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Non–Contact Atomic Force Microscopy with CO–functionalized metal tips (referred to as HR-AFM) provides access to the internal structure of individual molecules adsorbed on a surface with totally unprecedented resolution. Previous works have shown that deep learning (DL) models can retrieve the chemical and structural information encoded in a 3D stack of constant-height HR–AFM images, leading to molecular identification. In this work, we overcome their limitations by using a well-established description of the molecular structure in terms of topological fingerprints, the 1024–bit Extended Connectivity Chemical Fingerprints of radius 2 (ECFP4), that were developed for substructure and similarity searching. ECFPs provide local structural information of the molecule, each bit correlating with a particular substructure within the molecule. Our DL model is able to extract this optimized structural descriptor from the 3D HR–AFM stacks and use it, through virtual screening, to identify molecules from their predicted ECFP4 with a retrieval accuracy on theoretical images of 95.4%. Furthermore, this approach, unlike previous DL models, assigns a confidence score, the Tanimoto similarity, to each of the candidate molecules, thus providing information on the reliability of the identification. By construction, the number of times a certain substructure is present in the molecule is lost during the hashing process, necessary to make them useful for machine learning applications. We show that it is possible to complement the fingerprint-based virtual screening with global information provided by another DL model that predicts from the same HR–AFM stacks the chemical formula, boosting the identification accuracy up to a 97.6%. Finally, we perform a limited test with experimental images, obtaining promising results towards the application of this pipeline under real conditions. Scientific contribution Previous works on molecular identification from AFM images used chemical descriptors that were intuitive for humans but sub–optimal for neural networks. We propose a novel method to extract the ECFP4 from AFM images and identify the molecule via a virtual screening, beating previous state-of-the-art models. [ABSTRACT FROM AUTHOR]

This study addresses the challenges posed by rainfall variability, leading to water deficits during critical stages of crop growth, resulting in a drastic reduction of cotton yield. In a comprehensive evaluation, thirty cotton genotypes, including five Gossypium arboreum (wild) and twenty-five Gossypium hirsutum (cultivated), were grown under rainfed and irrigated conditions. Drought tolerance indices (DTI) were evaluated, categorizing genotypes based on their resilience. Further, in-vitro screening at the seedling stage (20 days) under PEG-induced drought identified tolerant genotypes exhibiting elevated levels of free proline (19.07±5.31 mg.g−100fr.wt.), amino acids (34.59±6.51 mg.g−100fr.wt.), soluble proteins (13.73±2.65 mg.g−1fr.wt.), and glycine betaine (10.95±3.62 mg.g−100fr.wt.), in their leaves, positively correlating (p<0.001) with relative water content (87.70±3.45 %). Molecular analysis using drought-specific simple sequence repeats (SSR) markers revealed significant genetic variability in a cotton genotypes, with lower observed and higher expected heterozygosity. F statistics exposed a higher level of gene flow corresponding to low differentiation among populations. Among the genotypes group, wild GAM-67 and cultivated Deviraj emerged as the most potent, exhibiting the higher DTI and diverse gene pools. Study exhibited higher total gene diversity in drought-tolerant wild GAM-67 (0.8501) and greater expected heterozygosity (0.626) and gene flow (0.6731) in cultivated Deviraj, underlining their robust genetic adaptability to drought conditions. The integrated approach of field evaluations, in-vitro screening, and molecular analyses explained substantial genetic diversity, with the identification of promising genotypes displaying higher drought tolerance indices, elevated levels of stress-related biochemical osmolytes, and pronounced genetic adaptability, thereby contributing to the advancement of breeding initiatives for enhanced drought resilience in cotton. [Display omitted] • Thirty cotton genotypes were categorized according to their drought tolerance indices (DTI). • GAM-67 (wild) and Deviraj (cultivated) exhibited higher biochemical osmolytes, DTI and productivity, considering promising genotypes. • SSR markers unveiled significant genetic variability, high gene flow, and diverse gene pools in promising genotypes. • These genotypes performed better for the coefficient of gene differentiation and expected heterozygosity. • Molecular analysis reveals substantial genetic diversity, aiding breeding efforts for enhanced drought resilience in cotton. [ABSTRACT FROM AUTHOR]

Computational approaches for small-molecule drug discovery now regularly scale to the consideration of libraries containing billions of candidate small molecules. One promising approach to increased the speed of evaluating billion-molecule libraries is to develop succinct representations of each molecule that enable the rapid identification of molecules with similar properties. Molecular fingerprints are thought to provide a mechanism for producing such representations. Here, we explore the utility of commonly used fingerprints in the context of predicting similar molecular activity. We show that fingerprint similarity provides little discriminative power between active and inactive molecules for a target protein based on a known active—while they may sometimes provide some enrichment for active molecules in a drug screen, a screened data set will still be dominated by inactive molecules. We also demonstrate that high-similarity actives appear to share a scaffold with the query active, meaning that they could more easily be identified by structural enumeration. Furthermore, even when limited to only active molecules, fingerprint similarity values do not correlate with compound potency. In sum, these results highlight the need for a new wave of molecular representations that will improve the capacity to detect biologically active molecules based on their similarity to other such molecules. [ABSTRACT FROM AUTHOR]

Vector representation of molecules allows performing mathematical calculations in an effective way and using data analysis methods. The article reviews several approaches of representing molecules as vectors and their application in machine learning models. Additionally, part of the text was devoted to explaining visualization techniques of the influence of selected molecule's fragment on explored characteristic – QSAR (quantity structure-activity relationship). Finally, we performed an experiment using the described vectorization methods to compare their effectiveness on various prediction tasks. [ABSTRACT FROM AUTHOR]

Reproductive toxicity poses significant risks to fertility and progeny health, making its identification in pharmaceutical compounds crucial. In this study, we conducted a comprehensive in silico investigation of reproductive toxic molecules, identifying three distinct categories represented by Dimethylhydantoin, Phenol, and Dicyclohexyl phthalate. Our analysis included physicochemical properties, target prediction, and KEGG and GO pathway analyses, revealing diverse and complex mechanisms of toxicity. Given the complexity of these mechanisms, traditional molecule-target research approaches proved insufficient. Support Vector Machines (SVMs) combined with molecular descriptors achieved an accuracy of 0.85 in the test dataset, while our custom deep learning model, integrating molecular SMILES and graphs, achieved an accuracy of 0.88 in the test dataset. These models effectively predicted reproductive toxicity, highlighting the potential of computational methods in pharmaceutical safety evaluation. Our study provides a robust framework for utilizing computational methods to enhance the safety evaluation of potential pharmaceutical compounds. [ABSTRACT FROM AUTHOR]

In line with recent advances in neural drug design and sensitivity prediction, we introduce a novel architecture for the interpretable prediction of anticancer compound sensitivity utilizing a multimodal attention-based convolutional encoder. Our approach is based on three primary foundations: prior knowledge of intracellular interactions from protein-protein interaction networks, gene expression profiles of tumors, and the structure of chemicals as a SMILES sequence. With R2 = 0.86 and RMSE = 0.89, our multi-scale convolutional attention-based encoder significantly outperforms a baseline model trained on Morgan fingerprints, a set of SMILES-based encoders, and the previously reported state-of-the-art for multimodal drug sensitivity prediction. Talk about the Ensemble Convolution Neural Network Model: A Novel Regression-Based Approach (ECNN-NRNN) to Drug Sensitivity Analysis Using Multiple Pharmaomics Data Sets and Addressing Heterogeneity in Feature Selection for Sub-Pharmacoomics Parameters. Because some pharmacogenomic data is available online and should be made publicly available, it is essential to address drug sensitivity prediction and drug identification and design. Outline how the performance in sensitivity prediction can be improved using conventional methods, and provide an experimental evaluation. [ABSTRACT FROM AUTHOR]

In this work, we present scikit-fingerprints, a Python package for computation of molecular fingerprints for applications in chemoinformatics. Our library offers an industry-standard scikit-learn interface, allowing intuitive usage and easy integration with machine learning pipelines. It is also highly optimized, featuring parallel computation that enables efficient processing of large molecular datasets. Currently, scikit-fingerprints stands as the most feature-rich library in the open source Python ecosystem, offering over 30 molecular fingerprints. Our library simplifies chemoinformatics tasks based on molecular fingerprints, including molecular property prediction and virtual screening. It is also flexible, highly efficient, and fully open source.

Abstract Molecular fingerprints are indispensable tools in cheminformatics. However, stereochemistry is generally not considered, which is problematic for large molecules which are almost all chiral. Herein we report MAP4C, a chiral version of our previously reported fingerprint MAP4, which lists MinHashes computed from character strings containing the SMILES of all pairs of circular substructures up to a diameter of four bonds and the shortest topological distance between their central atoms. MAP4C includes the Cahn-Ingold-Prelog (CIP) annotation (R, S, r or s) whenever the chiral atom is the center of a circular substructure, a question mark for undefined stereocenters, and double bond cis–trans information if specified. MAP4C performs slightly better than the achiral MAP4, ECFP and AP fingerprints in non-stereoselective virtual screening benchmarks. Furthermore, MAP4C distinguishes between stereoisomers in chiral molecules from small molecule drugs to large natural products and peptides comprising thousands of diastereomers, with a degree of distinction smaller than between structural isomers and proportional to the number of chirality changes. Due to its excellent performance across diverse molecular classes and its ability to handle stereochemistry, MAP4C is recommended as a generally applicable chiral molecular fingerprint. Scientific contribution The ability of our chiral fingerprint MAP4C to handle stereoisomers from small molecules to large natural products and peptides is unprecedented and opens the way for cheminformatics to include stereochemistry as an important molecular parameter across all fields of molecular design. Graphical Abstract

Histone deacetylase 6 (HDAC6) is a promising drug target for the treatment of human diseases such as cancer, neurodegenerative diseases (in particular, Alzheimer's disease), and multiple sclerosis. Considerable attention is paid to the development of selective non-toxic HDAC6 inhibitors. To this end, we successfully form a set of 3854 compounds and proposed adequate regression QSAR models for HDAC6 inhibitors. The models have been developed using the PubChem, Klekota-Roth, 2D atom pair fingerprints, and RDkit descriptors and the gradient boosting, support vector machines, neural network, and k-nearest neighbours methods. The models are integrated into the developed HT_PREDICT application, which is freely available at . In vitro studies have confirmed the predictive ability of the proposed QSAR models integrated into the HT_PREDICT web application. In addition, the virtual screening performed with the HT_PREDICT web application allowed us to propose two promising inhibitors for further investigations. [ABSTRACT FROM AUTHOR]

Lung cancer is the second most common cancer, which is the leading cause of cancer death worldwide. The FDA has approved almost 100 drugs against lung cancer, but it is still not curable as most drugs target a single protein and block a single pathway. In this study, we screened the Drug Bank library against three major proteins- ribosomal protein S6 kinase alpha-6 (6G77), cyclic-dependent protein kinase 2 (1AQ1), and insulin-like growth factor 1 (1K3A) of lung cancer and identified the compound 5-nitroindazole (DB04534) as a multitargeted inhibitor that potentially can treat lung cancer. For the screening, we deployed multisampling algorithms such as HTVS, SP and XP, followed by the MM\GBSA calculation, and the study was extended to molecular fingerprinting analysis, pharmacokinetics prediction, and Molecular Dynamics simulation to understand the complex's stability. The docking scores against the proteins 6G77, 1AQ1, and 1K3A were − 6.884 kcal/mol, − 7.515 kcal/mol, and − 6.754 kcal/mol, respectively. Also, the compound has shown all the values satisfying the ADMET criteria, and the fingerprint analysis has shown wide similarities and the water WaterMap analysis that helped justify the compound's suitability. The molecular dynamics of each complex have shown a cumulative deviation of less than 2 Å, which is considered best for the biomolecules, especially for the protein–ligand complexes. The best feature of the identified drug candidate is that it targets multiple proteins that control cell division and growth hormone mediates simultaneously, reducing the burden of the pharmaceutical industry by reducing the resistance chance. [ABSTRACT FROM AUTHOR]

Molecular fingerprints are indispensable tools in cheminformatics. However, stereochemistry is generally not considered, which is problematic for large molecules which are almost all chiral. Herein we report MAP4C, a chiral version of our previously reported fingerprint MAP4, which lists MinHashes computed from character strings containing the SMILES of all pairs of circular substructures up to a diameter of four bonds and the shortest topological distance between their central atoms. MAP4C includes the Cahn-Ingold-Prelog (CIP) annotation (R, S, r or s) whenever the chiral atom is the center of a circular substructure, a question mark for undefined stereocenters, and double bond cis–trans information if specified. MAP4C performs slightly better than the achiral MAP4, ECFP and AP fingerprints in non-stereoselective virtual screening benchmarks. Furthermore, MAP4C distinguishes between stereoisomers in chiral molecules from small molecule drugs to large natural products and peptides comprising thousands of diastereomers, with a degree of distinction smaller than between structural isomers and proportional to the number of chirality changes. Due to its excellent performance across diverse molecular classes and its ability to handle stereochemistry, MAP4C is recommended as a generally applicable chiral molecular fingerprint. Scientific contribution: The ability of our chiral fingerprint MAP4C to handle stereoisomers from small molecules to large natural products and peptides is unprecedented and opens the way for cheminformatics to include stereochemistry as an important molecular parameter across all fields of molecular design. [ABSTRACT FROM AUTHOR]

BackgroundChemicals may lead to acute liver injuries, posing a serious threat to human health. Achieving the precise safety profile of a compound is challenging due to the complex and expensive testing procedures. In silico approaches will aid in identifying the potential risk of drug candidates in the initial stage of drug development and thus mitigating the developmental cost.MethodsIn current studies, QSAR models were developed for hepatotoxicity predictions using the ensemble strategy to integrate machine learning (ML) and deep learning (DL) algorithms using various molecular features. A large dataset of 2588 chemicals and drugs was randomly divided into training (80%) and test (20%) sets, followed by the training of individual base models using diverse machine learning or deep learning based on three different kinds of descriptors and fingerprints. Feature selection approaches were employed to proceed with model optimizations based on the model performance. Hybrid ensemble approaches were further utilized to determine the method with the best performance.ResultsThe voting ensemble classifier emerged as the optimal model, achieving an excellent prediction accuracy of 80.26%, AUC of 82.84%, and recall of over 93% followed by bagging and stacking ensemble classifiers method. The model was further verified by an external test set, internal 10-fold cross-validation, and rigorous benchmark training, exhibiting much better reliability than the published models.ConclusionThe proposed ensemble model offers a dependable assessment with a good performance for the prediction regarding the risk of chemicals and drugs to induce liver damage.

Mycobacterium tuberculosis (Mtb) remains a major global health concern, causing millions of infections and deaths annually. Drug-resistant tuberculosis poses significant challenges, necessitating the search for new Mtb inhibitors. In this context, a cheminformatic analysis of the currently available antimycobacterial chemical space can be performed to discover structural diversity and new scaffolds. This study conducts a comprehensive cheminformatic analysis of two datasets containing Mtb inhibitors (Mtbs and Phytochemicals), comparing them with Approved drugs and Nutraceuticals datasets. The analysis includes physicochemical properties, molecular scaffolds, structural fingerprints, and structural similarities. The analysis of physicochemical property distributions revealed that Mtb inhibitors are generally less polar or equally polar compared to approved drugs and have similar flexibility. The visual representation of the property space illustrates that the Mtbs dataset is confined to a narrower area, whereas certain compounds within the Phytochemical dataset broaden the scope of the traditional medicinal space. Scaffold analysis identifies several promising candidates in both the Mtbs and Phytochemical datasets. Additionally, employing SkelSpheres descriptors and rubber band scaling for similarity analysis reveals a limited depiction of the chemical space. The analysis of structural diversity indicates that the compounds in the Mtbs dataset exhibit less structural diversity. These findings underscore the necessity to broaden the chemical space of Mtb-targeting molecules by incorporating representative molecules from diverse scaffolds to effectively tackle the issue of drug resistance.

This work evaluates the performance of four machine learning models (MLMs): support vector machine (SVM), K-nearest neighbor (KNN), discriminant analysis (DA), and logistic regression (LR) in predicting the biodegradability of chemicals, a critical factor for assessing environmental risks. For this purpose, the RDKit library was initially employed to extract nine fingerprints from a dataset consisting of 1717 chemical compounds. Subsequently, the Information Gain (IG) feature ranking algorithm was used to identify the top 100 predictive features for each fingerprint. The MLM Hyperparameters were optimized, and informative features were selected using a multi-objective genetic algorithm (MOGA), which identified dominant pairs (MLM/fingerprint) maximizing F1-score while minimizing number of features. MACCS, Layered, and Avalon molecular fingerprints associated with the different MLM showed superior performance, with KNN/MACCS achieving a cross-validated F1-score of 87.63% using only 13 features. Afterwards, the final classification models were constructed using the solution with the highest cross-validated F1-score complement for each combination of MLM/fingerprint. These models were then evaluated on both the training and test datasets. In the training subset, the MACCS fingerprint consistently outperformed others, achieving cross-validated AUC scores above 90% for all models (SVM: 91%, KNN: 90.4%, DA: 90.9%, LR: 91.32%). The SVM/MACCS pair demonstrated the highest accuracy (94.17%), specificity (95.84%), and F1-score (93.14%). In the test subset, the SVM/Layered pair exhibited the highest accuracy (84.01%) and specificity (88.09%). The DA/Avalon combination achieved the highest sensitivity (84.40%), while the SVM/Avalon pair reached the highest F1-score (82.58%). These results underscore the effectiveness of MACCS fingerprints for biodegradation classification across various models. Additionally, Permutation Feature Importance (PFI) and Shapley Additive Explanations (SHAP) have identified the key MACCS features influencing biodegradation classifications in the SVM model. These methods highlighted MACCS bit numbers 154, 145, 142, 144, and 156 as the most crucial contributors to the SVM model’s predictions.

Abstract Conventional machine learning (ML) and deep learning (DL) play a key role in the selectivity prediction of kinase inhibitors. A number of models based on available datasets can be used to predict the kinase profile of compounds, but there is still controversy about the advantages and disadvantages of ML and DL for such tasks. In this study, we constructed a comprehensive benchmark dataset of kinase inhibitors, involving in 141,086 unique compounds and 216,823 well-defined bioassay data points for 354 kinases. We then systematically compared the performance of 12 ML and DL methods on the kinase profiling prediction task. Extensive experimental results reveal that (1) Descriptor-based ML models generally slightly outperform fingerprint-based ML models in terms of predictive performance. RF as an ensemble learning approach displays the overall best predictive performance. (2) Single-task graph-based DL models are generally inferior to conventional descriptor- and fingerprint-based ML models, however, the corresponding multi-task models generally improves the average accuracy of kinase profile prediction. For example, the multi-task FP-GNN model outperforms the conventional descriptor- and fingerprint-based ML models with an average AUC of 0.807. (3) Fusion models based on voting and stacking methods can further improve the performance of the kinase profiling prediction task, specifically, RF::AtomPairs + FP2 + RDKitDes fusion model performs best with the highest average AUC value of 0.825 on the test sets. These findings provide useful information for guiding choices of the ML and DL methods for the kinase profiling prediction tasks. Finally, an online platform called KIPP ( https://kipp.idruglab.cn ) and python software are developed based on the best models to support the kinase profiling prediction, as well as various kinase inhibitor identification tasks including virtual screening, compound repositioning and target fishing.

Molecular recognition is fundamental in biology, underpinning intricate processes through specific protein–ligand interactions. This understanding is pivotal in drug discovery, yet traditional experimental methods face limitations in exploring the vast chemical space. Computational approaches, notably quantitative structure–activity/property relationship analysis, have gained prominence. Molecular fingerprints encode molecular structures and serve as property profiles, which are essential in drug discovery. While two-dimensional (2D) fingerprints are commonly used, three-dimensional (3D) structural interaction fingerprints offer enhanced structural features specific to target proteins. Machine learning models trained on interaction fingerprints enable precise binding prediction. Recent focus has shifted to structure-based predictive modeling, with machine-learning scoring functions excelling due to feature engineering guided by key interactions. Notably, 3D interaction fingerprints are gaining ground due to their robustness. Various structural interaction fingerprints have been developed and used in drug discovery, each with unique capabilities. This review recapitulates the developed structural interaction fingerprints and provides two case studies to illustrate the power of interaction fingerprint-driven machine learning. The first elucidates structure–activity relationships in β2 adrenoceptor ligands, demonstrating the ability to differentiate agonists and antagonists. The second employs a retrosynthesis-based pre-trained molecular representation to predict protein–ligand dissociation rates, offering insights into binding kinetics. Despite remarkable progress, challenges persist in interpreting complex machine learning models built on 3D fingerprints, emphasizing the need for strategies to make predictions interpretable. Binding site plasticity and induced fit effects pose additional complexities. Interaction fingerprints are promising but require continued research to harness their full potential. [ABSTRACT FROM AUTHOR]

Fragment‐based drug design is an emerging technology in pharmaceutical research and development. One of the key aspects of this technology is the identification and quantitative characterization of molecular fragments. This study presents a strategy for identifying important molecular fragments based on molecular fingerprints and decision tree algorithms and verifies its feasibility in predicting protein‐ligand binding affinity. Specifically, the three‐dimensional (3D) structures of protein‐ligand complexes are encoded using extended‐connectivity fingerprints (ECFP), and three decision tree models, namely Random Forest, XGBoost, and LightGBM, are used to quantitatively characterize the feature importance, thereby extracting important molecular fragments with high reliability. Few‐shot learning reveals that the extracted molecular fragments contribute significantly and consistently to the binding affinity even with a small sample size. Despite the absence of location and distance information for molecular fragments in ECFP, 3D visualization, in combination with the reverse ECFP process, shows that the majority of the extracted fragments are located at the binding interface of the protein and the ligand. This alignment with the distance constraints critical for binding affinity further supports the reliability of the strategy for identifying important molecular fragments. [ABSTRACT FROM AUTHOR]

• This research introduces a new deep learning model called DMFPGA for predicting DILI positivity. • DMFPGA fuses multiple features and finally channels them into a fully connected neural network to achieve classification. • We take into account complementary features at both atomic and molecular levels through the extraction and processing of various distinct molecular attributes. • The amalgamation of multiple features is employed to represent each molecule. Drug-induced liver injury (DILI) is a significant issue in drug development and clinical treatment due to its potential to cause liver dysfunction or damage, which, in severe cases, can lead to liver failure or even fatality. DILI has numerous pathogenic factors, many of which remain incompletely understood. Consequently, it is imperative to devise methodologies and tools for anticipatory assessment of DILI risk in the initial phases of drug development. In this study, we present DMFPGA, a novel deep learning predictive model designed to predict DILI. To provide a comprehensive description of molecular properties, we employ a multi-head graph attention mechanism to extract features from the molecular graphs, representing characteristics at the level of compound nodes. Additionally, we combine multiple fingerprints of molecules to capture features at the molecular level of compounds. The fusion of molecular fingerprints and graph features can more fully express the properties of compounds. Subsequently, we employ a fully connected neural network to classify compounds as either DILI-positive or DILI-negative. To rigorously evaluate DMFPGA's performance, we conduct a 5-fold cross-validation experiment. The obtained results demonstrate the superiority of our method over four existing state-of-the-art computational approaches, exhibiting an average AUC of 0.935 and an average ACC of 0.934. We believe that DMFPGA is helpful for early-stage DILI prediction and assessment in drug development. [ABSTRACT FROM AUTHOR]

The composition of the juice from grape berries is at the basis of the definition of technological ripeness before harvest, historically evaluated from global sugar and acid contents. If many studies have contributed to the identification of other primary and secondary metabolites in whole berries, deepening knowledge about the chemical composition of the sole flesh of grape berries (i.e., without considering skins and seeds) at harvest is of primary interest when studying the enological potential of widespread grape varieties producing high-added-value wines. Here, we used non-targeted DI-FT-ICR-MS and RP-UHPLC-Q-ToF-MS analyses to explore the extent of metabolite coverage of up to 290 grape juices from four Vitis vinifera grape varieties, namely Chardonnay, Pinot noir, Meunier, and Aligoté, sampled at harvest from 91 vineyards in Europe and Argentina, over three successive vintages. SPE pretreatment of samples led to the identification of more than 4500 detected C,H,O,N,S-containing elemental compositions, likely associated with tens of thousands of distinct metabolites. We further revealed that a major part of this chemical diversity appears to be common to the different juices, as exemplified by Pinot noir and Chardonnay samples. However, it was possible to build significant models for the discrimination of Chardonnay from Pinot noir grape juices, and of Chardonnay from Aligoté grape juices, regardless of the geographical origin or the vintage. Therefore, this metabolomic approach opens access to a remarkable holistic molecular description of the instantaneous composition of such a biological matrix, which is the result of complex interplays among environmental, biochemical, and vine growing practices. [ABSTRACT FROM AUTHOR]

Computational approaches for small-molecule drug discovery now regularly scale to the consideration of libraries containing billions of candidate small molecules. One promising approach to increased the speed of evaluating billion-molecule libraries is to develop succinct representations of each molecule that enable the rapid identification of molecules with similar properties. Molecular fingerprints are thought to provide a mechanism for producing such representations. Here, we explore the utility of commonly used fingerprints in the context of predicting similar molecular activity. We show that fingerprint similarity provides little discriminative power between active and inactive molecules for a target protein based on a known active—while they may sometimes provide some enrichment for active molecules in a drug screen, a screened data set will still be dominated by inactive molecules. We also demonstrate that high-similarity actives appear to share a scaffold with the query active, meaning that they could more easily be identified by structural enumeration. Furthermore, even when limited to only active molecules, fingerprint similarity values do not correlate with compound potency. In sum, these results highlight the need for a new wave of molecular representations that will improve the capacity to detect biologically active molecules based on their similarity to other such molecules.

The drug development process takes a long time since it requires sorting through a large number of inactive compounds from a large collection of compounds chosen for study and choosing just the most pertinent compounds that can bind to a disease protein. The use of virtual screening in pharmaceutical research is growing in popularity. During the early phases of medication research and development, it is crucial. Chemical compound searches are nowmore narrowly targeted. Because the databases containmore andmore ligands, thismethod needs to be quick and exact. Neural network fingerprints were created more effectively than the well-known Extended Connectivity Fingerprint (ECFP). Only the largest sub-graph is taken into consideration to learn the representation, despite the fact that the conventional graph network generates a better-encoded fingerprint. When using the average or maximum pooling layer, it also contains unrelated data. This article suggested the Graph Convolutional Attention Network (GCAN), a graph neural network with an attention mechanism, to address these problems. Additionally, it makes the nodes or sub-graphs that are used to create the molecular fingerprint more significant. The generated fingerprint is used to classify drugs using ensemble learning. As base classifiers, ensemble stacking is applied to Support Vector Machines (SVM), Random Forest, Nave Bayes, Decision Trees, AdaBoost, and Gradient Boosting. When compared to existing models, the proposed GCAN fingerprint with an ensemble model achieves relatively high accuracy, sensitivity, specificity, and area under the curve. Additionally, it is revealed that our ensemble learning with generated molecular fingerprint yields 91% accuracy, outperforming earlier approaches. [ABSTRACT FROM AUTHOR]

Computational molecular design can yield chemically unreasonable compounds when performed carelessly. A popular strategy to mitigate this risk is mimicking reference chemistry. This is commonly achieved by restricting the way in which molecules are constructed or modified. While it is well established that such an approach helps in designing chemically appealing molecules, concerns about these restrictions impacting chemical space exploration negatively linger. In this work we present a software library for constrained graph-based molecule manipulation and showcase its functionality by developing a molecule generator. Said generator designs molecules mimicking reference chemical features of differing granularity. We find that restricting molecular construction lightly, beyond the usual positive effects on drug-likeness and synthesizability of designed molecules, provides guidance to optimization algorithms navigating chemical space. Nonetheless, restricting molecular construction excessively can indeed hinder effective chemical space exploration. [ABSTRACT FROM AUTHOR]

The main aim of this study is to find relevant analytic fingerprints for plants' structural characterization using spectroscopic techniques and thermogravimetric analyses (TGAs) as alternative methods, particularized on cabbage treated with selenium–baker's yeast vinasse formulation (Se-VF) included in a foliar fertilizer formula. The hypothesis investigated is that Se-VF will induce significant structural changes compared with the control, analytically confirming the biofortification of selenium-enriched cabbage as a nutritive vegetable, and particularly the plant biostimulant effects of the applied Se-VF formulation on cabbage grown in the field. The TGA evidenced a structural transformation of the molecular building blocks in the treated cabbage leaves. The ash residues increased after treatment, suggesting increased mineral accumulation in leaves. X-ray diffraction (XRD) and Fourier-transform infrared spectroscopy (FTIR) evidenced a pectin–Iα-cellulose structure of cabbage that correlated with each other in terms of leaf crystallinity. FTIR analysis suggested the accumulation of unesterified pectin and possibly (seleno) glucosinolates and an increased network of hydrogen bonds. The treatment with Se-VF formulation induced a significant increase in the soluble fibers of the inner leaves, accompanied by a decrease in the insoluble fibers. The ratio of soluble/insoluble fibers correlated with the crystallinity determined by XRD and with the FTIR data. The employed analytic techniques can find practical applications as fast methods in studies of the effects of new agrotechnical practices, while in our particular case study, they revealed effects specific to plant biostimulants of the Se-VF formulation treatment: enhanced mineral utilization and improved quality traits. [ABSTRACT FROM AUTHOR]

The HDAC6 (histone deacetylase 6) enzyme plays a key role in many biological processes, including cell division, apoptosis, and immune response. To date, HDAC6 inhibitors are being developed as effective drugs for the treatment of various diseases. In this work, adequate QSAR models of HDAC6 inhibitors are proposed. They are integrated into the developed application HDAC6 Detector, which is freely available at . The web application HDAC6 Detector can be used to perform virtual screening of HDAC6 inhibitors by dividing the compounds into active and inactive ones relative to the reference vorinostat compound (IC50 = 10.4 nM). The web application implements a structural interpretation of the developed QSAR models. In addition, the application can evaluate the compliance of a compound with Lipinski's rule. The developed models are used for virtual screening of a series of 12 new hydroxamic acids, namely, the derivatives of 3-hydroxyquinazoline-4(3H)-ones and 2-aryl-2,3-dihydroquinazoline-4(1H)-ones. In vitro evaluation of the inhibitory activity of this series of compounds against HDAC6 allowed us to confirm the results of virtual screening and to select promising compounds V-6 and V-11, the IC50 of which is 0.99 and 0.81 nM, respectively. [ABSTRACT FROM AUTHOR]

The power conversion efficiency (PCE) of ternary polymer solar cells (PSCs) with non-fullerene has a phenomenal increase in recent years. However, improving the open circuit voltage (Voc) of ternary PSCs with non-fullerene still remains a challenge. Therefore, in this work, machine learning (ML) algorithms are employed, including eXtreme gradient boosting, K-nearest neighbor and random forest, to quantitatively analyze the impact mechanism of Voc in ternary PSCs with the double acceptors from the two aspects of photovoltaic materials. In one aspect of photovoltaic materials, the doping concentration has the greatest impact on Voc in ternary PSCs. Furthermore, the addition of the third component affects the energy offset between the donor and acceptor for increasing Voc in ternary PSCs. More importantly, to obtain the maximum Voc in ternary PSCs with the double acceptors, the HOMO and LUMO energy levels of the third component should be around (−5.7 ± 0.1) eV and (−3.6 ± 0.1) eV, respectively. In the other aspect of molecular descriptors and molecular fingerprints in the third component of ternary PSCs with the double acceptors, the hydrogen bond strength and aromatic ring structure of the third component have high impact on the Voc of ternary PSCs. In partial dependence plot, it is clear that when the number of methyl groups is four and the number of carbonyl groups is two in the third component of acceptor, the Voc of ternary PSCs with the double acceptors can be maximized. All of these findings provide valuable insights into the development of materials with high Voc in ternary PSCs for saving time and cost. [ABSTRACT FROM AUTHOR]

Understanding structure‐activity landscapes is essential in drug discovery. Similarly, it has been shown that the presence of activity cliffs in compound data sets can have a substantial impact not only on the design progress but also can influence the predictive ability of machine learning models. With the continued expansion of the chemical space and the currently available large and ultra‐large libraries, it is imperative to implement efficient tools to analyze the activity landscape of compound data sets rapidly. The goal of this study is to show the applicability of the n‐ary indices to quantify the structure‐activity landscapes of large compound data sets using different types of structural representation rapidly and efficiently. We also discuss how a recently introduced medoid algorithm provides the foundation to finding optimum correlations between similarity measures and structure‐activity rankings. The applicability of the n‐ary indices and the medoid algorithm is shown by analyzing the activity landscape of 10 compound data sets with pharmaceutical relevance using three fingerprints of different designs, 16 extended similarity indices, and 11 coincidence thresholds. [ABSTRACT FROM AUTHOR]

This work applies the concept of structural causal modelling (SCM) for the prediction of eutectic temperatures of choline chloride based deep eutectic solvents (DES). Two SCM models were developed, one based on molecular descriptors (MD), and the other based on molecular fingerprints (MF). The models are presented in the form of directed acyclic graphs (DAG). The SCM-MD model shows that the chi simple cluster connectivity descriptor (SC.5) and a number of hydrogen atoms (nH.1) are the key causal variables. The causal relations between the model variables and eutectic temperature were determined after performing d-separation to block the variable confounding interference. The corresponding nonlinear causal relations were modelled by Bayes neural network with a single inner layer. Based on the SCM-MD model, a decision tree is proposed for the prediction of eutectic temperatures. Model performances were tested on a literature dataset of eutectic temperatures of ChCl based DESs. The SCM-MD model provided the most accurate prediction with an error of 7.5 °C.

Machine learning based Virtual Screening has proved as an important intermediate process that helps in the field of drug discovery in reducing the cost and manpower of classical drug discovery process. This work proposes a deep learning based virtual screening model for the early discovery of drug compounds for the disease named Hemochromatosis, which is the excess absorption of iron in the human body. Our study focuses on finding possible drug compounds from medicinal plants to cure Hemochromatosis. The proposed method uses Graph Convolutional Neural Networks (GCN) for Ligand Based Virtual Screening (LBVS). Deep Learning algorithm, GCN outperformed all other experimented models in LBVS with an accuracy of 98.26% and F-score of 98% respectively. A small set of biologically active compounds was identified from the phytochemical dataset after performing the LBVS. The selected ligands after LBVS are taken for In-Silico Structure Based Screening (SBVS) called molecular docking and the best compounds that have high binding affinity towards the disease protein for Hemochromatosis is selected and recommended for in-vitro studies. Ablation studies are done with 12 different machine learning models including ensemble models. The proposed model exhibited a related percentage improvement of around 0.5% in accuracy and F-score, when compared to the tree based ensemble model, XGBoost. This study aims to suggest in-silico studies for ligand based and structure based screening to identify potential drug molecules from medicinal plants which can be tested in in-vitro analysis and studies.

Abstract Background Phytochemicals or secondary metabolites are low molecular weight organic compounds with little function in plant growth and development. Nevertheless, the metabolite diversity govern not only the phenetics of an organism but may also inform the evolutionary pattern and adaptation of green plants to the changing environment. Plant chemoinformatics analyzes the chemical system of natural products using computational tools and robust mathematical algorithms. It has been a powerful approach for species-level differentiation and is widely employed for species classifications and reinforcement of previous classifications. Results This study attempts to classify Angiosperms using plant sulfur-containing compound (SCC) or sulphated compound information. The SCC dataset of 692 plant species were collected from the comprehensive species-metabolite relationship family (KNApSAck) database. The structural similarity score of metabolite pairs under all possible combinations (plant species-metabolite) were determined and metabolite pairs with a Tanimoto coefficient value > 0.85 were selected for clustering using machine learning algorithm. Metabolite clustering showed association between the similar structural metabolite clusters and metabolite content among the plant species. Phylogenetic tree construction of Angiosperms displayed three major clades, of which, clade 1 and clade 2 represented the eudicots only, and clade 3, a mixture of both eudicots and monocots. The SCC-based construction of Angiosperm phylogeny is a subset of the existing monocot-dicot classification. The majority of eudicots present in clade 1 and 2 were represented by glucosinolate compounds. These clades with SCC may have been a mixture of ancestral species whilst the combinatorial presence of monocot-dicot in clade 3 suggests sulphated-chemical structure diversification in the event of adaptation during evolutionary change. Conclusions Sulphated chemoinformatics informs classification of Angiosperms via machine learning technique.

Molecular recognition is fundamental in biology, underpinning intricate processes through specific protein–ligand interactions. This understanding is pivotal in drug discovery, yet traditional experimental methods face limitations in exploring the vast chemical space. Computational approaches, notably quantitative structure–activity/property relationship analysis, have gained prominence. Molecular fingerprints encode molecular structures and serve as property profiles, which are essential in drug discovery. While two-dimensional (2D) fingerprints are commonly used, three-dimensional (3D) structural interaction fingerprints offer enhanced structural features specific to target proteins. Machine learning models trained on interaction fingerprints enable precise binding prediction. Recent focus has shifted to structure-based predictive modeling, with machine-learning scoring functions excelling due to feature engineering guided by key interactions. Notably, 3D interaction fingerprints are gaining ground due to their robustness. Various structural interaction fingerprints have been developed and used in drug discovery, each with unique capabilities. This review recapitulates the developed structural interaction fingerprints and provides two case studies to illustrate the power of interaction fingerprint-driven machine learning. The first elucidates structure–activity relationships in β2 adrenoceptor ligands, demonstrating the ability to differentiate agonists and antagonists. The second employs a retrosynthesis-based pre-trained molecular representation to predict protein–ligand dissociation rates, offering insights into binding kinetics. Despite remarkable progress, challenges persist in interpreting complex machine learning models built on 3D fingerprints, emphasizing the need for strategies to make predictions interpretable. Binding site plasticity and induced fit effects pose additional complexities. Interaction fingerprints are promising but require continued research to harness their full potential.

Machine learning (ML) is increasingly being used to guide drug discovery processes. When applying ML approaches to chemical datasets, molecular descriptors and fingerprints are typically used to represent compounds as numerical vectors. However, in recent years, end-to-end deep learning (DL) methods that can learn feature representations directly from line notations or molecular graphs have been proposed as alternatives to using precomputed features. This study set out to investigate which compound representation methods are the most suitable for drug sensitivity prediction in cancer cell lines. Twelve different representations were benchmarked on 5 compound screening datasets, using DeepMol, a new chemoinformatics package developed by our research group, to perform these analyses. The results of this study show that the predictive performance of end-to-end DL models is comparable to, and at times surpasses, that of models trained on molecular fingerprints, even when less training data is available. This study also found that combining several compound representation methods into an ensemble can improve performance. Finally, we show that a post hoc feature attribution method can boost the explainability of the DL models.

BACKGROUND: Protein-ligand binding affinity is of significant importance in structure-based drug design. Recently, the development of machine learning techniques has provided an efficient and accurate way to predict binding affinity. However, the prediction performance largely depends on how molecules are represented. OBJECTIVE: Different molecular descriptors are designed to capture different features. The study aims to identify the optimal circular fingerprints for predicting protein-ligand binding affinity with matched neural network architectures. METHODS: Extended-connectivity fingerprints (ECFP) and protein-ligand extended connectivity fingerprints (PLEC) encode circular atomic and bonding connectivity environments with the preference for intra- and inter-molecular features, respectively. Densely-connected neural networks are employed to map the circular fingerprints of protein-ligand complexes to binding affinities RESULTS: The performance of neural networks is sensitive to the parameters used for ECFP and PLEC fingerprints. The R2_score of the evaluated ECFP and PLEC fingerprints reaches 0.52 and 0.49, higher than that of the improperly set ECFP and PLEC fingerprints with R2_score of 0.45 and 0.38, respectively. Additionally, compared to the predictions from the standalone fingerprints, the ECFP+PLEC conjoint ones slightly improve the prediction accuracy with R2_score of approximately 0.55. CONCLUSION: Both intra- and inter-molecular structural features encoded in the circular fingerprints contribute to the protein-ligand binding affinity. Optimizing the parameters of ECFP and PLEC can enhance performance. The conjoint fingerprint scheme can be generally extended to other molecular descriptors for enhanced feature engineering and improved predictive performance. [ABSTRACT FROM AUTHOR]

Spiking neural networks are biologically inspired machine learning algorithms attracting researchers' attention for their applicability to alternative energy-efficient hardware other than traditional computers. In the current work, spiking neural networks have been tested in a quantitative structure–activity analysis targeting the toxicity of molecules. Multiple public-domain databases of compounds have been evaluated with spiking neural networks, achieving accuracies compatible with high-quality frameworks presented in the previous literature. The numerical experiments also included an analysis of hyperparameters and tested the spiking neural networks on molecular fingerprints of different lengths. Proposing alternatives to traditional software and hardware for time- and resource-consuming tasks, such as those found in chemoinformatics, may open the door to new research and improvements in the field. [ABSTRACT FROM AUTHOR]

[Display omitted] • Machine learning models trained on conjoint fingerprints achieve R2 values exceeding 0.9. • A novel structure-constrained molecular generator is employed for systematic chemical structure generation. • Explainable artificial intelligence techniques are applied to achieve atomic-level visualization and quantitative analysis. • Case studies demonstrate the effectiveness of the developed computer-aided molecular design framework. This work presents an advanced computer-aided molecular design framework by considering the molecular structure of fuels as the fundamental design degree of freedom. Initially, machine learning algorithms were trained with conjoint fingerprints along with extended connectivity fingerprints, molecular access system keys and functional groups fingerprint separately. Conjoint fingerprints demonstrated the highest predictive accuracy, with R2 values generally exceeding 0.9 for physicochemical properties. Subsequently, a novel structure-constrained molecular generator was introduced to systematically generate chemical structures by exploring all rule-based possible isomers of a given target molecule, aiming to produce high-performance fuels. Computational property prediction was employed to virtually screen the generated structures against desired physicochemical fuel property constraints. Finally, explainable artificial intelligence techniques were then applied to achieve atomic-level visualization and quantitative analysis by overlaying the target molecular structure with the atomic contribution values gained for specific properties, providing detailed insights that improve the understanding of how fuel molecular structures affect properties and aid in designing new molecules compared to traditional qualitative analysis. Two case studies were dedicated to illustrating the framework for (i) molecular generation tailored for compression-ignition engines and (ii) analyzing cetane number attributions based on atomic contributions. [ABSTRACT FROM AUTHOR]

Reactive bromine species (RBS) such as bromine atom (Br•) and dibromine radical (Br 2 •−) are important oxidative species accounting for the transformation of organic compounds in bromide-containing water. This study developed quantitative structure−activity relationship (QSAR) models to predict second order rate constants (k) of RBS by machine learning (ML) and conducted knowledge transfer between RBS and reactive chlorine species (RCS, e.g., Cl• and Cl 2 •−) to improve model performance. The ML-based models (RMSE test = 0.476 −0.712) outperformed the multiple linear regression-based models (RMSE test = 0.572 −3.68) for predicting k of RBS. In addition, the combination of molecular fingerprints (MFs) and quantum descriptors (QDs) as input features improved the performance of ML-based models (RMSE test = 0.476 −0.712) compared to those developed by MFs (RMSE test = 0.524 −0.834) or QDs (RMSE test = 0.572 −0.806) alone. E HOMO and E gap were identified to be the most important features affecting k of RBS based on SHAP analysis. A unified model integrating the datasets of four reactive halogen species (RHS, e.g., Br•, Br 2 •−, Cl• and Cl 2 •−) was further developed (R 2 test = 0.802), which showed better predictive performance than the individual models (R 2 test = 0.521 −0.776). Meanwhile, the model performance changed differently by employing knowledge transfer among RHS, which was improved for Br•/Cl•, mixed for Br•/Br 2 •− and Cl•/Cl 2 •−, but worse for Br 2 •−/Cl 2 •−. This study provides useful tools for predicting k of RHS in aqueous environments. [Display omitted] • Machine learning based QSAR models for predicting rate constants of Br• and Br 2 •− were first developed. • Combination of molecular fingerprints and quantum descriptors as input features improved the model performance. • A unified model integrating the datasets of RBS and RCS outperformed the individual models. • Knowledge transfer among RHS improved their model performance, especially for Br• and Cl•. [ABSTRACT FROM AUTHOR]

Organic solar cells (OSCs) are gaining fame for their cost-effective solution processing. Machine learning is increasingly popular for material design in OSCs. In this study, molecular fingerprints are used to train over 40 machine learning models. The random forest regressor emerges as the most predictive one. 10k new dyes are generated. A pre-trained ML model is used to predict their reorganization energy values. Dyes are selected on the basis of reorganization energy, dyes with lower reorganization energy are retrained. The synthetic accessibility of chosen dyes is then analyzed. Chemical similarity analysis has indicated reasonable resemble among selected dyes. [Display omitted] • Machine learning optimized dye design for lower hole reorganization energy. • Trained over 40 models, Random Forest is identified as top predictor. • Generated 10k new dyes for comprehensive analysis. • Dyes with lower reorganization energy are prioritized and retrained. • Analysis confirms accessibility of chosen dyes for practical application. [ABSTRACT FROM AUTHOR]

The composition of the juice from grape berries is at the basis of the definition of technological ripeness before harvest, historically evaluated from global sugar and acid contents. If many studies have contributed to the identification of other primary and secondary metabolites in whole berries, deepening knowledge about the chemical composition of the sole flesh of grape berries (i.e., without considering skins and seeds) at harvest is of primary interest when studying the enological potential of widespread grape varieties producing high-added-value wines. Here, we used non-targeted DI-FT-ICR-MS and RP-UHPLC-Q-ToF-MS analyses to explore the extent of metabolite coverage of up to 290 grape juices from four Vitis vinifera grape varieties, namely Chardonnay, Pinot noir, Meunier, and Aligoté, sampled at harvest from 91 vineyards in Europe and Argentina, over three successive vintages. SPE pretreatment of samples led to the identification of more than 4500 detected C,H,O,N,S-containing elemental compositions, likely associated with tens of thousands of distinct metabolites. We further revealed that a major part of this chemical diversity appears to be common to the different juices, as exemplified by Pinot noir and Chardonnay samples. However, it was possible to build significant models for the discrimination of Chardonnay from Pinot noir grape juices, and of Chardonnay from Aligoté grape juices, regardless of the geographical origin or the vintage. Therefore, this metabolomic approach opens access to a remarkable holistic molecular description of the instantaneous composition of such a biological matrix, which is the result of complex interplays among environmental, biochemical, and vine growing practices.

Background: Phytochemicals or secondary metabolites are low molecular weight organic compounds with little function in plant growth and development. Nevertheless, the metabolite diversity govern not only the phenetics of an organism but may also inform the evolutionary pattern and adaptation of green plants to the changing environment. Plant chemoinformatics analyzes the chemical system of natural products using computational tools and robust mathematical algorithms. It has been a powerful approach for species-level differentiation and is widely employed for species classifications and reinforcement of previous classifications. Results: This study attempts to classify Angiosperms using plant sulfur-containing compound (SCC) or sulphated compound information. The SCC dataset of 692 plant species were collected from the comprehensive species-metabolite relationship family (KNApSAck) database. The structural similarity score of metabolite pairs under all possible combinations (plant species-metabolite) were determined and metabolite pairs with a Tanimoto coefficient value > 0.85 were selected for clustering using machine learning algorithm. Metabolite clustering showed association between the similar structural metabolite clusters and metabolite content among the plant species. Phylogenetic tree construction of Angiosperms displayed three major clades, of which, clade 1 and clade 2 represented the eudicots only, and clade 3, a mixture of both eudicots and monocots. The SCC-based construction of Angiosperm phylogeny is a subset of the existing monocot-dicot classification. The majority of eudicots present in clade 1 and 2 were represented by glucosinolate compounds. These clades with SCC may have been a mixture of ancestral species whilst the combinatorial presence of monocot-dicot in clade 3 suggests sulphated-chemical structure diversification in the event of adaptation during evolutionary change. Conclusions: Sulphated chemoinformatics informs classification of Angiosperms via machine learning technique. [ABSTRACT FROM AUTHOR]

Four compounds, hippacine, 4,2′-dihydroxy-4′-methoxychalcone, 2′,5′-dihydroxy-4-methoxychalcone, and wighteone, were selected from 4924 African natural metabolites as potential inhibitors against SARS-CoV-2 papain-like protease (PLpro, PDB ID: 3E9S). A multi-phased in silico approach was employed to select the most similar metabolites to the co-crystallized ligand (TTT) of the PLpro through molecular fingerprints and structural similarity studies. Followingly, to examine the binding of the selected metabolites with the PLpro (molecular docking. Further, to confirm this binding through molecular dynamics simulations. Finally, in silico ADMET and toxicity studies were carried out to prefer the most convenient compounds and their drug-likeness. The obtained results could be a weapon in the battle against COVID-19 via more in vitro and in vivo studies. [ABSTRACT FROM AUTHOR]

This work applies the concept of structural causal modelling (SCM) for the prediction of eutectic temperatures of choline chloride based deep eutectic solvents (DES). Two SCM models were developed, one based on molecular descriptors (MD), and the other based on molecular fingerprints (MF). The models are presented in the form of directed acyclic graphs (DAG). The SCM-MD model shows that the chi simple cluster connectivity descriptor (SC.5) and a number of hydrogen atoms (nH.1) are the key causal variables. The causal relations between the model variables and eutectic temperature were determined after performing d-separation to block the variable confounding interference. The corresponding nonlinear causal relations were modelled by Bayes neural network with a single inner layer. Based on the SCM-MD model, a decision tree is proposed for the prediction of eutectic temperatures. Model performances were tested on a literature dataset of eutectic temperatures of ChCl based DESs. The SCM-MD model provided the most accurate prediction with an error of 7.5 °C. [ABSTRACT FROM AUTHOR]

Histone deacetylase 8 (HDAC8) inhibitors play a pivotal role in epigenetic regulation. Numerous HDAC8 inhibitors (HDAC8is), that are non-hydroxamates have been identified to date, and a few of them exhibit antiproliferative activity that is on par with hydroxamates. While many non-hydroxamate-based HDAC8is have demonstrated selectivity, hydroxamate-based HDAC8is, like Vorinostat and TSA, have a tendency of non-specificity among the different HDAC isoforms. Moreover, because of the unfavorable toxic side effects, there are significant concerns surrounding the use of hydroxamate derivatives as therapeutic agents in cancer as well as other chronic diseases. Consequently, the research on non-hydroxamate-based HDAC8is is of utmost priority. In the present study, a comprehensive study was presented to unravel the structural requirements of non-hydroxamate-based HDAC8is from a diverse set of 866 compounds. The study utilized Classification-based Quantitative Structure-Activity Relationship (QSAR) analysis, incorporating Bayesian classification, Recursive partitioning, and other machine learning methods to pinpoint the key structural features essential for HDAC8 inhibition. To underscore and gain deeper insights into the identified structural features, molecular docking, and molecular dynamic (MD) simulation studies were conducted. The integration of these computational approaches unveiled key structural motifs essential for potent HDAC8 inhibitory activity, shedding light on the molecular basis of HDAC8 inhibition using non-hydroxamates., Competing Interests: Declaration of competing interest The authors declare no competing financial interest., (Copyright © 2024 Elsevier B.V. All rights reserved.)

Abstract Natural products (NPs) represent one of the most important resources for discovering new drugs. Here we asked whether NP origin can be assigned from their molecular structure in a subset of 60,171 NPs in the recently reported Collection of Open Natural Products (COCONUT) database assigned to plants, fungi, or bacteria. Visualizing this subset in an interactive tree-map (TMAP) calculated using MAP4 (MinHashed atom pair fingerprint) clustered NPs according to their assigned origin ( https://tm.gdb.tools/map4/coconut_tmap/ ), and a support vector machine (SVM) trained with MAP4 correctly assigned the origin for 94% of plant, 89% of fungal, and 89% of bacterial NPs in this subset. An online tool based on an SVM trained with the entire subset correctly assigned the origin of further NPs with similar performance ( https://np-svm-map4.gdb.tools/ ). Origin information might be useful when searching for biosynthetic genes of NPs isolated from plants but produced by endophytic microorganisms.

The main aim of this study is to find relevant analytic fingerprints for plants’ structural characterization using spectroscopic techniques and thermogravimetric analyses (TGAs) as alternative methods, particularized on cabbage treated with selenium–baker’s yeast vinasse formulation (Se-VF) included in a foliar fertilizer formula. The hypothesis investigated is that Se-VF will induce significant structural changes compared with the control, analytically confirming the biofortification of selenium-enriched cabbage as a nutritive vegetable, and particularly the plant biostimulant effects of the applied Se-VF formulation on cabbage grown in the field. The TGA evidenced a structural transformation of the molecular building blocks in the treated cabbage leaves. The ash residues increased after treatment, suggesting increased mineral accumulation in leaves. X-ray diffraction (XRD) and Fourier-transform infrared spectroscopy (FTIR) evidenced a pectin–Iα-cellulose structure of cabbage that correlated with each other in terms of leaf crystallinity. FTIR analysis suggested the accumulation of unesterified pectin and possibly (seleno) glucosinolates and an increased network of hydrogen bonds. The treatment with Se-VF formulation induced a significant increase in the soluble fibers of the inner leaves, accompanied by a decrease in the insoluble fibers. The ratio of soluble/insoluble fibers correlated with the crystallinity determined by XRD and with the FTIR data. The employed analytic techniques can find practical applications as fast methods in studies of the effects of new agrotechnical practices, while in our particular case study, they revealed effects specific to plant biostimulants of the Se-VF formulation treatment: enhanced mineral utilization and improved quality traits.

Characterization, identification, and detection of aerosol particles in their native atmospheric states remain a challenge. Recently, optical trapping-Raman spectroscopy (OT-RS) has been developed and demonstrated for characterization of single, airborne particles. Such particles in different chemical groups have been characterized by OT-RS in recent years and many more are being studied. In this work, we collected single-particle Raman spectra measured using the OT-RS technique and began construction of a library of OT-RS fingerprints that may be used as a reference for potential detection and identification of aerosol particles in the atmosphere. We collected OT-RS fingerprints of aerosol particles from eight different categories including carbons, bioaerosols (pollens, fungi, vitamins, spores), dusts, biological warfare agent surrogates, etc. Among the eight categories, spectral fingerprints of six groups of aerosol particles have been published previously and two other groups are new. We also discussed challenges, limitations, and advantages of using single-particle optical trapping-Raman spectroscopy for aerosol-particle characterization, identification, and detection. [ABSTRACT FROM AUTHOR]

The identification of chemical–disease association types is helpful not only to discovery lead compounds and study drug repositioning, but also to treat disease and decipher pathomechanism. It is very urgent to develop computational method for identifying potential chemical–disease association types, since wet methods are usually expensive, laborious and time-consuming. In this study, molecular fingerprint, gene ontology and pathway are utilized to characterize chemicals and diseases. A novel predictor is proposed to recognize potential chemical–disease associations at the first layer, and further distinguish whether their relationships belong to biomarker or therapeutic relations at the second layer. The prediction performance of current method is assessed using the benchmark dataset based on ten-fold cross-validation. The practical prediction accuracies of the first layer and the second layer are 78.47% and 72.07%, respectively. The recognition ability for lead compounds, new drug indications, potential and true chemical–disease association pairs has also been investigated and confirmed by constructing a variety of datasets and performing a series of experiments. It is anticipated that the current method can be considered as a powerful high-throughput virtual screening tool for drug researches and developments. [ABSTRACT FROM AUTHOR]

Methods for the pairwise comparison of 2D and 3D molecular structures are established approaches in virtual screening. In this work, we explored three strategies for maximizing the virtual screening performance of these methods: (i) the merging of hit lists obtained from multi-compound screening using a single screening method, (ii) the merging of the hit lists obtained from 2D and 3D screening by parallel selection, and (iii) the combination of both of these strategies in an integrated approach. We found that any of these strategies led to a boost in virtual screening performance, with the clearest advantages observed for the integrated approach. On test sets for virtual screening, covering 50 pharmaceutically relevant proteins, the integrated approach, using sets of five query molecules, yielded, on average, an area under the receiver operating characteristic curve (AUC) of 0.84, an early enrichment among the top 1% of ranked compounds (EF1%) of 53.82 and a scaffold recovery rate among the top 1% of ranked compounds (SRR1%) of 0.50. In comparison, the 2D and 3D methods on their own (when using a single query molecule) yielded AUC values of 0.68 and 0.54, EF1% values of 19.96 and 17.52, and SRR1% values of 0.20 and 0.17, respectively. In conclusion, based on these results, the integration of 2D and 3D methods, via a (balanced) parallel selection strategy, is recommended, and, in particular, when combined with multi-query screening. [ABSTRACT FROM AUTHOR]