alkaloids (11, 12, 14, and 15) were found to exhibit antibacterial activities against methicillin-sensitive and -resistant Staphylococcus aureus. Further, while investigating other synthetic approaches toward bis-indole alkaloids, 16 incorrectly assigned synthetic α-hydroxy bis(3′-indolyl) alkaloids were identified. After careful analysis of their reported NMR data, and comparison with those obtained for the synthetic bis-indoles reported herein, all of the structures have been revised to α-methine bis(3′-indolyl) alkaloids. [ABSTRACT FROM AUTHOR]

Empowered by digital chemistry and informatics platforms, our study highlights the transformative impact of physics‐based simulation, machine learning, and data management in display industry R&D. This technology integration accelerates ideation and decision‐making, ensuring swift, accurate, and cost‐effective development for next‐generation display devices. [ABSTRACT FROM AUTHOR]

Cancer of the lung is known to be the principal cause of tumour-linked mortality worldwide. Non-small cell lung cancer (NSCLC) is the most common and lethal type of lung cancer with close to 2.0 million reported cases and a survival rate of not less than 20% at all stages in every 5 years of diagnosis. Despite the progress made in the treatment processes, there was no significant improvement in patients with NSCLC. As such, this research aims to design (utilizing a structure-based approach) potential EGFRT790M kinase inhibitors with their DFT calculations, Drug-likeness, ADME-Toxicity properties evaluation and MD simulation. A structure-based approach was employed to design potential EGFRT790M kinase inhibitors. The DFT calculations, Drug-likeness and ADME-Toxicity properties of the newly designed potential EGFRT790M kinase inhibitors were evaluated. MD simulation of the best newly designed compound was further studied. The virtual screening performed on the investigated EGFRT790M Kinase inhibitors has identified compound 8 with the highest mole dock score of − 136.377 kcal/mol as the best hit. The structure-based strategy was adopted to design six new compounds with better affinity (between − 138.255 to − 140.552 kcal/mol) towards the EGFRT790M Kinase enzyme (3IKA). The newly designed EGFR mutant inhibitors were seen to possess better mole dock scores than AZD9291 (− 118.872 kcal/mol). Based on the predicted drug-likeness and ADMET properties, the newly designed compounds are orally bioavailable with a bioavailability score of 0.55, can be synthesized easily in the wet lab (based on their synthetic accessibility score) and possess good pharmacokinetic profiles. The MD simulation performed at 150 ns provides insight into the stability of the S2D3 compound in the binding pocket of the 3IKA target protein. The total binding free energy calculation of the studied S2D3-3IKA complex suggested that van der Waals interactions and electrostatic interactions provided the general powerful force for the binding process. The designed compound S2D3 being stable with higher affinity and best pharmacokinetic properties is therefore recommended to be synthesized in the wet lab as potential NSCLC agents. [ABSTRACT FROM AUTHOR]

Quantitative structure-retention relationship (QSRR) modeling has emerged as an efficient alternative to predict analyte retention times using molecular descriptors. However, most reported QSRR models are column-specific, requiring separate models for each high-performance liquid chromatography (HPLC) system. This study evaluates the potential of machine learning (ML) algorithms and quantum mechanical (QM) descriptors to develop QSRR models that can predict retention times across three different reversed-phase HPLC columns under varying conditions. Four machine learning methods—partial least squares (PLS) regression, ridge regression (RR), random forest (RF), and gradient boosting (GB)—were compared on a dataset of 360 retention times for 15 aromatic analytes. Molecular descriptors were calculated using density functional theory (DFT). Column characteristics like particle size and pore size and experimental conditions like temperature and gradient time were additionally used as descriptors. Results showed that the GB-QSRR model demonstrated the best predictive performance, with Q2 of 0.989 and root mean square error of prediction (RMSEP) of 0.749 min on the test set. Feature analysis revealed that solvation energy (SE), HOMO–LUMO energy gap (∆E HOMO–LUMO), total dipole moment (Mtot), and global hardness (η) are among the most influential predictors for retention time prediction, indicating the significance of electrostatic interactions and hydrophobicity. Our findings underscore the efficiency of ensemble methods, GB and RF models employing non-linear learners, in capturing local variations in retention times across diverse experimental setups. This study emphasizes the potential of cross-column QSRR modeling and highlights the utility of ML models in optimizing chromatographic analysis. [ABSTRACT FROM AUTHOR]

Ever since the emergence of the COVID‐19 pandemic, to date, no specific and efficient therapeutic cure is available for the SARS‐CoV‐2 viral infection. Few reports have been observed with the late cardiovascular or neurodegenerative disorders that are associated with altered immunity. The nsp14 protein of SARS‐CoV‐2 is a key target for antiviral drug development as it controls replication of virus and evasion of host immune system. In this study, a consensus of cheminformatics approaches involving virtual screening, molecular docking, ADMET profiling, molecular dynamics simulations, free‐energy landscape, MM‐GBSA, DFT and 2D‐QSAR analysis is employed to discover the inhibitor of nsp14 protein. Our investigation reveals four natural bioactive compounds, i. e. ZINC2132169, ZINC8791872, ZINC8952607 and ZINC6624334 that possess enhanced binding affinity towards nsp14 protein with no predicted toxicity. Molecular docking, MD simulations and MM‐GBSA analysis established that all hit compounds strongly bind at the same pocket through hydrophobic interactions and hydrogen bonding. DFT analysis revealed greater reactivity of hit compounds and their stable complex formation with nsp14 protein, while the 2D‐QSAR (R2=0.92) predicted their pIC50 value in the range of 5.30–6.71 nM. These findings imply that hit compounds would be potent drug candidates in controlling the SARS‐CoV‐2 by inhibiting nsp14 protein. [ABSTRACT FROM AUTHOR]

Feline immunodeficiency virus (FIV) is a common infection found in domesticated and wild cats worldwide. Despite the wealth of therapeutic understanding of the disease in humans, considerably less information exists regarding the treatment of the disease in felines. Current treatment relies on drugs developed for the related human immunodeficiency virus (HIV) and includes compounds of the popular non‐nucleotide reverse transcriptase (NNRTI) class. This is despite FIV‐RT being only 67% similar to HIV‐1 RT at the enzyme level, increasing to 88% for the allosteric pocket targeted by NNRTIs. The goal of this project was to try to quantify how well the more extensive pharmacological knowledge available for human disease translates to felines. To this end we screened known NNRTIs and 10 diverse pyrimidine analogs identified virtually. We use this chemo‐centric probe approach to (a) assess the similarity between the two related RT targets based on the observed experimental inhibition values, (b) try to identify more potent inhibitors at FIV, and (c) gain a better appreciation of the structure–activity relationships (SAR). We found the correlation between IC50s at the two targets to be strong (r2 = 0.87) and identified compound 1 as the most potent inhibitor of FIV with IC50 of 0.030 μM ± 0.009. This compared to FIV IC50 values of 0.22 ± 0.17 μM, 0.040 ± 0.010 μM and >160 μM for known anti HIV‐1 RT drugs Efavirenz, Rilpivirine, and Nevirapine, respectively. This knowledge, along with an understanding of the structural origin that give rise to any differences could improve the way HIV drugs are repurposed for FIV. [ABSTRACT FROM AUTHOR]

Cheminformatics combines chemistry, computing, and mathematics to research and improve cancer drugs. Cheminformatics deals with graph theory and its tools. A graph invariant is a number that can only be computed via a graph. Atoms are the vertices and bonds are the edges in chemical graph theory's representation of compounds as graphs. Many topological indices have been devised to identify the physical characteristics of chemical substances. In this study, we calculated the melting point, boiling point, stable properties, surface tension, and cohesion of chemical structures of newly prepared anticancer drugs to topological invariants, K-Banhatti Sombor (KBSO) invariants, Dharwad invariants, and second-order anti-harmonic invariants (QCI). The discovery of the complex physical and chemical behavior of these chemical compounds in the human body is supported by computational studies, which are highly valued. In addition, it aids students in comprehending how these chemical structures are put together and enhanced through enhanced chemical and physical capabilities. The results of these derivations are used to model chemical structures and other network structures. [ABSTRACT FROM AUTHOR]