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New psychoactive substances (NPS) have increasingly been illegally synthesized and used around the world in recent years. Due to the large volume and the variety of NPS, most do not have sufficient information about their addictive potential and harmful effects to human subjects. This makes it difficult to evaluate these potential substances of abuse. This study aims to build a database based on Taiwan's controlled substances, to provide quick structural and pharmacological feedback. Taiwan Controlled Substances Database (TCSD) includes the collection of controlled substances, relevant experimental and structural information, as well as computational features such as molecular fingerprints and descriptors. Two types of structural search were added: substructure search and topological fingerprint similarity search. A web framework was used to enhance accessibility and usability (https://cs2search.cmdm.tw).

Abstract The concept of molecular scaffolds as defining core structures of organic molecules is utilised in many areas of chemistry and cheminformatics, e.g. drug design, chemical classification, or the analysis of high-throughput screening data. Here, we present Scaffold Generator, a comprehensive open library for the generation, handling, and display of molecular scaffolds, scaffold trees and networks. The new library is based on the Chemistry Development Kit (CDK) and highly customisable through multiple settings, e.g. five different structural framework definitions are available. For display of scaffold hierarchies, the open GraphStream Java library is utilised. Performance snapshots with natural products (NP) from the COCONUT (COlleCtion of Open Natural prodUcTs) database and drug molecules from DrugBank are reported. The generation of a scaffold network from more than 450,000 NP can be achieved within a single day.

Background: Marine prokaryotes are a rich source of novel bioactive secondary metabolites for drug discovery. Recent genome mining studies have revealed their great potential to bio-synthesize novel secondary metabolites. However, the exact biosynthetic chemical space encoded by the marine prokaryotes has yet to be systematically evaluated. Results: We first investigated the secondary metabolic potential of marine prokaryotes by analyzing the diversity and novelty of the biosynthetic gene clusters (BGCs) in 7541 prokaryotic genomes from cultivated and single cells, along with 26,363 newly assembled medium-to-high-quality genomes from marine environmental samples. To quantitatively evaluate the unexplored biosynthetic chemical space of marine prokaryotes, the clustering thresholds for constructing the biosynthetic gene cluster and molecular networks were optimized to reach a similar level of the chemical similarity between the gene cluster family (GCF)-encoded metabolites and molecular family (MF) scaffolds using the MIBiG database. The global genome mining analysis demonstrated that the predicted 70,011 BGCs were organized into 24,536 mostly new (99.5%) GCFs, while the reported marine prokaryotic natural products were only classified into 778 MFs at the optimized clustering thresholds. The number of MF scaffolds is only 3.2% of the number of GCF-encoded scaffolds, suggesting that at least 96.8% of the secondary metabolic potential in marine prokaryotes is untapped. The unexplored biosynthetic chemical space of marine prokaryotes was illustrated by the 88 potential novel antimicrobial peptides encoded by ribosomally synthesized and post-translationally modified peptide BGCs. Furthermore, a sea-water-derived Aquimarina strain was selected to illustrate the diverse biosynthetic chemical space through untargeted metabolomics and genomics approaches, which identified the potential biosynthetic pathways of a group of novel polyketides and two known compounds (didemnilactone B and macrolactin A 15-ketone). Conclusions: The present bioinformatics and cheminformatics analyses highlight the promising potential to explore the biosynthetic chemical diversity of marine prokaryotes and provide valuable knowledge for the targeted discovery and biosynthesis of novel marine prokaryotic natural products. 8qkWfGqcrPbn_o7-XLBWPj Video Abstract [ABSTRACT FROM AUTHOR]

The chemistry community is currently witnessing a surge of scientific discoveries in organic chemistry supported by machine learning (ML) techniques. Whereas many of these techniques were developed for big data applications, the nature of experimental organic chemistry often confines practitioners to small datasets. Herein, we touch upon the limitations associated with small data in ML and emphasize the impact of bias and variance on constructing reliable predictive models. We aim to raise awareness to these possible pitfalls, and thus, provide an introductory guideline for good practice. Ultimately, we stress the great value associated with statistical analysis of small data, which can be further boosted by adopting a holistic data‐centric approach in chemistry. [ABSTRACT FROM AUTHOR]

Definition: Efficient chemical library design for high-throughput virtual screening and drug design requires a pre-screening filter pipeline capable of labeling aggregators, pan-assay interference compounds (PAINS), and rapid elimination of swill (REOS); identifying or excluding covalent binders; flagging moieties with specific bio-evaluation data; and incorporating physicochemical and pharmacokinetic properties early in the design without compromising the diversity of chemical moieties present in the library. This adaptation of the chemical space results in greater enrichment of hit lists, identified compounds with greater potential for further optimization, and efficient use of computational time. A number of medicinal chemistry filters have been implemented in the Konstanz Information Miner (KNIME) software and analyzed their impact on testing representative libraries with chemoinformatic analysis. It was found that the analyzed filters can effectively tailor chemical libraries to a lead-like chemical space, identify protein–protein inhibitor-like compounds, prioritize oral bioavailability, identify drug-like compounds, and effectively label unwanted scaffolds or functional groups. However, one should be cautious in their application and carefully study the chemical space suitable for the target and general medicinal chemistry campaign, and review passed and labeled compounds before taking further in silico steps. [ABSTRACT FROM AUTHOR]

Automated prediction of reaction impurities is useful in early‐stage reaction development, synthesis planning and optimization. Existing reaction predictors are catered towards main product prediction, and are often black‐box, making it difficult to troubleshoot erroneous outcomes. This work aims to present an automated, interpretable impurity prediction workflow based on data mining large chemical reaction databases. A 14‐step workflow was implemented in Python and RDKit using Reaxys® data. Evaluation of potential chemical reactions between functional groups present in the same reaction environment in the user‐supplied query species can be accurately performed by directly mining the Reaxys® database for similar or 'analogue' reactions involving these functional groups. Reaction templates can then be extracted from analogue reactions and applied to the relevant species in the original query to return impurities and transformations of interest. Three proof‐of‐concept case studies (paracetamol, agomelatine and lersivirine) were conducted, with the workflow correctly suggesting impurities within the top two outcomes. At all stages, suggested impurities can be traced back to the originating template and analogue reaction in the literature, allowing for closer inspection and user validation. Ultimately, this work could be useful as a benchmark for more sophisticated algorithms or models since it is interpretable, as opposed to purely black‐box solutions. [ABSTRACT FROM AUTHOR]

Exploratory analysis of the chemical space is an important task in the field of cheminformatics. For example, in drug discovery research, chemists investigate sets of thousands of chemical compounds in order to identify novel yet structurally similar synthetic compounds to replace natural products. Manually exploring the chemical space inhabited by all possible molecules and chemical compounds is impractical, and therefore presents a challenge. To fill this gap, we present ChemoGraph, a novel visual analytics technique for interactively exploring related chemicals. In ChemoGraph, we formalize a chemical space as a hypergraph and apply novel machine learning models to compute related chemical compounds. It uses a database to find related compounds from a known space and a machine learning model to generate new ones, which helps enlarge the known space. Moreover, ChemoGraph highlights interactive features that support users in viewing, comparing, and organizing computationally identified related chemicals. With a drug discovery usage scenario and initial expert feedback from a case study, we demonstrate the usefulness of ChemoGraph. [ABSTRACT FROM AUTHOR]

Chemical Industry Press is based on serving undergraduate teaching in engineering universities and colleges, with a complete range of science, engineering, agriculture, forestry and medicine, covering both the basic professional courses and professional orientation courses. There are about 300 types of products available for sale, including 20 kinds of textbooks for the 12th Five-Year Plan, 24 kinds of textbooks for the 11th Five-Year Plan, and 80 kinds of textbooks for the four major chemical theory courses. At present, Chemical Industry Press have utilized cloud services, public accounts, teaching platforms, etc. to develop online resources and new forms of teaching materials. In the future, we will keep up with the trends of education reform, actively use information technology, and focus on planning and publishing a batch of high-quality textbooks with outstanding features. At the same time, we will increase efforts to introduce excellent chemistry textbooks from abroad. [ABSTRACT FROM AUTHOR]

Molecular descriptors are widely employed to present molecular characteristics in cheminformatics. In QSAR/QSPR study, molecular descriptors are utilized to predict the bioactivity of chemical compounds. The polyphenylene network, known as porous graphene, is one of the most important and widely studied two-dimensional materials. In this paper we compute exact analytical expressions for certain distance based topological indices for polyphenylene superhoneycomb networks. [ABSTRACT FROM AUTHOR]

The GlyCosmos Glycoscience Portal (https://glycosmos.org) and PubChem (https://pubchem.ncbi.nlm.nih.gov/) are major portals for glycoscience and chemistry, respectively. GlyCosmos is a portal for glycan-related repositories, including GlyTouCan, GlycoPOST, and UniCarb-DR, as well as for glycan-related data resources that have been integrated from a variety of 'omics databases. Glycogenes, glycoproteins, lectins, pathways, and disease information related to glycans are accessible from GlyCosmos. PubChem, on the other hand, is a chemistry-based portal at the National Center for Biotechnology Information. PubChem provides information not only on chemicals, but also genes, proteins, pathways, as well as patents, bioassays, and more, from hundreds of data resources from around the world. In this work, these 2 portals have made substantial efforts to integrate their complementary data to allow users to cross between these 2 domains. In addition to glycan structures, key information, such as glycan-related genes, relevant diseases, glycoproteins, and pathways, was integrated and cross-linked with one another. The interfaces were designed to enable users to easily find, access, download, and reuse data of interest across these resources. Use cases are described illustrating and highlighting the type of content that can be investigated. In total, these integrations provide life science researchers improved awareness and enhanced access to glycan-related information. [ABSTRACT FROM AUTHOR]

The growth of technologies concerned with the high demand in lithium (Li) sources dictates the need for technological solutions garnering Li supplies to preserve the sustainability of the processes. The aim of this study was to use a machine learning-based search for phosphoryl-containing podandic ligands, potentially selective for lithium extraction from brine. Based on the experimental data available on the stability constant values of phosphoryl-containing organic ligands with Li + and Na + cations at 4:1 THF:CHCl 3 , candidate di-podandic ligands were proposed, for which the stability constant values (logK) with Li + and Na + as well as the corresponding selectivity values were evaluated using machine learning methods (ML). The modelling showed a reasonable predictive performance with the following statistical parameters: the determination coefficient R 2 = 0.75, 0.87 and 0.83 and root-mean-square error RMSE = 0.485, 0.449 and 0.32 were obtained for the prediction of the stability constant values with Li + and Na + cations and Li + /Na + selectivity values, respectively. This ML-based analysis was complemented by the preliminary estimation of the host–guest complementarity of metal–ligand 1:1 complexes using the HostDesigner software. [ABSTRACT FROM AUTHOR]

Data on the diversity; morphological structure; and physical, chemical, and agrochemical properties of soils within the Uldz River floodplain (Mongolia, Uldz–Torei plain) have been obtained for the first time. The predominance of alluvial medium-thick dark humus soil type of saline and quasi-gleic subtypes is established. A very important feature in soil genesis is identified, namely, a high level of groundwater and light texture of alluvial deposits, determining the same type of plant residues transformation (dark humus accumulations). It is concluded that the differences between soils are due to the degree of salinity and the type of chemistry. The alluvial soils under study are alkaline and slightly saline, predominantly of chloride and soda-chloride type of salinity for anions and of magnesium–sodium or sodium–magnesium type for cations. Solonchaks that form in the lower parts of the floodplain have different types of salinity within genetic horizons for anions and a sodium type of salinity for cations. Phytocenoses grown on these soils are of low productivity, projective cover, and depleted species composition and are represented only by halophytes. Unfavorable factors for plant growth are responsible for the occurrence of light humus processes, similar to the zonal soil. The level of natural fertility of the soils under study is found to be very low because of their thin layered profile; unfavorable physical, chemical, and agrochemical properties; and especially low content of nitrate nitrogen and labile phosphorus. Destructive agrogenic processes are described that are possible when alluvial dark humus soils are involved in arable use. These soils should be mainly used as grasslands and hay pastures, and it is recommended that "focal" plots should be allocated for arable land in compliance with soil conservation measures. To increase the biopoductivity of the floodplain lands, it is necessary to use various types of manure, composts, green manure, and mineral fertilizers (primarily nitrogen and phosphorus). Typical solonchaks have unfavorable physical, chemical, and meliorative properties and an extremely low level of natural fertility. In this regard, they should be classified as marginal or virtually unsuitable for use in agricultural production with the occasional grazing of farm animals. [ABSTRACT FROM AUTHOR]

Matrix metalloproteinase-2 (MMP-2) is capable of degrading Collage TypeIV in the vascular basement membrane and extracellular matrix. Studies have shown that MMP-2 is tightly associated with the biological behavior of malignant tumors. Therefore, the identification of inhibitors targeting MMP-2 could be effective in treating the disease by maintaining extracellular matrix homeostasis. In the pharmaceutical and biomedical fields, many computational tools are widely used, which improve the efficiency of the whole process to some extent. Apart from the conventional cheminformatics approaches (e.g., pharmacophore model and molecular docking), virtual screening strategies based on machine learning also have promising applications. In this study, we collected 2871 compound activity data against MMP-2 from the ChEMBL database and divided the training and test sets in a 3:1 ratio. Four machine learning algorithms were then selected to construct the classification models, and the best-performing model, i.e., the stacking-based fusion model with the highest AUC value in both training and test datasets, was used for the virtual screening of ZINC database. Next, we screened 17 potential MMP-2 inhibitors from the results predicted by the machine learning model via ADME/T analysis. The interactions between these compounds and the target protein were explored through molecular docking calculations, and the results showed that ZINC712249, ZINC4270723, and ZINC15858504 had lower binding free energies than the co-crystal ligand. To further examine the binding stability of the complexes, we performed molecular dynamics simulations and finally identified these three hits as the most promising natural products for MMP-2 inhibitors. [ABSTRACT FROM AUTHOR]

Copyright of FACSalud is the property of Revista FACSalud and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

With the recent popularity of food computing, there is a growing demand for research on creating ingredient embeddings by representation learning. The general-purpose representation obtained from a latent space of food ingredients can aid in developing various applications related to food computing. Existing methods create ingredient embeddings based on the ingredient-compound graph, and the co-occurrence in recipe data construct ingredient relationship. However, existing methods need help with the learning process for ingredient representation. When generating a path to input to the graph embedding model, the path disregards the cooccurrence information in the recipe. This method treats high-frequency and low-frequency ingredients in the same way. Hence, when using ingredient embeddings created with existing methods, the need for detailed recipe information may prevent accurate food ingredient recommendations based on the recipe (food pairing recommendation, alternative ingredient recommendation). Our study proposes a novel ingredient embedding method that can solve the abovementioned problems by constructing an ingredient-compound network expressing a containment relationship between an ingredient and its chemical compounds. Our experimental evaluation in the classification task of food ingredients indicated that our method outperforms existing methods, so our ingredient embeddings can express their features in the task. [ABSTRACT FROM AUTHOR]

Drug resistance represents a major obstacle to therapeutic innovations and is a prevalent feature in prostate cancer (PCa). Androgen receptors (ARs) are the hallmark therapeutic target for prostate cancer modulation and AR antagonists have achieved great success. However, rapid emergence of resistance contributing to PCa progression is the ultimate burden of their long-term usage. Hence, the discovery and development of AR antagonists with capability to combat the resistance, remains an avenue for further exploration. Therefore, this study proposes a novel deep learning (DL)-based hybrid framework, named DeepAR, to accurately and rapidly identify AR antagonists by using only the SMILES notation. Specifically, DeepAR is capable of extracting and learning the key information embedded in AR antagonists. Firstly, we established a benchmark dataset by collecting active and inactive compounds against AR from the ChEMBL database. Based on this dataset, we developed and optimized a collection of baseline models by using a comprehensive set of well-known molecular descriptors and machine learning algorithms. Then, these baseline models were utilized for creating probabilistic features. Finally, these probabilistic features were combined and used for the construction of a meta-model based on a one-dimensional convolutional neural network. Experimental results indicated that DeepAR is a more accurate and stable approach for identifying AR antagonists in terms of the independent test dataset, by achieving an accuracy of 0.911 and MCC of 0.823. In addition, our proposed framework is able to provide feature importance information by leveraging a popular computational approach, named SHapley Additive exPlanations (SHAP). In the meanwhile, the characterization and analysis of potential AR antagonist candidates were achieved through the SHAP waterfall plot and molecular docking. The analysis inferred that N-heterocyclic moieties, halogenated substituents, and a cyano functional group were significant determinants of potential AR antagonists. Lastly, we implemented an online web server by using DeepAR (at http://pmlabstack.pythonanywhere.com/DeepAR). We anticipate that DeepAR could be a useful computational tool for community-wide facilitation of AR candidates from a large number of uncharacterized compounds. [ABSTRACT FROM AUTHOR]

This report aims to present the importance of the quality professional training of librarians in the 21st century, as a guarantee for their successful implementation..The report was developed as part of a project "Creating an Eco – chemical Model and Laboratory for Teaching Preservation of Written Cultural Heritage", Contract No КП – 06 - Н 40/1 / 10.12.2019 with project manager Eng. Dr. Iskra Tsvetanska, financed by National Science Fund of Bulgaria. [ABSTRACT FROM AUTHOR]

Dissemination of novel research methods, especially in the form of chemoinformatics software, depends heavily on their ease of applicability for non‐expert users with only a little or no programming skills and knowledge in computer science. Visual programming has become widely popular over the last few years, also enabling researchers without in‐depth programming skills to develop tailored data processing pipelines using elements from a repository of predefined standard procedures. In this work, we present the development of a set of nodes for the KNIME platform implementing the QPhAR algorithm. We show how the developed KNIME nodes can be included in a typical workflow for biological activity prediction. Furthermore, we present best‐practice guidelines that should be followed to obtain high‐quality QPhAR models. Finally, we show a typical workflow to train and optimise a QPhAR model in KNIME for a set of given input compounds, applying the discussed best practices. [ABSTRACT FROM AUTHOR]

It is insightful to report an estimator that describes how certain a model is in a prediction, additionally to the prediction alone. For regression tasks, most approaches implement a variation of the ensemble method, apart from few exceptions. Instead of a single estimator, a group of estimators yields several predictions for an input. The uncertainty can then be quantified by measuring the disagreement between the predictions, for example by the standard deviation. In theory, ensembles should not only provide uncertainties, they also boost the predictive performance by reducing errors arising from variance. Despite the development of novel methods, they are still considered the "golden-standard" to quantify the uncertainty of regression models. Subsampling-based methods to obtain ensembles can be applied to all models, regardless whether they are related to deep learning or traditional machine learning. However, little attention has been given to the question whether the ensemble method is applicable to virtually all scenarios occurring in the field of cheminformatics. In a widespread and diversified attempt, ensembles are evaluated for 32 datasets of different sizes and modeling difficulty, ranging from physicochemical properties to biological activities. For increasing ensemble sizes with up to 200 members, the predictive performance as well as the applicability as uncertainty estimator are shown for all combinations of five modeling techniques and four molecular featurizations. Useful recommendations were derived for practitioners regarding the success and minimum size of ensembles, depending on whether predictive performance or uncertainty quantification is of more importance for the task at hand. [ABSTRACT FROM AUTHOR]

As the primary method of communicating research results, journals garner an enormous impact on community behavior. Publishing the underlying research data alongside journal articles is widely considered good scientific practice. Ideally, journals and their publishers place these recommendations or requirements in their author guidelines and data policies. Several efforts are working to improve the infrastructure, processes, and uptake of research data sharing, including the NFDI4Chem consortium, working groups within the RDA, and IUPAC, including the WorldFAIR Chemistry project. In this article, we present the results of a large-scale analysis of author guidelines from several publishers and journals active in chemistry research, showing how well the publishing landscape supports different criteria and where there is room for improvement. While the requirement for deposition of X-ray diffraction data is commonplace, guidelines rarely mention machine-readable chemical structures and metadata/minimum information standards. Further evaluation criteria included recommendations on persistent identifiers, data availability statements, data deposition into repositories as well as of open analytical data formats. Our survey shows that publishers and journals are starting to include aspects of research data in their guidelines. We as authors should accept and embrace the guidelines with increasing requirements for data availability, data interoperability, and re-usability to improve chemistry research. [ABSTRACT FROM AUTHOR]

Background: The increasing amount of chemical reaction data makes traditional ways to navigate its corpus less effective, while the demand for novel approaches and instruments is rising. Recent data science and machine learning techniques support the development of new ways to extract value from the available reaction data. On the one side, Computer-Aided Synthesis Planning tools can predict synthetic routes in a model-driven approach; on the other side, experimental routes can be extracted from the Network of Organic Chemistry, in which reaction data are linked in a network. In this context, the need to combine, compare and analyze synthetic routes generated by different sources arises naturally. Results: Here we present LinChemIn, a python toolkit that allows chemoinformatics operations on synthetic routes and reaction networks. Wrapping some third-party packages for handling graph arithmetic and chemoinformatics and implementing new data models and functionalities, LinChemIn allows the interconversion between data formats and data models and enables route-level analysis and operations, including route comparison and descriptors calculation. Object-Oriented Design principles inspire the software architecture, and the modules are structured to maximize code reusability and support code testing and refactoring. The code structure should facilitate external contributions, thus encouraging open and collaborative software development. Conclusions: The current version of LinChemIn allows users to combine synthetic routes generated from various tools and analyze them, and constitutes an open and extensible framework capable of incorporating contributions from the community and fostering scientific discussion. Our roadmap envisages the development of sophisticated metrics for routes evaluation, a multi-parameter scoring system, and the implementation of an entire "ecosystem" of functionalities operating on synthetic routes. LinChemIn is freely available at https://github.com/syngenta/linchemin. [ABSTRACT FROM AUTHOR]

Copyright of Journal of Central China Normal University is the property of Huazhong Normal University and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Designing of molecules for drugs is important topic from many decades. The search of new drugs is very hard, and it is expensive process. Computer assisted framework can provide the fastest way to design and screen drug-like compounds. In present work, a multidimensional approach is introduced for the designing and screening of antioxidant compounds. Antioxidants play a crucial role in ensuring that the body's oxidizing and reducing species are kept in the proper balance, minimizing oxidative stress. Machine learning models are used to predict antioxidant activity. Three hydroxycinnamates are selected as standard antioxidants. Similar compounds are searched from ChEMBL database using chemical structural similarity method. The libraries of new compounds are generated using evolutionary method. New compounds are also designed using automatic decomposition and construction building blocks. The antioxidant activity of all designed and searched compounds is predicted using machine learning models. The chemical space of searched and generated compounds is envisioned using t-distributed stochastic neighbor embedding (t-SNE) method. Best compounds are shortlisted, and their synthetic accessibility is predicted to further facilitate the experimental chemists. The chemical similarity between standard and selected compounds is also studied using fingerprints and heatmap.

Scientific workflows facilitate the automation of data analysis tasks by integrating various software and tools executed in a particular order. To enable transparency and reusability in workflows, it is essential to implement the FAIR principles. Here, we describe our experiences implementing the FAIR principles for metabolomics workflows using the Metabolome Annotation Workflow (MAW) as a case study. MAW is specified using the Common Workflow Language (CWL), allowing for the subsequent execution of the workflow on different workflow engines. MAW is registered using a CWL description on WorkflowHub. During the submission process on WorkflowHub, a CWL description is used for packaging MAW using the Workflow RO-Crate profile, which includes metadata in Bioschemas. Researchers can use this narrative discussion as a guideline to commence using FAIR practices for their bioinformatics or cheminformatics workflows while incorporating necessary amendments specific to their research area.

MolOptimizer is a user-friendly computational toolkit designed to streamline the hit-to-lead optimization process in drug discovery. MolOptimizer extracts features and trains machine learning models using a user-provided, labeled, and small-molecule dataset to accurately predict the binding values of new small molecules that share similar scaffolds with the target in focus. Hosted on the Azure web-based server, MolOptimizer emerges as a vital resource, accelerating the discovery and development of novel drug candidates with improved binding properties.

Drug discovery using advanced computational tools such as machine learning has succeeded in reducing about 40% and 60% of the time and costs required by conventional drug discovery pipelines respectively. In this study we aim at building a combinatorial library of anthraquinone and chalcone derivative and producing a workflow of different screening and scoring methodologies to find hits against cancer- related proteins, and examine them using molecular dynamic and mechanics simulations. A combinatorial library, consisting of virtual compounds, was synthesized using 20 anthraquinone and 24 chalcone core structures via R-group enumeration methodology. The resulting compounds were optimized to the near drug-likeness properties and the physicochemical descriptors were calculated for all datasets and compared with commercially available databases such as FDA, Non-FDA, and natural products (NPs) datasets from ZINC 15. A workflow of a novel virtual screening and scoring methods was optimized based on the nature of the protein target. As a result; the optimized enumeration resulted in 1,610,268 compounds with NP-Likeness, and synthetic feasibility mean scores close to FDA, Non-FDA, and NPs datasets. The cheminformatic analysis illustrated an overlap between the chemical space of the generated library was more prominent with NPs with the lowest molecular diversity compared with other natural and synthetic drugs databases. Moreover, the consensus scoring methodology that we produced was based on quantitative structure-activity relationship, pharmacophore fitness, shape similarity, and docking scores. The optimized virtual screening for the protein targets was found to be beneficial in the retrospective enrichment studies, as it prioritized true positives in high percentage (ROC curve > 0.9). Compared to all other conventional screening methods individually, consensus scoring outperformed them. It was also found that this method of multistage virtual screening overcome challenges in the training set such as limited number of data points and limited diversity of activity. In molecular mechanic simulations, the range of activity of the experimental datasets plays a crucial role in the nature of the correlation between experimental activity values and binding free energy obtained by MM/GBSA calculations. In conclusion, consensus scoring using z-score fusion method is a beneficial way of virtual screening especially when the training dataset is imbalanced. [ABSTRACT FROM AUTHOR]

The number of published materials science articles has increased manyfold over the past few decades. Now, a major bottleneck in the materials discovery pipeline arises in connecting new results with the previously established literature. A potential solution to this problem is to map the unstructured raw text of published articles onto structured database entries that allow for programmatic querying. To this end, we apply text mining with named entity recognition (NER) for large-scale information extraction from the published materials science literature. The NER model is trained to extract summary-level information from materials science documents, including inorganic material mentions, sample descriptors, phase labels, material properties and applications, as well as any synthesis and characterization methods used. Our classifier achieves an accuracy (f1) of 87%, and is applied to information extraction from 3.27 million materials science abstracts. We extract more than 80 million materials-science-related named entities, and the content of each abstract is represented as a database entry in a structured format. We demonstrate that simple database queries can be used to answer complex "meta-questions" of the published literature that would have previously required laborious, manual literature searches to answer. All of our data and functionality has been made freely available on our Github ( https://github.com/materialsintelligence/matscholar ) and website ( http://matscholar.com ), and we expect these results to accelerate the pace of future materials science discovery.

Abstract TUCAN is a canonical serialization format that is independent of domain-specific concepts of structure and bonding. The atomic number is the only chemical feature that is used to derive the TUCAN format. Other than that, the format is solely based on the molecular topology. Validation is reported on a manually curated test set of molecules as well as a library of non-chemical graphs. The serialization procedure generates a canonical “tuple-style” output which is bidirectional, allowing the TUCAN string to serve as both identifier and descriptor. Use of the Python NetworkX graph library facilitated a compact and easily extensible implementation. Graphical Abstract

Introduction: A 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. Methods: A 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. Results: Results 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. Conclusion: ML 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.

Ligand-based virtual screening is a widespread method in modern drug design. It allows for a rapid screening of large compound databases in order to identify similar structures. Here we report an open-source command line tool which includes a substructure-, fingerprint- and shape-based virtual screening. Most of the implemented features fully rely on the RDKit cheminformatics framework. VSFlow accepts a wide range of input file formats and is highly customizable. Additionally, a quick visualization of the screening results as pdf and/or pymol file is supported. [ABSTRACT FROM AUTHOR]

Essential oils (EOs) are a promising source for novel environmentally safe insecticides. However, the structural diversity of their compounds poses challenges to accurately elucidate their biological mechanisms of action. We present a new chemoinformatics methodology aimed at predicting the impact of essential oil (EO) compounds on the molecular targets of commercial insecticides. Our approach merges virtual screening, chemoinformatics, and machine learning to identify custom signatures and reference molecule clusters. By assigning a molecule to a cluster, we can determine its most likely interaction targets. Our findings reveal that the main targets of EOs are juvenile hormone-specific proteins (JHBP and MET) and octopamine receptor agonists (OctpRago). Three of the twenty clusters show strong similarities to the juvenile hormone, steroids, and biogenic amines. For instance, the methodology successfully identified E-Nerolidol, for which literature points indications of disrupting insect metamorphosis and neurochemistry, as a potential insecticide in these pathways. We validated the predictions through experimental bioassays, observing symptoms in blowflies that were consistent with the computational results. This new approach sheds a higher light on the ways of action of EO compounds in nature and biotechnology. It also opens new possibilities for understanding how molecules can interfere with biological systems and has broad implications for areas such as drug design. [ABSTRACT FROM AUTHOR]

The Australia Group (AG) is a forum of like-minded states seeking to harmonize export controls to prevent the proliferation of chemical and biological weapons. The AG Chemical Weapons Precursors list features dual-use chemicals that can be used as precursors for the synthesis of chemical weapons, all individually enumerated. This is in contrast with the Chemical Weapons Convention (CWC) Schedules, which, alongside entries describing discrete chemicals, also include entries that describe families of chemicals. By using families of chemicals, the CWC achieves the objective of covering with a single entry a wide array of related chemicals of concern, including chemicals that have not yet been made. There are practical reasons why the AG Chemical Weapons Precursors list is exclusively based on the enumeration of individual chemicals. A cheminformatics tool of which we have developed a prototype, the Nonproliferation Compliance Cheminformatics Tool (NCCT), has the potential to enable export control officers to handle control lists that contain families of chemicals. Thus, it opens the way to expand the AG Chemical Weapons Precursors list to a family-based approach for some of its entries. Such a change would result in a closer alignment of the chemical space covered by the AG Chemical Weapons Precursors list with that covered by the CWC Schedules, thus closing loopholes that could be exploited by proliferators. [ABSTRACT FROM AUTHOR]

Deep learning has gone through breakthroughs in many research fields including computer vision, natural language processing, etc. due to multiple driving factors such as knowledge, data, algorithms and computing power. In addition, it has gradually spawned a number of new research directions with the migration and application as well as cross-integration among various disciplines. Cheminformatics is a discipline that solves chemical problems with the applied informatics methods, and deep learning can be useful since it is very powerful in nonlinear learning. Deep learning model can be used to screen and predict in the data set, and then verify the feasibility of the results based on theoretical calculation. Finally, the results are represented by experiments, which shortens the experimental period, reduces the labor cost and accelerates the intelligence of cheminformatics. This paper briefly introduces the development history and main network model architecture of deep learning as well as the latest research and application status of deep learning in synthesis planning, compound structure-activity relation and catalyst design in recent years, and also discusses and expects the future development direction. [ABSTRACT FROM AUTHOR]

In this work, a novel machine learning based methodology was developed to predict fragrance from the molecular structure and the effect of the subjects attributes on odour perception. As fragrance is linked to the molecular structure and interactions, topological indices are used to develop a predictive model. Rough set‐based machine learning is used to generate rule‐based models that link the topology of fragrant molecules and dilution to their respective odour characteristics. The results show that the generated models are effective in determining the odour characteristic of molecules. [ABSTRACT FROM AUTHOR]

The Hungarian Academy of Sciences (HAS, established in 1825), similar to the academies of the old Soviet bloc, ran a research network from 1950 until 2019 when it was detached from the Academy. The first research institute of the HAS was the Institute of Biochemistry, which started its operation in 1950. Its first director was Imre Szörényi (1905–1959) who lived in emigration in Kiev until he was called back to Hungary in 1950 by the Secretariat of the Hungarian Workers Party. Initially, for a few years research in the Institute was partly influenced by Lepeshinskaya's 'New Cell Theory' and Szörényi himself became the chair of the 'Living Protein' Committee of the HAS. He returned for more than two years to Kiev where he received a shared Stalin Prize in 1952 for the development of the antibiotic, Microcid. After his final return to Hungary in 1953, he was able to shape the characteristic image of the Institute of Biochemistry, making it one of the leading workshops of Hungarian biochemistry. From 1956 onwards, ideological considerations no longer interfered with the choice of research topics. The relationship between the chemical structure and the specific biological function of enzymes became the main profile of the Institute. In spite of his untimely death, Szörényi exerted a long-lasting influence on Hungarian biochemistry through his disciples. [ABSTRACT FROM AUTHOR]

Clinically significant pathogens such as Pseudomonas aeruginosa evade the effects of antibiotics using quorum sensing (QS) systems, making antimicrobial resistance (AMR) a persistent and potentially fatal global health issue. Hence, QS has been identified as a novel therapeutic target for identifying novel drug candidates against P. aeruginosa, and plant-derived products, including essential oils, have been demonstrated as effective QS modulators. This study assessed the antipathogenic efficacy of essential oils from two sunflower cultivars (AGSUN 5102 CLP and AGSUN 5106 CLP) against P. aeruginosa ATCC 27853 in vitro and in silico. At the sub-inhibitory concentrations, both AGSUN 5102 CLP (62.61%) and AGSUN 5106 CLP (59.23%) competed favorably with cinnamaldehyde (60.74%) and azithromycin (65.15%) in suppressing the expression of QS-controlled virulence phenotypes and biofilm formation in P. aeruginosa. A further probe into the mechanism of anti-QS action of the oils over a 100-ns simulation period against Las QS system revealed that phylloquinone (−66.42 ± 4.63 kcal/mol), linoleic acid (−53.14 ± 3.53 kcal/mol), and oleic acid (−52.02 ± 3.91 kcal/mol) had the best affinity and structural compactness as potential modulators of LasR compared to cinnamaldehyde (−16.95 ± 1.75 kcal/mol) and azithromycin (−32.08 ± 10.54 kcal/mol). These results suggest that the identified compounds, especially phylloquinone, could be a possible LasR modulator and may represent a novel therapeutic alternative against infections caused by P. aeruginosa. As a result, phylloquinone could be further studied as a QS modulator and perhaps find utility in developing new therapeutics. [ABSTRACT FROM AUTHOR]

Maximum common substructures (MCS) have received a lot of attention in the chemoinformatics community. They are typically used as a similarity measure between molecules, showing high predictive performance when used in classification tasks, while being easily explainable substructures. In the present work, we applied the Pairwise Maximum Common Subgraph Feature Generation (PMCSFG) algorithm to automatically detect toxicophores (structural alerts) and to compute fingerprints based on MCS. We present a comparison between our MCS‐based fingerprints and 12 well‐known chemical fingerprints when used as features in machine learning models. We provide an experimental evaluation and discuss the usefulness of the different methods on mutagenicity data. The features generated by the MCS method have a state‐of‐the‐art performance when predicting mutagenicity, while they are more interpretable than the traditional chemical fingerprints. [ABSTRACT FROM AUTHOR]

Cytochrome P450 17A1 (CYP17A1) is one of the key enzymes in steroidogenesis that produces dehydroepiandrosterone (DHEA) from cholesterol. Abnormal DHEA production may lead to the progression of severe diseases, such as prostatic and breast cancers. Thus, CYP17A1 is a druggable target for anti-cancer molecule development. In this study, cheminformatic analyses and quantitative structure–activity relationship (QSAR) modeling were applied on a set of 962 CYP17A1 inhibitors (i.e., consisting of 279 steroidal and 683 nonsteroidal inhibitors) compiled from the ChEMBL database. For steroidal inhibitors, a QSAR classification model built using the PubChem fingerprint along with the extra trees algorithm achieved the best performance, reflected by the accuracy values of 0.933, 0.818, and 0.833 for the training, cross-validation, and test sets, respectively. For nonsteroidal inhibitors, a systematic cheminformatic analysis was applied for exploring the chemical space, Murcko scaffolds, and structure–activity relationships (SARs) for visualizing distributions, patterns, and representative scaffolds for drug discoveries. Furthermore, seven total QSAR classification models were established based on the nonsteroidal scaffolds, and two activity cliff (AC) generators were identified. The best performing model out of these seven was model VIII, which is built upon the PubChem fingerprint along with the random forest algorithm. It achieved a robust accuracy across the training set, the cross-validation set, and the test set, i.e., 0.96, 0.92, and 0.913, respectively. It is anticipated that the results presented herein would be instrumental for further CYP17A1 inhibitor drug discovery efforts. [ABSTRACT FROM AUTHOR]

Phyo Phyo Kyaw Zin, Ph.D (Chemistry), is a Cheminformatics Scientist at Terray Therapeutics, where she develops in-silico techniques to advance drug discovery. My current work at Terray Therapeutics helps with overcoming the data scarcity challenge in the field because we are generating extremely large, precise chemical datasets for biological targets of interest at an unparalleled scale using microarray technology. It is more of a vision because it will require advanced cheminformatics techniques, medicinal chemistry, and biology expertise, as well as substantial amounts of genetic, biological, and chemical data. I also develop rapid, reaction-based enumeration methods for enumerating virtual libraries, analyze large chemical data, and extract SAR (structure-activity relationships) and chemical insights for interactive drug design and optimization. [Extracted from the article]

Mortars, which are very important elements for the integrity of historic monuments, consist mainly of calcium carbonate and silicates in different proportions. Chemical dissolution due to exposure in open air is very important for the degradation of mortars. Inorganic nanoparticles with chemical and crystallographic affinity with mortar components are expected to be effective structure stabilizers and agents offering resistance to chemical dissolution. In the present work, we have developed and applied suspensions of amorphous calcium carbonate (ACC), silicon oxide (am-SiO2) and composite nanoparticles by the precipitation of ACC on am-SiO2 and vice versa. The application of suspensions of the synthesized nanoparticles on three different historical mortars of Roman times (1st century AD), retarded their dissolution rate in solutions undersaturated with respect to calcite, in acid pH (6.50, 25 °C). All three test historic mortars, treated with suspensions of the nanoparticles prepared, showed high resistance towards dissolution at pH 6.50. The ability of the nanoparticles' suspension to consolidate the damaged mortar was the key factor in deciding the corresponding effectiveness in the retardation of the rate of dissolution. The combination of ACC with am-SiO2 nanoparticles showed high efficiency for protection from the dissolution of calcite rich mortars. [ABSTRACT FROM AUTHOR]

Chemokines modulate the immune response by regulating the migration of immune cells. They are also known to participate in such processes as cell–cell adhesion, allograft rejection, and angiogenesis. Chemokines interact with two different subfamilies of G protein-coupled receptors: conventional chemokine receptors and atypical chemokine receptors. Here, we focused on the former one which has been linked to many inflammatory diseases, including: multiple sclerosis, asthma, nephritis, and rheumatoid arthritis. Available crystal and cryo-EM structures and homology models of six chemokine receptors (CCR1 to CCR6) were described and tested in terms of their usefulness in structure-based drug design. As a result of structure-based virtual screening for CCR2 and CCR3, several new active compounds were proposed. Known inhibitors of CCR1 to CCR6, acquired from ChEMBL, were used as training sets for two machine learning algorithms in ligand-based drug design. Performance of LightGBM was compared with a sequential Keras/TensorFlow model of neural network for these diverse datasets. A combination of structure-based virtual screening with machine learning allowed to propose several active ligands for CCR2 and CCR3 with two distinct compounds predicted as CCR3 actives by all three tested methods: Glide, Keras/TensorFlow NN, and LightGBM. In addition, the performance of these three methods in the prediction of the CCR2/CCR3 receptor subtype selectivity was assessed. [ABSTRACT FROM AUTHOR]

A series of novel 3,9-disubstituted acridines were synthesized and their biological potential was investigated. The synthetic plan consists of eight reaction steps, which produce the final products, derivatives 17a–17j, in a moderate yield. The principles of cheminformatics and computational chemistry were applied in order to study the relationship between the physicochemical properties of the 3,9-disubstituted acridines and their biological activity at a cellular and molecular level. The selected 3,9-disubstituted acridine derivatives were studied in the presence of DNA using spectroscopic (UV-Vis, circular dichroism, and thermal denaturation) and electrophoretic (nuclease activity, relaxation and unwinding assays for topoisomerase I and decatenation assay for topoisomerase IIα) methods. Binding constants (2.81–9.03 × 104 M−1) were calculated for the derivatives from the results of the absorption titration spectra. The derivatives were found to have caused the inhibition of both topoisomerase I and topoisomerase IIα. Molecular docking simulations suggested a different way in which the acridines 17a–17j can interact with topoisomerase I versus topoisomerase IIα. A strong correlation between the lipophilicity of the derivatives and their ability to stabilize the intercalation complex was identified for all of the studied agents. Acridines 17a–17j were also subjected to in vitro screening conducted by the Developmental Therapeutic Program of the National Cancer Institute (NCI) against a panel of 60 cancer cell lines. The strongest biological activity was displayed by aniline acridine 17a (MCF7–GI50 18.6 nM) and N,N-dimethylaniline acridine 17b (SR–GI50 38.0 nM). The relationship between the cytostatic activity of the most active substances (derivatives 17a, 17b, and 17e–17h) and their values of KB, LogP, ΔS°, and δ was also investigated. Due to the fact that a significant correlation was only found in the case of charge density, δ, it is possible to assume that the cytostatic effect might be dependent upon the structural specificity of the acridine derivatives. [ABSTRACT FROM AUTHOR]