Cheminformatics Methods Research Articles

Exploring the molecular pathways of the activation process in PPARγ recurrent bladder cancer mutants.

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

Revealing Chemical Trends: Insights from Data-Driven Visualization and Patent Analysis in Exposomics Research.

Understanding historical chemical usage is crucial for assessing current and past impacts on human health and the environment and for informing future regulatory decisions. However, past monitoring data are often limited in scope and number of chemicals, while suitable sample types are not always available for remeasurement. Data-driven cheminformatics methods for patent and literature data offer several opportunities to fill this gap. The chemical stripes were developed as an interactive, open source tool for visualizing patent and literature trends over time, inspired by the global warming and biodiversity stripes. This paper details the underlying code and data sets behind the visualization, with a major focus on the patent data sourced from PubChem, including patent origins, uses, and countries. Overall trends and specific examples are investigated in greater detail to explore both the promise and caveats that such data offer in assessing the trends and patterns of chemical patents over time and across different geographic regions. Despite a number of potential artifacts associated with patent data extraction, the integration of cheminformatics, statistical analysis, and data visualization tools can help generate valuable insights that can both illuminate the chemical past and potentially serve toward an early warning system for the future.

Natural products in neuroprotective therapies: Experimental and cheminformatics approaches to manage neurological disorders

Neurological disorders (NDs) such as Alzheimer’s disease (AD), Parkinson’s disease (PD), epilepsy, despondency, and dementia have been evidenced as a rising concern among diverse geographical regions. Brain-related diseases are currently the main concern because they increase mortality and morbidity in the elderly. Regardless of the continual efforts by modern scientists to develop a promising pharmacological or surgical management, the outcome has not been satisfactory. Also, due to synthetic drugs’ associated side effects, scientists have taken the initiative to consider using natural compounds as an alternative. Hence, they obtain pretty effective results by using natural compounds. Natural ingredients are synthesized from a variety of plant and animal sources. These natural ingredients cure brain diseases through a variety of mechanisms. For effective medication advancement, the molecules must go through preliminary clinical systems that require some investment and significant speculation. In this situation, cheminformatics is fundamental in diminishing time and venture. Cheminformatics methods play a significant role in these issues, including 3-dimensional quantitative structure-activity relationship 3D-(QSAR), virtual screening, docking, molecular dynamic studies, and quantum chemical studies. The vital purpose of this study is to disclose different types of NDs and the neuroprotective effect of several natural products for experimental and cheminformatics-based therapy. Natural products like green tea, flavonoids, and ginseng are discussed as effective neuroprotective products. However, more investigation is expected to comprehend the better utilization of regular items in future exploratory and cheminformatics-based treatment for NDs.

Targeting NMDA receptor in Alzheimer's disease: identifying novel inhibitors using computational approaches.

The glutamate-gated ion channels known as N-methyl-d-aspartate receptors (NMDARs) are important for both normal and pathological brain function. Subunit-selective antagonists have high therapeutic promise since many pathological conditions involve NMDAR over activation, although few clinical successes have been reported. Allosteric inhibitors of GluN2B-containing receptors are among the most potential NMDAR targeting drugs. Since the discovery of ifenprodil, a variety of GluN2B-selective compounds have been discovered, each with remarkably unique structural motifs. These results expand the allosteric and pharmacolog-ical spectrum of NMDARs and provide a new structural basis for the development of next-generation GluN2B antagonists that have therapeutic potential in brain diseases. Small molecule therapeutic inhibitors targeting NMDA have recently been developed to target CNS disorders such as Alzheimer's disease. In the current study, a cheminformatics method was used to discover potential antagonists and to identify the structural requirements for Gly/NMDA antagonism. In this case we have created a useful pharmacophore model with solid statistical values. Through pharmacophore mapping, the verified model was used to filter out virtual matches from the ZINC database. Assessing receptor-ligand binding mechanisms and affinities used molecular docking. To find the best hits, the GlideScore and the interaction of molecules with important amino acids were considered essential features. We found some molecular inhibitors, namely, ZINC13729211, ZINC07430424, ZINC08614951, ZINC60927204, ZINC12447511, and ZINC18889258 with high binding affinity using computational methods. The molecules in our studies showed characteristics such as good stability, hydrogen bonding and higher binding affinities in the solvation-based assessment method than ifenprodil with acceptable ADMET profile. Moreover, these six leads have been proposed as potential new perspectives for exploring potent Gly/NMDA receptor antagonists. In addition, it can be tested in the laboratory for potential therapeutic strategies for both in vitro and in vivo research.

Comprehensive characterization of natural products of Polygonum multiflorum by cheminformatics analysis

IntroductionA growing body of evidence has suggested that Polygonum multiflorum Thunb. (PM) may cause PM-herb-induced liver injury (PM-HILI). However, the compounds triggering PM-HILI remain controversial. Herein, we set out to investigate the relationships among natural products of PM (NPPM), drug-induced liver injury (DILI) positive (POS), and negative (NEG) compounds by using cheminformatics methods. MethodsA total of 197 NPPM and 2384 annotated DILI dataset were collected from the literature. Chemical space, physicochemical properties, drug-likeness, intra-set similarity, and scaffold diversity were compared to gain insights into the multiple features of NPPM. An ensemble machine learning (ML) model was constructed to predict the DILI potential of NPPM. Twelve NPPM were selected and tested on HepaRG cells to validate the prediction results. ResultsResults of the principal component analysis suggest that NPPM bears more similarity to NEG in terms of chemical space when compared with POS. Besides, NPPM share a moderate overall scaffold diversity and one-third ingredients in NPPM compiled the drug-like rules. The predictive results of ML model show that 28.9% of the small molecules in NPPM bear DILI potential. Further in vitro study by detecting cytotoxicity of representative compounds on HepaRG cells showed that trans- and cis-emodin-physcion dianthrone exhibited the lowest IC50 values of 53.05 µM and 17.11 µM, respectively. ConclusionML methods and further validation recognize the liver toxicity of dianthonres and emodins. A proportion of NPPM components exhibit drug-likeness profiles and might offer complementary resources for drug discovery. The results demonstrated machine learning-powered DILI prediction as a useful tool to study potential DILI risk of compounds, providing a basis for further identification of toxins or leads in PM. The codes used and generated in this study are freely available at https://github.com/dreadlesss/Polygonum_database_analysis.

Triazole‐tethered boswellic acid derivatives against breast cancer: Synthesis, in vitro, and in‐silico studies

A series of new analogues of 3-O-acetyl-11-keto-β-boswellic acid (β-AKBA, 1), 3-O-acetyl-β-boswellic acid (β-ABA, 2) (3–8) and 1H-1,2,3-triazole hybrids of β-AKBA (10a-j) were synthesized by employing highly efficient “Click” chemistry reaction protocol. All synthesized compounds were characterized by 1H-, 13C NMR, and HRMS spectroscopy. The structures of compounds 5 and 6 were unambiguously confirmed by the single crystal X-ray analysis diffraction method. The cytotoxic potential of the synthesized compounds was scrutinized against the triple-negative breast cancer (MDA-MB-231) and normal (MCF-10A) cell lines. Furthermore, all the synthesized derivatives exhibited highly potent anti-proliferative activity with IC50 values ranging from 4.45 to 14.45 µM. Among them, compounds 10j, 10f, and 10i exhibited exceptional inhibitory potency and were found several times more potent than the parent compounds 1 and 2. Additionally, the cheminformatics method was used to identify the potential drug target of the most potent compounds (10j, 10f, and 10i) and their binding mechanism with the selected drug target was predicted through molecular docking. The combined 2D-similarity searching, and structure-based binding investigation predict that these compounds can target CHK1 to produce anti-cancer effects in TNBC. This study lays a good foundation for new triterpenic triazole analogues of boswellic acids and could be effective anticancer leads in breast cancer therapeutics.

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

Modelling solution structures of cyclic peptides - How good are we?

Cyclic peptides (CPs) are macrocycles made from amino acids that can bind to classically undruggable drug targets. Controlling pharmacokinetic properties of these macrocyclic peptides is an open question, limited by having precise knowledge of the underlying solution structures. CP solution dynamics are complex: individual structures are often separated by large energy barriers; the presence of conformers is environment dependent and solution conformers can be different from bioactive structures. Specialised simulation methods for CPs have previously been designed to reproduce available x-ray structures, but it is often unknown how well solution structures are reproduced. Here, we report the largest cyclic peptide solution NMR dataset “MacroConf”, which we built from existing literature. We use a range of molecular dynamics methods and specialised cheminformatics methods to model the solution structures of over 30 cyclic peptides from this dataset. We explore (a) how different methods perform relative to one another and to the experimental reference structures and (b) whether computationally cheaper methods can reach the accuracy of gold-standard accelerated molecular dynamics simulations to allow for fast prediction of solution ensembles. By providing automated computational workflows, this work is easily extendable, fully reproducible, and is built as a reference point to guide development of future conformer generators for cyclic peptides.

Uncovering novel liquid organic hydrogen carriers: a systematic exploration of chemical compound space using cheminformatics and quantum chemical methods

We present a comprehensive, in silico-based discovery approach to identifying novel liquid organic hydrogen carrier (LOHC) candidates using cheminformatics methods and quantum chemical calculations.

Predictive and explanatory themes of NOAEL through a systematic comparison of different machine learning methods and descriptors.

No observed adverse effect level (NOAEL) is an identified dose level which used as a point of departure to infer a safe exposure limit of chemicals, especially in food additives and cosmetics. Recently, in silico approaches have been employed as effective alternatives to determine the toxicity endpoints of chemicals instead of animal experiments. Several acceptable models have been reported, yet assessing the risk of repeated-dose toxicity remains inadequate. This study established robust machine learning predictive models for NOAEL at different exposure durations by constructing high-quality datasets and comparing different kinds of molecular representations and algorithms. The features of molecular structures affecting NOAEL were explored using advanced cheminformatics methods, and predictive models also communicated the NOAEL between different species and exposure durations. In addition, a NOAEL prediction tool for chemical risk assessment is provided (available at: https://github.com/ifyoungnet/NOAEL). We hope this study will help researchers easily screen and evaluate the subacute and sub-chronic toxicity of disparate compounds in the development of food additives in the future.

In silico Activity and Target Prediction Analyses of Three Triazolothiadiazine Derivatives

Objective: Polypharmacology, interaction of one drug with multiple targets, emerged as an effective approach in drug discovery and development. Bioinformatics and cheminformatics methods are essential tools for determination of polypharmacological profiles of newly synthesized or known compounds and drugs. Previously, three novel triazolothiadiazine derivatives; 1h, 3c and 3h, have been shown to induce apoptosis and cause cell cycle arrest on liver cancer cells. The aim of this study is to find possible action mechanisms and potential targets for these three triazolothiadiazine derivatives, and to investigate their potential as new therapeutic agents by using computational methods. Materials and Methods: PASS software was used to identify biological activities and Swiss Target Prediction and BindingDB databases to predict potential targets for 1h, 3c and 3h. PDE4A, ALR and DUSP1 proteins were selected for molecular docking analysis following the protein modeling of the three proteins. Results: Activity prediction results show that 1h, 3c and 3h might have phosphatase and signal transduction pathway inhibitor, hepatocyte growth factor antagonist, anti-inflammatory and antifungal activities. These derivatives are predicted as inhibitors of several phosphodiesterases by activity and target prediction tools. Conclusion: Based on prediction and molecular docking results, it is proposed that these compounds may have therapeutic properties through new predicted targets.

Computer-Aided Methods for Molecular Classification

The study aims to analyze the degree of similarity of some molecules belonging to two subgroups of Aminoalkylindoles. After extracting the molecules’ characteristics using Cheminformatics methods, and the computation of the Tanimoto coefficients, dendrograms and heatmaps were built to reveal the degree of similarity of the analyzed drugs. Some atom-pair similarities between the molecules in the same group were detected. The clusters determined by the k-means method divided the Benzoylindoles into two subgroups but kept all the Phenylacetylindoles together in the same set. The activity spectrum of the elements in each group was also analyzed, and similarities have been emphasized. The clustering has been validated using the Kruskal–Wallis test on the series of computed probabilities of the main effects.

Study of the additives for polymeric materials by cheminformatics methods

Abstract This article analyzes the structures of some antioxidants, UV stabilizers, and inhibitors utilized to improve the polymeric materials’ functionalities using Cheminformatics tools implemented in the R software. Based on their properties, the dendrograms are drawn, and the molecules are classified in clusters, utilizing hierarchical clustering and the k-means algorithm.

How fullerene derivatives (FDs) act on therapeutically important targets associated with diabetic diseases

Fullerene derivatives (FDs) belong to a relatively new family of nano-sized organic compounds. They are widely applied in materials science, pharmaceutical industry, and (bio) medicine. This research focused on the study of FDs in terms of their potential inhibitory effect on therapeutic targets associated with diabetic disease, as well as analysis of protein–ligand binding in order to identify the key binding characteristics of FDs.Therapeutic drug compounds when entering the biological system usually inevitably encounter and interact with a vast variety of biomolecules that are responsible for many different functions in organisms. Protein biomolecules are the most important functional components and used in this study as target structures. The structures of proteins [(PDB ID: 1BMQ, 1FM6, 1GPB, 1H5U, 1US0)] belonging to the class of anti-diabetes targets were obtained from the Protein Data Bank (PDB). Protein binding activity data (binding scores) were calculated for the dataset of 169 FDs related to these five proteins. Subsequently, the resulting data were analyzed using various machine learning and cheminformatics methods, including artificial neural network algorithms for variable selection and property prediction.The Quantitative Structure-Activity Relationship (QSAR) models for prediction of binding scores activity were built up according to five Organization for Economic Co-operation and Development (OECD) principles.All the data obtained can provide important information for further potential use of FDs with different functional groups as promising medical antidiabetic agents. Binding scores activity can be used for ranking of FDs in terms of their inhibitory activity (pharmacological properties) and potential toxicity.

Computer-Aided Classification of New Psychoactive Substances

The appearance on the free market of synthetic cannabinoids raised the researchers’ interest in establishing their molecular similarity by QSAR analysis. A rigorous criterion for classifying drugs is their chemical structure. Therefore, this article presents the structural similarity of two groups of drugs: benzoylindoles and phenylacetylindoles. Statistical analysis and clustering of the molecules are performed based on their numerical characteristics extracted using Cheminformatics methods. Their similarities/dissimilarities are emphasized using the dendrograms and heat map. The highest discrepancies are found in the phenylacetylindoles group.

GenUI: interactive and extensible open source software platform for de novo molecular generation and cheminformatics

Many contemporary cheminformatics methods, including computer-aided de novo drug design, hold promise to significantly accelerate and reduce the cost of drug discovery. Thanks to this attractive outlook, the field has thrived and in the past few years has seen an especially significant growth, mainly due to the emergence of novel methods based on deep neural networks. This growth is also apparent in the development of novel de novo drug design methods with many new generative algorithms now available. However, widespread adoption of new generative techniques in the fields like medicinal chemistry or chemical biology is still lagging behind the most recent developments. Upon taking a closer look, this fact is not surprising since in order to successfully integrate the most recent de novo drug design methods in existing processes and pipelines, a close collaboration between diverse groups of experimental and theoretical scientists needs to be established. Therefore, to accelerate the adoption of both modern and traditional de novo molecular generators, we developed Generator User Interface (GenUI), a software platform that makes it possible to integrate molecular generators within a feature-rich graphical user interface that is easy to use by experts of diverse backgrounds. GenUI is implemented as a web service and its interfaces offer access to cheminformatics tools for data preprocessing, model building, molecule generation, and interactive chemical space visualization. Moreover, the platform is easy to extend with customizable frontend React.js components and backend Python extensions. GenUI is open source and a recently developed de novo molecular generator, DrugEx, was integrated as a proof of principle. In this work, we present the architecture and implementation details of GenUI and discuss how it can facilitate collaboration in the disparate communities interested in de novo molecular generation and computer-aided drug discovery.

A Computational Protocol Combining DFT and Cheminformatics for Prediction of pH-Dependent Redox Potentials.

Discovering new materials for energy storage requires reliable and efficient protocols for predicting key properties of unknown compounds. In the context of the search for new organic electrolytes for redox flow batteries, we present and validate a robust procedure to calculate the redox potentials of organic molecules at any pH value, using widely available quantum chemistry and cheminformatics methods. Using a consistent experimental data set for validation, we explore and compare a few different methods for calculating reaction free energies, the treatment of solvation, and the effect of pH on redox potentials. We find that the B3LYP hybrid functional with the COSMO solvation method, in conjunction with thermal contributions evaluated from BLYP gas-phase harmonic frequencies, yields a good prediction of pH = 0 redox potentials at a moderate computational cost. To predict how the potentials are affected by pH, we propose an improved version of the Alberty-Legendre transform that allows the construction of a more realistic Pourbaix diagram by taking into account how the protonation state changes with pH.

Applications of Virtual Screening in Bioprospecting: Facts, Shifts, and Perspectives to Explore the Chemo-Structural Diversity of Natural Products.

Natural products are continually explored in the development of new bioactive compounds with industrial applications, attracting the attention of scientific research efforts due to their pharmacophore-like structures, pharmacokinetic properties, and unique chemical space. The systematic search for natural sources to obtain valuable molecules to develop products with commercial value and industrial purposes remains the most challenging task in bioprospecting. Virtual screening strategies have innovated the discovery of novel bioactive molecules assessing in silico large compound libraries, favoring the analysis of their chemical space, pharmacodynamics, and their pharmacokinetic properties, thus leading to the reduction of financial efforts, infrastructure, and time involved in the process of discovering new chemical entities. Herein, we discuss the computational approaches and methods developed to explore the chemo-structural diversity of natural products, focusing on the main paradigms involved in the discovery and screening of bioactive compounds from natural sources, placing particular emphasis on artificial intelligence, cheminformatics methods, and big data analyses.

In-silico screening of bioactive phytopeptides for novel anti-ageing therapeutics

A metabolic network of energy-sensing molecular pathways drives the biological ageing process. Regulating certain network elements can help decelerate the ageing process and ameliorate ageing associated disorders. Bioactive phytopeptides are a prospective avenue for anti-ageing therapeutics and rejuvenation biotechnology. The present study investigates the potential of therapeutic plant peptides against cellular senescence by targeting three key proteins in the ageing network – target of rapamycin (mTOR), adenosine monophosphate-activated protein kinase (AMPK) and sirtuin 1 (SIRT1). This investigation screened a library of reported bioactive peptides using standard cheminformatic methods including in-silico ADMET, molecular docking, molecular dynamics simulation and molecular mechanics calculation. The retrieved simulation data predict 25 diverse phytopeptides as potential safe and drug-like anti-ageing biologics with half-lives >20 h and bioavailability scores >0.40. The best docked peptide, Cycloleonuripeptide B, exhibited strong binding affinity and stable complex formation with mTOR (-17.5 kCal/mol), SIRT1 (-28.54 kCal/mol) and two active sites in AMPK (-41.8 kCal/mol; −36.0 kCal/mol) during molecular dynamics simulations. The computational study acts as a foundation for future laboratory and clinical research into the potential of repurposing therapeutic phytopeptides against cellular senescence and associated pathophysiology. Communicated by Ramaswamy H. Sarma

Cheminformatics in Natural Product-based Drug Discovery.

This review seeks to provide a timely survey of the scope and limitations of cheminformatics methods in natural product‐based drug discovery. Following an overview of data resources of chemical, biological and structural information on natural products, we discuss, among other aspects, in silico methods for (i) data curation and natural products dereplication, (ii) analysis, visualization, navigation and comparison of the chemical space, (iii) quantification of natural product‐likeness, (iv) prediction of the bioactivities (virtual screening, target prediction), ADME and safety profiles (toxicity) of natural products, (v) natural products‐inspired de novo design and (vi) prediction of natural products prone to cause interference with biological assays. Among the many methods discussed are rule‐based, similarity‐based, shape‐based, pharmacophore‐based and network‐based approaches, docking and machine learning methods.