Authentication of the species identity of squid rings using UHPLC-Q-Orbitrap MS/MS-based lipidome fingerprinting and chemoinformatics

Species mislabeling of commercial loliginidae squid can undermine important conservation efforts and prevent consumers from making informed decisions. A comprehensive lipidomic fingerprint of Uroteuthis singhalensis, Uroteuthis edulis, and Uroteuthis duvauceli rings was established using high-resolution mass spectrometry-based lipidomics and chemoinformatics analysis. The principal component analysis showed a clear separation of sample groups, with R2X and Q2 values of 0.97 and 0.85 for ESI+ and 0.96 and 0.86 for ESI-, indicating a good model fit. The optimized OPLS-DA and PLS-DA models could discriminate the species identity of validation samples with 100 % accuracy. A total of 67 and 90 lipid molecules were putatively identified as biomarkers in ESI+ and ESI-, respectively. Identified lipids, including PC(40:6), C14 sphingomyelin, PS(O-36:0), and PE(41:4), played an important role in species discrimination. For the first time, this study provides a detailed lipidomics profile of commercially important loliginidae squid and establishes a faster workflow for species authentication.

Synthesis and Chemoinformatic Analysis of Fluorinated Piperidines as 3D Fragments for Fragment-Based Drug Discovery

The concise synthesis of a small library of fluorinated piperidines from readily available dihydropyridinone derivatives has been described. The effect of the fluorination on different positions has then been evaluated by chemoinformatic tools. In particular, the compounds' pKa's have been calculated, revealing that the fluorine atoms notably lowered their basicity, which is correlated to the affinity for hERG channels resulting in cardiac toxicity. The "lead-likeness" and three-dimensionality have also been evaluated to assess their ability as useful fragments for drug design. A random screening on a panel of representative proteolytic enzymes was then carried out and revealed that one scaffold is recognized by the catalytic pocket of 3CLPro (main protease of SARS-CoV-2 coronavirus).

Chemoinformatic approaches for navigating large chemical spaces

INTRODUCTION

Large chemical spaces (CSs) include traditional large compound collections, combinatorial libraries covering billions to trillions of molecules, DNA-encoded chemical libraries comprising complete combinatorial CSs in a single mixture, and virtual CSs explored by generative models. The diverse nature of these types of CSs require different chemoinformatic approaches for navigation.

AREAS COVERED

An overview of different types of large CSs is provided. Molecular representations and similarity metrics suitable for large CS exploration are discussed. A summary of navigation of CSs in generative models is provided. Methods for characterizing and comparing CSs are discussed.

EXPERT OPINION

The size of large CSs might restrict navigation to specialized algorithms and limit it to considering neighborhoods of structurally similar molecules. Efficient navigation of large CSs not only requires methods that scale with size but also requires smart approaches that focus on better but not necessarily larger molecule selections. Deep generative models aim to provide such approaches by implicitly learning features relevant for targeted biological properties. It is unclear whether these models can fulfill this ideal as validation is difficult as long as the covered CSs remain mainly virtual without experimental verification.

Exploiting Vector Pattern Diversity of Molecular Scaffolds for Cheminformatics Tasks in Drug Discovery

Chemical diversity is challenging to describe objectively. Despite this, various notions of chemical diversity are used throughout the medicinal chemistry optimization process in drug discovery. In this work, we show the usefulness of considering exploited vectors during different phases of the drug design process to provide a quantitative and objective description of chemical diversity. We have developed a concise and fast approach to enumerate and analyze the exploited vector patterns (EVPs) of molecular compound series, which can then be used in archetypal compound selection tasks, from hit matter identification to hit expansion and lead optimization. We first show that EVPs can be used to assess the progressibility of compounds in a fragment library design exercise. By considering EVPs, we then show how a set of compounds can be prioritized for hit expansion using EVP-based, customizable diversity sampling approaches, reducing the time taken and mitigating human biases. We also show that EVPs are a useful tool to analyze SAR data, offering the chance to uncover correlations between different vectors without predetermining the molecular scaffold structures. The codes used to perform these tasks are presented as easy-to-use Jupyter notebooks, which can be readily adapted for further related tasks.

Computational resources and chemoinformatics for translational health research

The integration of computational resources and chemoinformatics has revolutionized translational health research. It has offered a powerful set of tools for accelerating drug discovery. This chapter overviews the computational resources and chemoinformatics methods used in translational health research. The resources and methods can be used to analyze large datasets, identify potential drug candidates, predict drug-target interactions, and optimize treatment regimens. These resources have the potential to transform the drug discovery process and foster personalized medicine research. We discuss insights into their various applications in translational health and emphasize the need for addressing challenges, promoting collaboration, and advancing the field to fully realize the potential of these tools in transforming healthcare.

Creation of Polymer Datasets with Targeted Backbones for Screening of High-Performance Membranes for Gas Separation

A simple approach was developed to computationally construct a polymer dataset by combining simplified molecular-input line-entry system (SMILES) strings of a targeted polymer backbone and a variety of molecular fragments. This method was used to create 14 polymer datasets by combining seven polymer backbones and molecules from two large molecular datasets (MOSES and QM9). Polymer backbones that were studied include four polydimethylsiloxane (PDMS) based backbones, poly(ethylene oxide) (PEO), poly(allyl glycidyl ether) (PAGE), and polyphosphazene (PPZ). The generated polymer datasets can be used for various cheminformatics tasks, including high-throughput screening for gas permeability and selectivity. This study utilized machine learning (ML) models to screen the polymers for CO2/CH4 and CO2/N2 gas separation using membranes. Several polymers of interest were identified. The results highlight that employing an ML model fitted to polymer selectivities leads to higher accuracy in predicting polymer selectivity compared to using the ratio of predicted permeabilities.

Curation and cheminformatics analysis of a Ugi-reaction derived library (URDL) of synthetically tractable small molecules for virtual screening application

Virtual screening (VS) is an important approach in drug discovery and relies on the availability of a virtual library of synthetically tractable molecules. Ugi reaction (UR) represents an important multi-component reaction (MCR) that reliably produces a peptidomimetic scaffold. Recent literature shows that a tactically assembled Ugi adduct can be subjected to further chemical modifications to yield a variety of rings and scaffolds, thus, renewing the interest in this old reaction. Given the reliability and efficiency of UR, we collated an UR derived library (URDL) of small molecules (total = 5773) for VS. The synthesis of the majority of URDL molecules may be carried out in 1-2 pots in a time and cost-effective manner. The detailed analysis of the average property and chemical space of URDL was also carried out using the open-source Datawarrior program. The comparison with FDA-approved oral drugs and inhibitors of protein-protein interactions (iPPIs) suggests URDL molecules are 'clean', drug-like, and conform to a structurally distinct space from the other two categories. The average physicochemical properties of compounds in the URDL library lie closer to iPPI molecules than oral drugs thus suggesting that the URDL resource can be applied to discover novel iPPI molecules. The URDL molecules consist of diverse ring systems, many of which have not been exploited yet for drug design. Thus, URDL represents a small virtual library of drug-like molecules with unexplored chemical space designed for VS. The structures of all molecules of URDL, oral drugs, and iPPI compounds are being made freely accessible as supplementary information for broader application.

When Yield Prediction Does Not Yield Prediction: An Overview of the Current Challenges

Machine Learning (ML) techniques face significant challenges when predicting advanced chemical properties, such as yield, feasibility of chemical synthesis, and optimal reaction conditions. These challenges stem from the high-dimensional nature of the prediction task and the myriad essential variables involved, ranging from reactants and reagents to catalysts, temperature, and purification processes. Successfully developing a reliable predictive model not only holds the potential for optimizing high-throughput experiments but can also elevate existing retrosynthetic predictive approaches and bolster a plethora of applications within the field. In this review, we systematically evaluate the efficacy of current ML methodologies in chemoinformatics, shedding light on their milestones and inherent limitations. Additionally, a detailed examination of a representative case study provides insights into the prevailing issues related to data availability and transferability in the discipline.

Exploring the Role of Chemoinformatics in Accelerating Drug Discovery: A Computational Approach

Cheminformatics and its role in drug discovery is expected to be the privileged approach in handling large number of chemical datasets. This approach contributes toward the pharmaceutical development and assessment of chemical compounds at a faster rate efficiently. Additionally, as technological advancement impacts research, cheminformatics is being used more and more in the field of health science. This chapter describes the concepts of cheminformatics along with its involvement in drug discovery with a case study.

GUIDEMOL: A Python graphical user interface for molecular descriptors based on RDKit

GUIDEMOL is a Python computer program based on the RDKit software to process molecular structures and calculate molecular descriptors with a graphical user interface using the tkinter package. It can calculate descriptors already implemented in RDKit as well as grid representations of 3D molecular structures using the electrostatic potential or voxels. The GUIDEMOL app provides easy access to RDKit tools for chemoinformatics users with no programming skills and can be adapted to calculate other descriptors or to trigger other procedures. A command line interface (CLI) is also provided for the calculation of grid representations. The source code is available at https://github.com/jairesdesousa/guidemol.

Cheminformatics identification of phenolics as modulators of penicillin-binding protein-3 of Pseudomonas aeruginosa towards interventive antibacterial therapy

Antibacterial resistance to β-lactams in microorganisms has been attributed majorly to alterations in penicillin-binding proteins (PBPs) coupled with β-lactams' inactivation by β-lactamase. Consequently, the identification of a novel class of therapeutics with improved modulatory action on the PBPs is imperative and plant secondary metabolites, including phenolics, have found relevance in this regard. For the first time in this study, the over 10,000 phenolics currently known were computationally evaluated against PBP3 of Pseudomonas aeruginosa, a superbug implicated in several nosocomial infections. In doing this, a library of phenolics with an affinity for PBP3 of P. aeruginosa was screened using structure-activity relationship-based pharmacophore and molecular docking approaches. Subsequent thermodynamic screening of the top five phenolics with higher docking scores, more drug-likeness attributes, and feasible synthetic accessibility was achieved through a 120 ns molecular dynamic (MD) simulation. Four of the top five hits had higher binding free energy than cefotaxime (-18.72 kcal/mol), with catechin-3-rhamside having the highest affinity (-28.99 kcal/mol). All the hits were stable at the active site of the PBP3, with catechin-3-rhamside being the most stable (2.14 Å), and established important interactions with Ser294, implicated in the catalytic activity of PBP3. Also, PBP3 became more compact with less fluctuation of the active site amino acid residues following the binding of the hits. These observations are indicative of the potential of the test compounds as PBP3 inhibitors, with catechin-3-rhamside being the most prominent of the compounds that could be further improved for enhanced druggability against PBP3 in vitro and in vivo.Communicated by Ramaswamy H. Sarma.

Protein-Ligand Blind Docking Using CB-Dock2

Protein-ligand blind docking is a widely used method for studying the binding sites and poses of ligands and receptors in pharmaceutical and biological research. Recently, our new blind docking server named CB-Dock2 has been released and is currently being utilized by researchers worldwide. CB-Dock2 outperforms state-of-the-art methods due to its accuracy in binding site identification and binding pose prediction, which are enabled by its knowledge-based docking engine. This highly automated server offers interactive and intuitive input and output web interfaces, making it an efficient and user-friendly tool for the bioinformatics and cheminformatics communities. This chapter provides a brief overview of the methods, followed by a detailed guide on using the CB-Dock2 server. Additionally, we present a case study that evaluates the performance of protein-ligand blind docking using this tool.

Biotransformation of Δ1-Progesterone Using Selected Entomopathogenic Filamentous Fungi and Prediction of Its Products' Bioactivity

This research aimed at obtaining new derivatives of pregn-1,4-diene-3,20-dione (Δ1-progesterone) (2) through microbiological transformation. For the role of catalysts, we used six strains of entomopathogenic filamentous fungi (Beauveria bassiana KCh J1.5, Beauveria caledonica KCh J3.3, Isaria fumosorosea KCh J2, Isaria farinosa KCh KW1.1, Isaria tenuipes MU35, and Metarhizium robertsii MU4). The substrate (2) was obtained by carrying out an enzymatic 1,2-dehydrogenation on an increased scale (3.5 g/L) using a recombinant cholest-4-en-3-one Δ1-dehydrogenase (AcmB) from Sterolibacterium denitrificans. All selected strains were characterized by the high biotransformation capacity for the used substrate. As a result of the biotransformation, six steroid derivatives were obtained: 11α-hydroxypregn-1,4-diene-3,20-dione (3), 6β,11α-dihydroxypregn-1,4-diene-3,20-dione (4), 6β-hydroxypregn-1,4-diene-3,11,20-trione (5), 6β,17α-dihydroxypregn-1,4-diene-3,20-dione (6), 6β,17β-dihydroxyandrost-1,4-diene-3-one (7), and 12β,17α-dihydroxypregn-1,4-diene-3,20-dione (8). The results show evident variability of the biotransformation process between strains of the tested biocatalysts from different species described as entomopathogenic filamentous fungi. The obtained products were tested in silico using cheminformatics tools for their pharmacokinetic and pharmacodynamic properties, proving their potentially high biological activities. This study showed that the obtained compounds may have applications as effective inhibitors of testosterone 17β-dehydrogenase. Most of the obtained products should, also with a high probability, find potential uses as androgen antagonists, a prostate as well as menopausal disorders treatment. They should also demonstrate immunosuppressive, erythropoiesis-stimulating, and anti-inflammatory properties.

Fragments quantum descriptors in classification of bio-accumulative compounds

The aim of the following research is to assess the applicability of calculated quantum properties of molecular fragments as molecular descriptors in machine learning classification task. The research is based on bio-concentration and QM9-extended databases. A number of compounds with results from quantum-chemical calculations conducted with Psi4 quantum chemistry package was also added to the quantum properties database. Classification results are compared with a baseline of random guesses and predictions obtained with the traditional RDKit generated molecular descriptors. Chosen classification metrics show that results obtained with fragments quantum descriptors fall between results from baseline and those provided by molecular descriptors widely applied in cheminformatics. According to the results, the implementation of principal component analysis, causes a drop in categorization metrics.

Vaccinomics-aided next-generation novel multi-epitope-based vaccine engineering against multidrug resistant Shigella Sonnei: Immunoinformatics and chemoinformatics approaches

Shigella sonnei is a gram-negative bacterium and is the primary cause of shigellosis in advanced countries. An exceptional rise in the prevalence of the disease has been reported in Asia, the Middle East, and Latin America. To date, no preventive vaccine is available against S. sonnei infections. This pathogen has shown resistances towards both first- and second-line antibiotics. Therefore, an effective broad spectrum vaccine development against shigellosis is indispensable. In the present study, vaccinomics-aided immunoinformatics strategies were pursued to identify potential vaccine candidates from the S. sonnei whole proteome data. Pathogen essential proteins that are non-homologous to human and human gut microbiome proteome set, are feasible candidates for this purpose. Three antigenic outer membrane proteins were prioritized to predict lead epitopes based on reverse vaccinology approach. Multi-epitope-based chimeric vaccines was designed using lead B- and T-cell epitopes combined with suitable linker and adjuvant peptide sequences to enhance immune responses against the designed vaccine. The SS-MEVC construct was prioritized based on multiple physicochemical, immunological properties, and immune-receptors docking scores. Immune simulation analysis predicted strong immunogenic response capability of the designed vaccine construct. The Molecular dynamic simulations analysis ensured stable molecular interactions of lead vaccine construct with the host receptors. In silico restriction and cloning analysis predicted feasible cloning capability of the SS-MEVC construct within the E. coli expression system. The proposed vaccine construct is predicted to be more safe, effective and capable of inducing robust immune responses against S. sonnei infections and may be worthy of examination via in vitro/in vivo assays.

Selection of Mexican Medicinal Plants by Identification of Potential Phytochemicals with Anti-Aging, Anti-Inflammatory, and Anti-Oxidant Properties through Network Analysis and Chemoinformatic Screening

Many natural products have been acquired from plants for their helpful properties. Medicinal plants are used for treating a variety of pathologies or symptoms. The axes of many pathological processes are inflammation, oxidative stress, and senescence. This work is focused on identifying Mexican medicinal plants with potential anti-oxidant, anti-inflammatory, anti-aging, and anti-senescence effects through network analysis and chemoinformatic screening of their phytochemicals. We used computational methods to analyze drug-like phytochemicals in Mexican medicinal plants, multi-target compounds, and signaling pathways related to anti-oxidant, anti-inflammatory, anti-aging, and anti-senescence mechanisms. A total of 1373 phytochemicals are found in 1025 Mexican medicinal plants, and 148 compounds showed no harmful functionalities. These compounds displayed comparable structures with reference molecules. Based on their capacity to interact with pharmacological targets, three clusters of Mexican medicinal plants have been established. Curatella americana, Ximenia americana, Malvastrum coromandelianum, and Manilkara zapota all have anti-oxidant, anti-inflammatory, anti-aging, and anti-senescence effects. Plumeria rubra, Lonchocarpus yucatanensis, and Salvia polystachya contained phytochemicals with anti-oxidant, anti-inflammatory, anti-aging, and anti-senescence reported activity. Lonchocarpus guatemalensis, Vallesia glabra, Erythrina oaxacana, and Erythrina sousae have drug-like phytochemicals with potential anti-oxidant, anti-inflammatory, anti-aging, and anti-senescence effects. Between the drug-like phytochemicals, lonchocarpin, vallesine, and erysotrine exhibit potential anti-oxidant, anti-inflammatory, anti-aging, and anti-senescence effects. For the first time, we conducted an initial virtual screening of selected Mexican medicinal plants, which was subsequently confirmed in vivo, evaluating the anti-inflammatory activity of Lonchocarpus guatemalensis Benth in mice.

Anonymous Pattern Molecular Fingerprint and its Applications on Property Identification

Molecular fingerprints are significant cheminformatics tools to map molecules into vectorial space according to their characteristics in diverse functional groups, atom sequences, and other topological structures. In this paper, we investigate a novel molecular fingerprint Anonymous-FP that possesses abundant perception about the underlying interactions shaped in small, medium, and large-scale atom chains. In detail, the possible atom chains from each molecule are sampled and extended as anonymous atom chains using an anonymous encoding manner. After that, the molecular fingerprint Anonymous-FP is embedded into vectorial space in virtue of the Natural Language Processing technique PV-DBOW. Anonymous-FP is studied on molecular property identification via molecule classification experiments on a series of molecule databases and has shown valuable advantages such as less dependence on prior knowledge, rich information content, full structural significance, and high experimental performance. During the experimental verification, the scale of the atom chain or its anonymous pattern is found significant to the overall representation ability of Anonymous-FP. Generally, the typical scale r = 8 could enhance the molecule classification performance, and specifically, Anonymous-FP gains the classification accuracy to above 93% on all NCI datasets.

Combination of UHPLC-MS/MS with context-specific network and cheminformatic approaches for identifying bioactivities and active components of propolis

Discovering new bioactivities and identifying active compounds of food materials are major fields of study in food science. However, the process commonly requires extensive experiments and can be technically challenging. In the current study, we employed network biology and cheminformatic approaches to predict new target diseases, active components, and related molecular mechanisms of propolis. Applying UHPLC-MS/MS analysis results of propolis to Context-Oriented Directed Associations (CODA) and Combination-Oriented Natural Product Database with Unified Terminology (COCONUT) systems indicated atopic dermatitis as a novel target disease. Experimental validation using cell- and human tissue-based models confirmed the therapeutic potential of propolis against atopic dermatitis. Moreover, we were able to find the major contributing compounds as well as their combinatorial effects responsible for the bioactivity of propolis. The CODA/COCONUT system also provided compound-associated genes explaining the underlying molecular mechanism of propolis. These results highlight the potential use of big data-driven network biological approaches to aid in analyzing the impact of food constituents at a systematic level.

Cheminformatics and systems pharmacology approaches to unveil the potential plant bioactives to combat COVID-19

COVID-19 was a global pandemic in the year 2020. Several treatment options failed to cure the disease. Thus, plant-based medicines are becoming a trend nowadays due to their less side effects. Bioactive chemicals from natural sources have been utilised for centuries as treatment options for a variety of ailments. To find out the potent bioactive compounds to counteract COVID-19, we use systems pharmacology and cheminformatics. They use the definitive data and predict the possible outcomes. In this study, we collected a total of 72 phytocompounds from the medicinally important plants such as Garcinia mangostana and Cinnamomum verum, of which 13 potential phytocompounds were identified to be active against the COVID-19 infection based on Swiss Target Prediction and compound target network analysis. These phytocompounds were annotated to identify the specific human receptor that targets COVID-19-specific genes such as MAPK8, MAPK14, ACE, CYP3A4, TLR4 and TYK2. Among these, compounds such as smeathxanthone A, demethylcalabaxanthone, mangostanol, trapezifolixanthone from Garcinia mangostana and camphene from C. verum were putatively target various COVID-19-related genes. Molecular docking results showed that smeathxanthone A and demethylcalabaxanthone exhibit increased binding efficiency towards the COVID-19-related receptor proteins. These compounds also showed efficient putative pharmacoactive properties than the commercial drugs ((R)-remdesivir, favipiravir and hydroxychloroquine) used to cure COVID-19. In conclusion, our study highlights the use of cheminformatics approach to unravel the potent and novel phytocompounds against COVID-19. These phytocompounds may be safer to use, more efficient and less harmful. This study highlights the value of natural products in the search for new drugs and identifies candidates with great promise.

Descriptor-Driven de Novo Design Algorithms for DOCK6 Using RDKit

Structure-based methods that employ principles of de novo design can be used to construct small organic molecules from scratch using pre-existing fragment libraries to sample chemical space and are an important class of computational algorithms for drug-lead discovery. Here, we present a powerful new design method for DOCK6 that employs a Descriptor-Driven De Novo strategy (termed D3N) in which user-defined cheminformatics descriptors (and their target ranges) are calculated at each layer of growth using the open-source toolkit RDKit. The objective is to tailor ligand growth toward desirable regions of chemical space. The approach was extensively validated through: (1) comparison of cheminformatics descriptors computed using the new DOCK6/RDKit interface versus the standard Python/RDKit installation, (2) examination of descriptor distributions generated using D3N growth under different conditions (target ranges and environments), and (3) construction of ligands with very tight (pinpoint) descriptor ranges using clinically relevant compounds as a reference. Our testing confirms that the new DOCK6/RDKit integration is robust, showcases how the new D3N routines can be used to direct sampling around user-defined chemical spaces, and highlights the utility of on-the-fly descriptor calculations for ligand design to important drug targets.

A chemoinformatic analysis on natural glycosides with respect to biological origin and structural class

Covering: up to 202216.19% of reported natural products (NPs) in the Dictionary of Natural Products (DNP) are glycosides. As one of the most important NPs' structural modifications, glycosylation can change the NPs' polarity, making the aglycones more amphipathic. However, until now, little is known about the general distribution profile of the natural glycosides in different biological sources or structural types. The reason, structural or species preferences of the natural glycosylation remain unclear. In this highlight, chemoinformatic methods were employed to analyze the natural glycosides from DNP, the most comprehensively annotated NP database. We found that the glycosylation ratios of NPs from plants, bacteria, animals and fungi decrease successively, which are 24.99%, 20.84%, 8.40% and 4.48%, respectively. Echinoderm-derived NPs (56.11%) are the most frequently glycosylated, while those produced by molluscs (1.55%), vertebrates (2.19%) and Rhodophyta (3.00%) are the opposite. Among the diverse structural types, a large proportion of steroids (45.19%), tannins (44.78%) and flavonoids (39.21%) are glycosides, yet aminoacids and peptides (5.16%), alkaloids (5.66%) are comparatively less glycosylated. Even within the same biological source or structural type, their glycosylation rates fluctuate drastically between sub- or cross-categories. The substitute patterns of flavonoid and terpenoid glycosides and the most frequently glycosylated scaffolds were identified. NPs with different glycosylation levels occupy different chemical spaces of physicochemical property and scaffold. These findings could help us to interpret the preference of NPs' glycosylation and investigate how NP glycosylation could aid NP-based drug discovery.

Cheminformatics-driven discovery of hit compounds against Paracoccidioides spp

Aims: The development of safe and effective therapies for treating paracoccidioidomycosis using computational strategies were employed to discover anti-Paracoccidioides compounds. Materials & methods: We 1) collected, curated and integrated the largest library of compounds tested against Paracoccidioides spp.; 2) employed a similarity search to virtually screen the ChemBridge database and select nine compounds for experimental evaluation; 3) performed an experimental evaluation to determine the minimum inhibitory concentration and minimum fungicidal concentration as well as cytotoxicity; and 4) employed computational tools to identify potential targets for the most active compounds. Seven compounds presented activity against Paracoccidioides spp. Conclusion: These compounds are new hits with a predicted mechanisms of action, making them potentially attractive to develop new compounds.

A principal odor map unifies diverse tasks in olfactory perception

Mapping molecular structure to odor perception is a key challenge in olfaction. We used graph neural networks to generate a principal odor map (POM) that preserves perceptual relationships and enables odor quality prediction for previously uncharacterized odorants. The model was as reliable as a human in describing odor quality: On a prospective validation set of 400 out-of-sample odorants, the model-generated odor profile more closely matched the trained panel mean than did the median panelist. By applying simple, interpretable, theoretically rooted transformations, the POM outperformed chemoinformatic models on several other odor prediction tasks, indicating that the POM successfully encoded a generalized map of structure-odor relationships. This approach broadly enables odor prediction and paves the way toward digitizing odors.

Cheminformatics Strategies Unlock Marburg Virus VP35 Inhibitors from Natural Compound Library

The Ebola virus and its close relative, the Marburg virus, both belong to the family Filoviridae and are highly hazardous and contagious viruses. With a mortality rate ranging from 23% to 90%, depending on the specific outbreak, the development of effective antiviral interventions is crucial for reducing fatalities and mitigating the impact of Marburg virus outbreaks. In this investigation, a virtual screening approach was employed to evaluate 2042 natural compounds for their potential interactions with the VP35 protein of the Marburg virus. Average and worst binding energies were calculated for all 20 poses, and compounds that exhibited binding energies <-6 kcal/mol in both criteria were selected for further analysis. Based on binding energies, only six compounds (Estradiol benzoate, INVEGA (paliperidone), Isosilybin, Protopanaxadiol, Permethrin, and Bufalin) were selected for subsequent investigations, focusing on interaction analysis. Among these selected compounds, Estradiol benzoate, INVEGA (paliperidone), and Isosilybin showed strong hydrogen bonds, while the others did not. In this study, the compounds Myricetin, Isosilybin, and Estradiol benzoate were subjected to a molecular dynamics (MD) simulation and free binding energy calculation using MM/GBSA analysis. The reference component Myricetin served as a control. Estradiol benzoate exhibited the most stable and consistent root-mean-square deviation (RMSD) values, whereas Isosilybin showed significant fluctuations in RMSD. The compound Estradiol benzoate exhibited the lowest ΔG binding free energy (-22.89 kcal/mol), surpassing the control compound's binding energy (-9.29 kcal/mol). Overall, this investigation suggested that Estradiol benzoate possesses favorable binding free energies, indicating a potential inhibitory mechanism against the VP35 protein of the Marburg virus. The study proposes that these natural compounds could serve as a therapeutic option for preventing Marburg virus infection. However, experimental validation is required to further corroborate these findings.

PREFER: A New Predictive Modeling Framework for Molecular Discovery

Machine-learning and deep-learning models have been extensively used in cheminformatics to predict molecular properties, to reduce the need for direct measurements, and to accelerate compound prioritization. However, different setups and frameworks and the large number of molecular representations make it difficult to properly evaluate, reproduce, and compare them. Here we present a new PREdictive modeling FramEwoRk for molecular discovery (PREFER), written in Python (version 3.7.7) and based on AutoSklearn (version 0.14.7), that allows comparison between different molecular representations and common machine-learning models. We provide an overview of the design of our framework and show exemplary use cases and results of several representation-model combinations on diverse data sets, both public and in-house. Finally, we discuss the use of PREFER on small data sets. The code of the framework is freely available on GitHub.

Identification of Macrocyclic Peptide Families from Combinatorial Libraries Containing Noncanonical Amino Acids Using Cheminformatics and Bioinformatics Inspired Clustering

In the past decade, macrocyclic peptides gained increasing interest as a new therapeutic modality to tackle intracellular and extracellular therapeutic targets that had been previously classified as "undruggable". Several technological advances have made discovering macrocyclic peptides against these targets possible: 1) the inclusion of noncanonical amino acids (NCAAs) into mRNA display, 2) increased availability of next generation sequencing (NGS), and 3) improvements in rapid peptide synthesis platforms. This type of directed-evolution based screening can produce large numbers of potential hit sequences given that DNA sequencing is the functional output of this platform. The current standard for selecting hit peptides from these selections for downstream follow-up relies on the frequency counting and sorting of unique peptide sequences which can result in the generation of false negatives due to technical reasons including low translation efficiency or other experimental factors. To overcome our inability to detect weakly enriched peptide sequences among our large data sets, we wanted to develop a clustering method that would enable the identification of peptide families. Unfortunately, utilizing traditional clustering algorithms, such as ClustalW, is not possible for this technology due to the incorporation of NCAAs in these libraries. Therefore, we developed a new atomistic clustering method with a Pairwise Aligned Peptide (PAP) chemical similarity metric to perform sequence alignments and identify macrocyclic peptide families. With this method, low enriched peptides, including isolated sequences (singletons), can now be clustered into families providing a comprehensive analysis of NGS data resulting from macrocycle discovery selections. Additionally, upon identification of a hit peptide with the desired activity, this clustering algorithm can be used to identify derivatives from the initial data set for structure-activity relationship (SAR) analysis without requiring additional selection experiments.

Cheminformatics-Based Study Identifies Potential Ebola VP40 Inhibitors

The Ebola virus (EBOV) is still highly infectious and causes severe hemorrhagic fevers in primates. However, there are no regulatorily approved drugs against the Ebola virus disease (EVD). The highly virulent and lethal nature of EVD highlights the need to develop therapeutic agents. Viral protein 40 kDa (VP40), the most abundantly expressed protein during infection, coordinates the assembly, budding, and release of viral particles into the host cell. It also regulates viral transcription and RNA replication. This study sought to identify small molecules that could potentially inhibit the VP40 protein by targeting the N-terminal domain using an in silico approach. The statistical quality of AutoDock Vina’s capacity to discriminate between inhibitors and decoys was determined, and an area under the curve of the receiver operating characteristic (AUC-ROC) curve of 0.791 was obtained. A total of 29,519 natural-product-derived compounds from Chinese and African sources as well as 2738 approved drugs were successfully screened against VP40. Using a threshold of −8 kcal/mol, a total of 7, 11, 163, and 30 compounds from the AfroDb, Northern African Natural Products Database (NANPDB), traditional Chinese medicine (TCM), and approved drugs libraries, respectively, were obtained after molecular docking. A biological activity prediction of the lead compounds suggested their potential antiviral properties. In addition, random-forest- and support-vector-machine-based algorithms predicted the compounds to be anti-Ebola with IC50 values in the micromolar range (less than 25 μM). A total of 42 natural-product-derived compounds were identified as potential EBOV inhibitors with desirable ADMET profiles, comprising 1, 2, and 39 compounds from NANPDB (2-hydroxyseneganolide), AfroDb (ZINC000034518176 and ZINC000095485942), and TCM, respectively. A total of 23 approved drugs, including doramectin, glecaprevir, velpatasvir, ledipasvir, avermectin B1, nafarelin acetate, danoprevir, eltrombopag, lanatoside C, and glycyrrhizin, among others, were also predicted to have potential anti-EBOV activity and can be further explored so that they may be repurposed for EVD treatment. Molecular dynamics simulations coupled with molecular mechanics Poisson–Boltzmann surface area calculations corroborated the stability and good binding affinities of the complexes (−46.97 to −118.9 kJ/mol). The potential lead compounds may have the potential to be developed as anti-EBOV drugs after experimental testing.

A New CSRML Structure-Based Fingerprint Method for Profiling and Categorizing Per- and Polyfluoroalkyl Substances (PFAS)

The term PFAS encompasses diverse per- and polyfluorinated alkyl (and increasingly aromatic) chemicals spanning industrial processes, commercial uses, environmental occurrence, and potential concerns. With increased chemical curation, currently exceeding 14,000 structures in the PFASSTRUCTV5 inventory on EPA's CompTox Chemicals Dashboard, has come increased motivation to profile, categorize, and analyze the PFAS structure space using modern cheminformatics approaches. Making use of the publicly available ToxPrint chemotypes and ChemoTyper application, we have developed a new PFAS-specific fingerprint set consisting of 129 TxP_PFAS chemotypes coded in CSRML, a chemical-based XML-query language. These are split into two groups, the first containing 56 mostly bond-type ToxPrints modified to incorporate attachment to either a CF group or F atom to enforce proximity to the fluorinated portion of the chemical. This focus resulted in a dramatic reduction in TxP_PFAS chemotype counts relative to the corresponding ToxPrint counts (averaging 54%). The remaining TxP_PFAS chemotypes consist of various lengths and types of fluorinated chains, rings, and bonding patterns covering indications of branching, alternate halogenation, and fluorotelomers. Both groups of chemotypes are well represented across the PFASSTRUCT inventory. Using the ChemoTyper application, we show how the TxP_PFAS chemotypes can be visualized, filtered, and used to profile the PFASSTRUCT inventory, as well as to construct chemically intuitive, structure-based PFAS categories. Lastly, we used a selection of expert-based PFAS categories from the OECD Global PFAS list to evaluate a small set of analogous structure-based TxP_PFAS categories. TxP_PFAS chemotypes were able to recapitulate the expert-based PFAS category concepts based on clearly defined structure rules that can be computationally implemented and reproducibly applied to process large PFAS inventories without need to consult an expert. The TxP_PFAS chemotypes have the potential to support computational modeling, harmonize PFAS structure-based categories, facilitate communication, and allow for more efficient and chemically informed exploration of PFAS chemicals moving forward.

Machine learning in antibacterial discovery and development: A bibliometric and network analysis of research hotspots and trends

Machine learning (ML) methods are used in cheminformatics processes to predict the activity of an unknown drug and thus discover new potential antibacterial drugs. This article conducts a bibliometric study to analyse the contributions of leading authors, universities/organisations and countries in terms of productivity, citations and bibliographic linkage. A sample of 1596 Scopus documents for the period 2006-2022 is the basis of the study. In order to develop the analysis, bibliometrix R-Tool and VOSviewer software were used. We determined essential topics related to the application of ML in the field of antibacterial development (Computer model in antibacterial drug design, and Learning algorithms and systems for forecasting). We identified obsolete and saturated areas of research. At the same time, we proposed emerging topics according to the various analyses carried out on the corpus of published scientific literature (Title, abstract and keywords). Finally, the applied methodology contributed to building a broader and more specific "big picture" of ML research in antibacterial studies for the focus of future projects.

Bioinformatics and cheminformatics approaches to identify pathways, molecular mechanisms and drug substances related to genetic basis of cervical cancer

Cervical cancer (CC) is a global threat to women and our knowledge is frighteningly little about its underlying genomic contributors. Our research aimed to understand the underlying molecular and genetic mechanisms of CC by integrating bioinformatics and network-based study. Transcriptomic analyses of three microarray datasets identified 218 common differentially expressed genes (DEGs) within control samples and CC specimens. KEGG pathway analysis revealed pathways in cell cycle, drug metabolism, DNA replication and the significant GO terms were cornification, proteolysis, cell division and DNA replication. Protein-protein interaction (PPI) network analysis identified 20 hub genes and survival analyses validated CDC45, MCM2, PCNA and TOP2A as CC biomarkers. Subsequently, 10 transcriptional factors (TFs) and 10 post-transcriptional regulators were detected through TFs-DEGs and miRNAs-DEGs regulatory network assessment. Finally, the CC biomarkers were subjected to a drug-gene relationship analysis to find the best target inhibitors. Standard cheminformatics method including in silico ADMET and molecular docking study substantiated PD0325901 and Selumetinib as the most potent candidate-drug for CC treatment. Overall, this meticulous study holds promises for further in vitro and in vivo research on CC diagnosis, prognosis and therapies. Communicated by Ramaswamy H. Sarma.

Cheminformatics Study on Structural and Bactericidal Activity of Latest Generation β-Lactams on Widespread Pathogens

Raman spectra of oxacillin (OXN), carbenicillin (CBC), and azlocillin (AZL) are reported for the first time together with their full assignment of the normal modes, as calculated using Density Functional Theory (DFT) methods with the B3LYP exchange-correlation functional coupled to the 6-31G(d) and 6-311+G(2d,p) basis sets. Molecular docking studies were performed on five penicillins, including OXN, CBC, and AZL. Subsequently, their chemical reactivity and correlated efficiency towards specific pathogenic strains were revealed by combining frontier molecular orbital (FMO) data with molecular electrostatic potential (MEP) surfaces. Their bactericidal activity was tested and confirmed on a couple of species, both Gram-positive and Gram-negative, by using the disk diffusion method. Additionally, a surface-enhanced Raman spectroscopy (SERS)-principal component analysis (PCA)-based resistogram of A. hydrophila is proposed as a clinically relevant insight resulting from the synergistic cheminformatics and vibrational study on CBC and AZL.

Cheminformatics-Based Discovery of Potential Chemical Probe Inhibitors of Omicron Spike Protein

During the past two decades, the world has witnessed the emergence of various SARS-CoV-2 variants with distinct mutational profiles influencing the global health, economy, and clinical aspects of the COVID-19 pandemic. These variants or mutants have raised major concerns regarding the protection provided by neutralizing monoclonal antibodies and vaccination, rates of virus transmission, and/or the risk of reinfection. The newly emerged Omicron, a genetically distinct lineage of SARS-CoV-2, continues its spread in the face of rising vaccine-induced immunity while maintaining its replication fitness. Efforts have been made to improve the therapeutic interventions and the FDA has issued Emergency Use Authorization for a few monoclonal antibodies and drug treatments for COVID-19. However, the current situation of rapidly spreading Omicron and its lineages demands the need for effective therapeutic interventions to reduce the COVID-19 pandemic. Several experimental studies have indicated that the FDA-approved monoclonal antibodies are less effective than antiviral drugs against the Omicron variant. Thus, in this study, we aim to identify antiviral compounds against the Spike protein of Omicron, which binds to the human angiotensin-converting enzyme 2 (ACE2) receptor and facilitates virus invasion. Initially, docking-based virtual screening of the in-house database was performed to extract the potential hit compounds against the Spike protein. The obtained hits were optimized by DFT calculations to determine the electronic properties and molecular reactivity of the compounds. Further, MD simulation studies were carried out to evaluate the dynamics of protein–ligand interactions at an atomistic level in a time-dependent manner. Collectively, five compounds (AKS-01, AKS-02, AKS-03, AKS-04, and AKS-05) with diverse scaffolds were identified as potential hits against the Spike protein of Omicron. Our study paves the way for further in vitro and in vivo studies.

Drug Chemical Space as a Guide for New Herbicide Development: A Cheminformatic Analysis

Herbicides are critical resources for meeting agricultural demand. While similar in structure and function to pharmaceuticals, the development of new herbicidal mechanisms of action and new scaffolds against known mechanisms of action has been much slower than in pharmaceutical sciences. We hypothesized that this may be due in part to a relative undersampling of possible herbicidal chemistries and set out to test whether this difference in sampling existed and whether increasing the diversity of possible herbicidal chemistries would be likely to result in more efficacious herbicides. To conduct this work, we first identified databases of commercially available herbicides and clinically approved pharmaceuticals. Using these databases, we created a two-dimensional embedding of the chemical, which provides a qualitative visualization of the degree to which each chemotype is distributed within the combined chemical space and shows a moderate degree of overlap between the two sets. Next, we trained several machine learning models to classify herbicides versus drugs based on physicochemical characteristics. The most accurate of these models has an accuracy of 93% with the key differentiating characteristics being the number of polar hydrogens, number of amide bonds, LogP, and polar surface area. We then used several types of scaffold decomposition to quantitatively evaluate the chemical diversity of each molecular family and showed herbicides to have considerably fewer unique structural fragments. Finally, we used molecular docking as an in silico evaluation of further structural diversification in herbicide development. To this end, we identified herbicides with well-characterized binding sites and modified those scaffolds based on similar structural subunits from the drug dataset not present in any commercial herbicide while using the machine-learned model to ensure that required herbicide properties were maintained. Redocking the original and modified scaffolds of several herbicides showed that even this simple design strategy is capable of yielding new molecules with higher predicted affinity for the target enzymes. Overall, we show that herbicides are distinct from drugs based on physicochemical properties but less diverse in their chemistry in a way not governed by these properties. We also demonstrate in silico that increasing the diversity of herbicide scaffolds has the potential to increase potency, potentially reducing the amount needed in agricultural practice.

Elastase inhibitory activity of quinoline Analogues: Synthesis, kinetic mechanism, cytotoxicity, chemoinformatics and molecular docking studies

Herein, we have synthesized quinoline united various Schiff base derivatives (Q1-Q13) and systematically characterized them using diverse analytical practices such as ¹H NMR, ¹³C NMR, FT-IR and LC-MS respectively. All of the target compounds that have been synthesized were tested for elastase inhibition, and the findings were compared to the standard drug oleanolic acid. Among the entire series, compound Q11 (IC₅₀ = 0.897 ± 0.015 µM) exhibit most promising elastase inhibitory activity than oleanolic acid (Standard) having an IC₅₀ value of 13.426 ± 0.015 µM. Also, the utmost effectivecompound Q11 was used for kinetic mechanism investigation based on in-vitro data, from which it has been concluded that compound Q11 inhibits elastase competitively. Furthermore, utilizing the MTT test approach, the most effective compounds were assessed for cytotoxicity on B16F10 melanoma cells. From the cytotoxicity experiment, the most potent compound did not display any hazardous response against B16F10 melanoma cells despite being treated at high concentrations. Additionally, the molecular docking study was settled to govern the binding interaction pattern among an enzyme and inhibitors.

Advancing Cheminformatics-A Theme Issue in Honor of Professor Jürgen Bajorath

While cheminformatics problems have been actively researched since the early 1960s, as witnessed by the QSAR approaches developed by Toshio Fujita and Corwin Hansch [...].

Optimizing cardio, hepato and phospholipidosis toxicity of the Bedaquiline by chemoinformatics and molecular modelling approach

The FDA granted expedited approval for Johnson and Johnson's Bedaquiline to treat pulmonary multidrug resistant tuberculosis on 28 December 2012 which is more common in China, Russian Federation and India. Bedaquiline is the first anti-tubercular drug approved by the FDA in the last 40 years, and it has become a cynosure in the circles of synthetic chemists researching new anti-tubercular drugs. Bedaquiline's highly lipophilic nature raises major concerns like suppression of the hERG gene, hepatotoxicity, and phospholipidosis despite its potential antitubercular profile. To address these toxicity concerns, in the present work, we have employed the structural optimization of Bedaquiline using the ADMETopt web server, which optimizes lead with scaffold hopping and ADMET screening. The ADMETopt web server yielded the 476 structures through optimization of three sites in Bedaquiline. Further, we have validated the optimized structures for their activity by performing molecular docking and molecular dynamics (MD) simulations against the mycobacterial ATP synthase enzyme and density functional theory (DFT) study further provides insight into the reactivity of the compounds. After screening and analysis, compound #449 was observed to be the most promising mycobacterial ATP synthase inhibitor with minimal cardiotoxicity, hepatotoxicity and phospholipidosis.

Chemoinformatic Exploration of "Bioinspired Metabolomes" Illuminates Diacetyl Assembly Pathways Toward Nesteretal A-Like Cage Molecules

An appealing and challenging cage structure along with an unusual biosynthetic pathway prompted us to explore an expeditious bioinspired one-pot total synthesis of nesteretal A. An unconventional strategy was chosen, and a cascade reaction starting from diacetyl was studied. Under organocatalytic conditions mimicking an aldolase, nesteretal A and a related cage analogue were anticipated by in silico metabolization, detected, targeted, and characterized.

Conceptual DFT-Based Computational Peptidology, Pharmacokinetics Study and ADMET Report of the Veraguamides A–G Family of Marine Natural Drugs

As a continuation of our research on the chemical reactivity, pharmacokinetics and ADMET properties of cyclopeptides of marine origin with potential therapeutic abilities, in this work our already presented integrated molecular modeling protocol has been used for the study of the chemical reactivity and bioactivity properties of the Veraguamides A–G family of marine natural drugs. This protocol results from the estimation of the conceptual density functional theory (CDFT) chemical reactivity descriptors together with several chemoinformatics tools commonly considered within the process of development of new therapeutic drugs. CP-CDFT is a branch of computational chemistry and molecular modeling dedicated to the study of peptides, and it is a protocol that allows the estimation with great accuracy of the CDFT-based reactivity descriptors and the associated physical and chemical properties, which can aid in determining the ability of the studied peptides to behave as potential useful drugs. Moreover, the superiority of the MN12SX density functional over other long-range corrected density functionals for the prediction of chemical and physical properties in the presence of water as the solvent is clearly demonstrated. The research was supplemented with an investigation of the bioactivity of the molecular systems and their ADMET (absorption, distribution, metabolism, excretion, and toxicity) parameters, as is customary in medicinal chemistry. Some instances of the CDFT-based chemical reactivity descriptors’ capacity to predict the pKas of peptides as well as their potential as AGE inhibitors are also shown.

Discovering pesticides and their TPs in Luxembourg waters using open cheminformatics approaches

The diversity of hundreds of thousands of potential organic pollutants and the lack of (publicly available) information about many of them is a huge challenge for environmental sciences, engineering, and regulation. Suspect screening based on high-resolution liquid chromatography-mass spectrometry (LC-HRMS) has enormous potential to help characterize the presence of these chemicals in our environment, enabling the detection of known and newly emerging pollutants, as well as their potential transformation products (TPs). Here, suspect list creation (focusing on pesticides relevant for Luxembourg, incorporating data sources in 4 languages) was coupled to an automated retrieval of related TPs from PubChem based on high confidence suspect hits, to screen for pesticides and their TPs in Luxembourgish river samples. A computational workflow was established to combine LC-HRMS analysis and pre-screening of the suspects (including automated quality control steps), with spectral annotation to determine which pesticides and, in a second step, their related TPs may be present in the samples. The data analysis with Shinyscreen (https://gitlab.lcsb.uni.lu/eci/shinyscreen/), an open source software developed in house, coupled with custom-made scripts, revealed the presence of 162 potential pesticide masses and 96 potential TP masses in the samples. Further identification of these mass matches was performed using the open source approach MetFrag (https://msbi.ipb-halle.de/MetFrag/). Eventual target analysis of 36 suspects resulted in 31 pesticides and TPs confirmed at Level-1 (highest confidence), and five pesticides and TPs not confirmed due to different retention times. Spatio-temporal analysis of the results showed that TPs and pesticides followed similar trends, with a maximum number of potential detections in July. The highest detections were in the rivers Alzette and Mess and the lowest in the Sûre and Eisch. This study (a) added pesticides, classification information and related TPs into the open domain, (b) developed automated open source retrieval methods - both enhancing FAIRness (Findability, Accessibility, Interoperability and Reusability) of the data and methods; and (c) will directly support "L'Administration de la Gestion de l'Eau" on further monitoring steps in Luxembourg.

Chemoinformatics View on Bitter Taste Receptor Agonists in Food

Food compounds with a bitter taste have a role in human health, both for their capability to influence food choice and preferences and for their possible systemic effect due to the modulation of extra-oral bitter taste receptors (TAS2Rs). Investigating the interaction of bitter food compounds with TAS2Rs is a key step to unravel their complex effects on health and to pave the way to rationally design new additives for food formulation or drugs. Here, we propose a collection of food bitter compounds, for which in vitro activity data against TAS2Rs are available. The patterns of TAS2R subtype-specific agonists were analyzed using scaffold decomposition and chemical space analysis, providing a detailed characterization of the associations between food bitter tastants and TAS2Rs.

Chemoinformatics-Driven Design of New Physical Solvents for Selective CO2 Absorption

The removal of CO₂ from gases is an important industrial process in the transition to a low-carbon economy. The use of selective physical (co-)solvents is especially perspective in cases when the amount of CO₂ is large as it enables one to lower the energy requirements for solvent regeneration. However, only a few physical solvents have found industrial application and the design of new ones can pave the way to more efficient gas treatment techniques. Experimental screening of gas solubility is a labor-intensive process, and solubility modeling is a viable strategy to reduce the number of solvents subject to experimental measurements. In this paper, a chemoinformatics-based modeling workflow was applied to build a predictive model for the solubility of CO₂ and four other industrially important gases (CO, CH₄, H₂, and N₂). A dataset containing solubilities of gases in 280 solvents was collected from literature sources and supplemented with the new data for six solvents measured in the present study. A modeling workflow based on the usage of several state-of-the-art machine learning algorithms was applied to establish quantitative structure-solubility relationships. The best models were used to perform virtual screening of the industrially produced chemicals. It enabled the identification of compounds with high predicted CO₂ solubility and selectivity toward other gases. The prediction for one of the compounds, 4-methylmorpholine, was confirmed experimentally.

Computational Pharmacokinetics Report, ADMET Study and Conceptual DFT-Based Estimation of the Chemical Reactivity Properties of Marine Cyclopeptides

Homophymines A-E and A1-E1 are bioactive natural cyclodepsipeptides with a complex molecular architecture. These molecules could have a potential use as antimicrobial, antiviral, and anticancer substances. We have carried out a computational study of the properties of this family of marine peptides using a CDFT-based Computational Peptidology (CDFT-CP) methodology that results from the combination of the chemical reactivity descriptors that arise from conceptual Density Functional Theory (CDFT) together with cheminformatics tools. The latter can be used to estimate the associated physicochemical parameters and to improve the process of virtual screening through a similarity search. Using this approach, the ability of the peptides to behave as a potentially useful drugs can be investigated. An analysis of their bioactivity and pharmacokinetics indices related to the ADMET (Absorption, Distribution, Metabolism, Excretion and Toxicity) features has also been carried out.

Exploring the regulatory mechanism of Nao Tai Fang on vascular Dementia's biological network based on cheminformatics and transcriptomics strategy

ETHNOPHARMACOLOGICAL RELEVANCE

Nao Tai Fang (NTF) is modified from Buyang Huanwu Decoction. Modern pharmacological research showed that NTF has a good anti-cerebral ischemic effect and can improve the learning and memory ability of cerebrovascular disease.

AIM

The purpose of this study is to explore the regulation mechanism of NTF on the regulation mechanism of vascular dementia (VD)'s biological network based on chemoinformatics and transcriptomics strategies.

METHOD

First, the bilateral common carotid artery ligation method was used to create a rat VD model. After NTF intervention for 30 days, the treatment effect was evaluated by HE staining and water maze experiment. Then, the Agilent mRNA expression profiling chip was used to obtain mRNA expression data of hippocampal tissues of VD model rats before and after NTF intervention, and microarray analysis was used to screen for genes with significant differential expression. The BATMAN database was utilized to obtain the potential targets of NTF and the Genecards and OMIM were utilized to collect the VD potential genes. The cytoscape was utilized to construct and analyze the networks.

RESULT

The animal experiments showed that NTF can improve VD. A total of 180 up-regulated proteins and 289 down-regulated proteins were identified. The top 20 up- and down-regulated differentially expressed genes were utilized to construct differentially expressed gene's protein-protein interaction (PPI) network. A total of 677 NTF potential targets and 550 VD genes were obtained and were utilized to construct NTF-VD PPI network. The cheminformatics analysis showed that NTF can regulate a lot of biological processes and signaling pathways (such as inflammation modules, vasodilation and contraction modules, hypoxia modules, angiogenesis, coagulation, neurovascular unit modules, Neuroactive ligand-receptor interaction, Calcium signaling pathway, Serotonergic synapse).

CONCLUSION

NTF may play a role in the treatment of VD through the targets, signaling pathways and biological processes discovered in this study.

Tubulin Inhibitors: A Chemoinformatic Analysis Using Cell-Based Data

Inhibiting the tubulin-microtubules (Tub-Mts) system is a classic and rational approach for treating different types of cancers. A large amount of data on inhibitors in the clinic supports Tub-Mts as a validated target. However, most of the inhibitors reported thus far have been developed around common chemical scaffolds covering a narrow region of the chemical space with limited innovation. This manuscript aims to discuss the first activity landscape and scaffold content analysis of an assembled and curated cell-based database of 851 Tub-Mts inhibitors with reported activity against five cancer cell lines and the Tub-Mts system. The structure-bioactivity relationships of the Tub-Mts system inhibitors were further explored using constellations plots. This recently developed methodology enables the rapid but quantitative assessment of analog series enriched with active compounds. The constellations plots identified promising analog series with high average biological activity that could be the starting points of new and more potent Tub-Mts inhibitors.

FragNet, a Contrastive Learning-Based Transformer Model for Clustering, Interpreting, Visualizing, and Navigating Chemical Space

The question of molecular similarity is core in cheminformatics and is usually assessed via a pairwise comparison based on vectors of properties or molecular fingerprints. We recently exploited variational autoencoders to embed 6M molecules in a chemical space, such that their (Euclidean) distance within the latent space so formed could be assessed within the framework of the entire molecular set. However, the standard objective function used did not seek to manipulate the latent space so as to cluster the molecules based on any perceived similarity. Using a set of some 160,000 molecules of biological relevance, we here bring together three modern elements of deep learning to create a novel and disentangled latent space, viz transformers, contrastive learning, and an embedded autoencoder. The effective dimensionality of the latent space was varied such that clear separation of individual types of molecules could be observed within individual dimensions of the latent space. The capacity of the network was such that many dimensions were not populated at all. As before, we assessed the utility of the representation by comparing clozapine with its near neighbors, and we also did the same for various antibiotics related to flucloxacillin. Transformers, especially when as here coupled with contrastive learning, effectively provide one-shot learning and lead to a successful and disentangled representation of molecular latent spaces that at once uses the entire training set in their construction while allowing "similar" molecules to cluster together in an effective and interpretable way.

Deciphering superior quality of Pu-erh tea from thousands of years' old trees based on the chemical profile

Pu-erh teas from thousands of years' old trees (TPT) equip with both superior flavors and powerful antioxidative capacities. With UHPLC-Q-TOF-MS approach, TPTs' chemical profiles were characterized by comparing with Pu-erh teas from ecological trees (EPT). TPTs are discovered to possess higher contents of amino acids, fatty acids, phenolic acids, nucleosides and nucleobases but lower contents of flavonoids and caffeine congeners based on 117 discriminative constituents from 305 identified ones. Particularly, a series of caffeic acid congeners including ten new hydroxycinnamic acid depsides with higher contents in TPTs are discovered, and caffeic acid with a fold change of 638 is the foremost discriminative component. Furthermore, distinguishing constituent proportion including caffeic acid congeners in TPTs are found to take great responsibilities for their more powerful antioxidative abilities and superior flavors especially more aroma and pleasant bitterness. This research provides information for deciphering formation of TPTs' superior qualities based on chemical profile.

Natural product fragment combination to performance-diverse pseudo-natural products

Natural product structure and fragment-based compound development inspire pseudo-natural product design through different combinations of a given natural product fragment set to compound classes expected to be chemically and biologically diverse. We describe the synthetic combination of the fragment-sized natural products quinine, quinidine, sinomenine, and griseofulvin with chromanone or indole-containing fragments to provide a 244-member pseudo-natural product collection. Cheminformatic analyses reveal that the resulting eight pseudo-natural product classes are chemically diverse and share both drug- and natural product-like properties. Unbiased biological evaluation by cell painting demonstrates that bioactivity of pseudo-natural products, guiding natural products, and fragments differ and that combination of different fragments dominates establishment of unique bioactivity. Identification of phenotypic fragment dominance enables design of compound classes with correctly predicted bioactivity. The results demonstrate that fusion of natural product fragments in different combinations and arrangements can provide chemically and biologically diverse pseudo-natural product classes for wider exploration of biologically relevant chemical space.

Nuisance compounds in cellular assays

Compounds that exhibit assay interference or undesirable mechanisms of bioactivity ("nuisance compounds") are routinely encountered in cellular assays, including phenotypic and high-content screening assays. Much is known regarding compound-dependent assay interferences in cell-free assays. However, despite the essential role of cellular assays in chemical biology and drug discovery, there is considerably less known about nuisance compounds in more complex cell-based assays. In our view, a major obstacle to realizing the full potential of chemical biology will not just be difficult-to-drug targets or even the sheer number of targets, but rather nuisance compounds, due to their ability to waste significant resources and erode scientific trust. In this review, we summarize our collective academic, government, and industry experiences regarding cellular nuisance compounds. We describe assay design strategies to mitigate the impact of nuisance compounds and suggest best practices to efficiently address these compounds in complex biological settings.