Search

Abstract PubChem ( https://pubchem.ncbi.nlm.nih.gov ) is a public chemical information resource containing more than 100 million unique chemical structures. One of the most requested tasks in PubChem and other chemical databases is to search chemicals by name (also commonly called a “chemical synonym”). PubChem performs this task by looking up chemical synonym-structure associations provided by individual depositors to PubChem. In addition, these synonyms are used for many purposes, including creating links between chemicals and PubMed articles (using Medical Subject Headings (MeSH) terms). However, these depositor-provided name-structure associations are subject to substantial discrepancies within and between depositors, making it difficult to unambiguously map a chemical name to a specific chemical structure. The present paper describes PubChem’s crowdsourcing-based synonym filtering strategy, which resolves inter- and intra-depositor discrepancies in synonym-structure associations as well as in the chemical-MeSH associations. The PubChem synonym filtering process was developed based on the analysis of four crowd-voting strategies, which differ in the consistency threshold value employed (60% vs 70%) and how to resolve intra-depositor discrepancies (a single vote vs. multiple votes per depositor) prior to inter-depositor crowd-voting. The agreement of voting was determined at six levels of chemical equivalency, which considers varying isotopic composition, stereochemistry, and connectivity of chemical structures and their primary components. While all four strategies showed comparable results, Strategy I (one vote per depositor with a 60% consistency threshold) resulted in the most synonyms assigned to a single chemical structure as well as the most synonym-structure associations disambiguated at the six chemical equivalency contexts. Based on the results of this study, Strategy I was implemented in PubChem’s filtering process that cleans up synonym-structure associations as well as chemical-MeSH associations. This consistency-based filtering process is designed to look for a consensus in name-structure associations but cannot attest to their correctness. As a result, it can fail to recognize correct name-structure associations (or incorrect ones), for example, when a synonym is provided by only one depositor or when many contributors are incorrect. However, this filtering process is an important starting point for quality control in name-structure associations in large chemical databases like PubChem.

PubChem (https://pubchem.ncbi.nlm.nih.gov) is a public chemical information resource containing more than 100 million unique chemical structures. One of the most requested tasks in PubChem and other chemical databases is to search chemicals by name (also commonly called a "chemical synonym"). PubChem performs this task by looking up chemical synonym-structure associations provided by individual depositors to PubChem. In addition, these synonyms are used for many purposes, including creating links between chemicals and PubMed articles (using Medical Subject Headings (MeSH) terms). However, these depositor-provided name-structure associations are subject to substantial discrepancies within and between depositors, making it difficult to unambiguously map a chemical name to a specific chemical structure. The present paper describes PubChem's crowdsourcing-based synonym filtering strategy, which resolves inter- and intra-depositor discrepancies in synonym-structure associations as well as in the chemical-MeSH associations. The PubChem synonym filtering process was developed based on the analysis of four crowd-voting strategies, which differ in the consistency threshold value employed (60% vs 70%) and how to resolve intra-depositor discrepancies (a single vote vs. multiple votes per depositor) prior to inter-depositor crowd-voting. The agreement of voting was determined at six levels of chemical equivalency, which considers varying isotopic composition, stereochemistry, and connectivity of chemical structures and their primary components. While all four strategies showed comparable results, Strategy I (one vote per depositor with a 60% consistency threshold) resulted in the most synonyms assigned to a single chemical structure as well as the most synonym-structure associations disambiguated at the six chemical equivalency contexts. Based on the results of this study, Strategy I was implemented in PubChem's filtering process that cleans up synonym-structure associations as well as chemical-MeSH associations. This consistency-based filtering process is designed to look for a consensus in name-structure associations but cannot attest to their correctness. As a result, it can fail to recognize correct name-structure associations (or incorrect ones), for example, when a synonym is provided by only one depositor or when many contributors are incorrect. However, this filtering process is an important starting point for quality control in name-structure associations in large chemical databases like PubChem. [ABSTRACT FROM AUTHOR]

The IUPAC International Chemical Identifier (InChI) is a structure-based chemical identifier that encodes various aspects of a chemical structure into a hierarchically layered line notation. Because InChI is non-proprietary, open-source, and freely available to everyone, it is adopted in popular chemical information resources and software programs. This paper describes the InChI Open Education Resource (OER) (https://www.inchi-trust.org/oer/), designed to provide educators and other interested parties with resources, training material, and information related to InChI. Currently, the OER contains over 100 materials collected from various sources and provides users with search, filtering, and sorting functionalities to locate specific records. New relevant materials can be suggested by anyone, allowing the scientific community to share and find InChI-related resources. This paper will show how to use the InChI OER tag taxonomy to filter content and demonstrate two resources within the InChI OER; the ChemNames2LCSS Google Sheet and the InChILayersExplorer, an Excel spreadsheet that breaks an InChI into its layers. While the InChI OER is of value to a broader chemistry community, this paper seeks to reach out to chemical educators and provide them with an understanding of InChI and its role in the practice of science.

Abstract Knowledge about the 3-dimensional structure, orientation and interaction of chemical compounds is important in many areas of science and technology. X-ray crystallography is one of the experimental techniques capable of providing a large amount of structural information for a given compound, and it is widely used for characterisation of organic and metal-organic molecules. The method provides precise 3D coordinates of atoms inside crystals, however, it does not directly deliver information about certain chemical characteristics such as bond orders, delocalization, charges, lone electron pairs or lone electrons. These aspects of a molecular model have to be derived from crystallographic data using refined information about interatomic distances and atom types as well as employing general chemical knowledge. This publication describes a curated automatic pipeline for the derivation of chemical attributes of molecules from crystallographic models. The method is applied to build a catalogue of chemical entities in an open-access crystallographic database, the Crystallography Open Database (COD). The catalogue of such chemical entities is provided openly as a derived database. The content of this catalogue and the problems arising in the fully automated pipeline are discussed, along with the possibilities to introduce manual data curation into the process.

Knowledge about the 3-dimensional structure, orientation and interaction of chemical compounds is important in many areas of science and technology. X-ray crystallography is one of the experimental techniques capable of providing a large amount of structural information for a given compound, and it is widely used for characterisation of organic and metal-organic molecules. The method provides precise 3D coordinates of atoms inside crystals, however, it does not directly deliver information about certain chemical characteristics such as bond orders, delocalization, charges, lone electron pairs or lone electrons. These aspects of a molecular model have to be derived from crystallographic data using refined information about interatomic distances and atom types as well as employing general chemical knowledge. This publication describes a curated automatic pipeline for the derivation of chemical attributes of molecules from crystallographic models. The method is applied to build a catalogue of chemical entities in an open-access crystallographic database, the Crystallography Open Database (COD). The catalogue of such chemical entities is provided openly as a derived database. The content of this catalogue and the problems arising in the fully automated pipeline are discussed, along with the possibilities to introduce manual data curation into the process. [ABSTRACT FROM AUTHOR]

Remarkable structural homologies between the main proteases of the severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) and the avian infectious bronchitis virus (IBV) were revealed by comparative amino-acid sequence and 3D structural alignment. Assessing whether reported IBV 3CLPro inhibitors could also interact with SARS-CoV-2 has been undertaken in silico using a PubChem BioAssay database of 388 compounds active on the avian infectious bronchitis virus 3C-like protease. Docking studies of this database on the SARS-CoV-2 protease resulted in the identification of four covalent inhibitors targeting the catalytic cysteine residue and five non-covalent inhibitors for which the binding was further investigated by molecular dynamics (MD) simulations. Predictive ADMET calculations on the nine compounds suggest promising pharmacokinetic properties. [ABSTRACT FROM AUTHOR]

Acacia honey is characterized by high nutritional, antioxidant, antibacterial and immuno-modulatory values. This work investigated the presence of short and cyclic peptides in Acacia and Ziziphus honey samples. Acacia honey samples (Acacia tortilis and Acacia hamulosa) and three Ziziphus honeys (Ziziphus spina-christi) were screened for their short and cyclic peptide contents using the LC-MS and the chemical structure databases. Moreover, the total protein content was determined using the Bradford method. The A. tortilis honey contained three short peptides; HWCC, DSST, and ECH, and the A. hamulosa honey sample contained five short peptides and one cyclic peptide. The short peptides of the A. hamulosa honey were Ac-GMGHG-OH (Ac-MGGHG-OH), Boc-R(Aloc)2-C(Pal)-OH, H-C (1)-NEt2·H-C (1)-NEt2, APAP (AAPP), and GAFQ (deamino-2-pyrid-4-yl-glycyl-dl-alanyl-dl-norvalyl-dl-asparagine). The cyclic peptide of the A. hamulosa honey was cyclo[Aad-RGD-d-F] (cyclo[Aad-Arg-Gly-Asp-d-Phe]). The Ziziphus honey was characterized by the presence of either Almiramide B or Auristatin-6-AQ. A. tortilis, A. hamulosa, and Ziziphus honeys are characterized by the presence of short and cyclic peptides which may contribute to their medicinal values.

AXL kinase is a promising target in novel drug discovery for cancer. A ligand-based pharmacophore model was generated with the Pharmit web server. Its inbuilt PubChem molecule database was screened and led to 408 candidate molecules. Docking of the AXL kinase active sites with the identified list of candidate molecules was carried out with Autodock Vina docking software. This resulted in four compounds selected for further investigation. Molecular dynamics simulation of two ligands (PubChem-122421875 and PubChem-78160848) showed considerable binding with AXL kinase. From the MM-PBSA binding free energies investigation, the PubChem-122421875 (G = −179.3 kJ/mol) and PubChem-78160848 (G = −208.3 kJ/mol) ligands had favorable protein-ligand complex stability and binding free energy. Hence, PubChem-122421875 and PubChem-78160848 molecules identified in this work could be a potent starting point for developing novel AXL kinase inhibitor molecules. [ABSTRACT FROM AUTHOR]

Remarkable structural homologies between the main proteases of the severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) and the avian infectious bronchitis virus (IBV) were revealed by comparative amino-acid sequence and 3D structural alignment. Assessing whether reported IBV 3CLPro inhibitors could also interact with SARS-CoV-2 has been undertaken in silico using a PubChem BioAssay database of 388 compounds active on the avian infectious bronchitis virus 3C-like protease. Docking studies of this database on the SARS-CoV-2 protease resulted in the identification of four covalent inhibitors targeting the catalytic cysteine residue and five non-covalent inhibitors for which the binding was further investigated by molecular dynamics (MD) simulations. Predictive ADMET calculations on the nine compounds suggest promising pharmacokinetic properties.

Abstract The software for the IUPAC Chemical Identifier, InChI, is extraordinarily reliable. It has been tested on large databases around the world, and has proved itself to be an essential tool in the handling and integration of large chemical databases. InChI version 1.05 was released in January 2017 and version 1.06 in December 2020. In this paper, we report on the current state of the InChI Software, the details of the improvements in the v1.06 release, and the results of a test of the InChI run on PubChem, a database of more than a hundred million molecules. The upgrade introduces significant new features, including support for pseudo-element atoms and an improved description of polymers. We expect that few, if any, applications using the standard InChI will need to change as a result of the changes in version 1.06. Numerical instability was discovered for 0.002% of this database, and a small number of other molecules were discovered for which the algorithm did not run smoothly. On the basis of PubChem data, we can demonstrate that InChI version 1.05 was 99.996% accurate, and InChI version 1.06 represents a step closer to perfection. Finally, we look forward to future releases and extensions for the InChI Chemical identifier.

Deep learning's automatic feature extraction has been a revolutionary addition to computational drug discovery, infusing both the capabilities of learning abstract features and discovering complex molecular patterns via learning from molecular data. Since biological and chemical knowledge are necessary for overcoming the challenges of data curation, balancing, training, and evaluation, it is important for databases to contain information regarding the exact target and disease of each bioassay. The existing depositories such as PubChem or ChEMBL offer the screening data for millions of molecules against a variety of cells and targets, however, their bioassays contain complex biological descriptions which can hinder their usage by the machine learning community. In this work, a comprehensive disease and target-based dataset is collected from PubChem in order to facilitate and accelerate molecular machine learning for better drug discovery. MolData is one the largest efforts to date for democratizing the molecular machine learning, with roughly 170 million drug screening results from 1.4 million unique molecules assigned to specific diseases and targets. It also provides 30 unique categories of targets and diseases. Correlation analysis of the MolData bioassays unveils valuable information for drug repurposing for multiple diseases including cancer, metabolic disorders, and infectious diseases. Finally, we provide a benchmark of more than 30 models trained on each category using multitask learning. MolData aims to pave the way for computational drug discovery and accelerate the advancement of molecular artificial intelligence in a practical manner. The MolData benchmark data is available at https://GitHub.com/Transilico/MolData as well as within the additional files. [ABSTRACT FROM AUTHOR]

In silico methodologies can be used in the discovery of new drugs for measuring toxicity, predicting effects of substances not yet analyzed by in vivo methodologies. The ADMET Predictor® software (absorption, distribution, metabolism, elimination, and toxicity [ADMET]) was used in this work to predict toxic effects of microcystin variants MC-LR, MC-YR, MC-RR, and MC-HarHar. In the case of rodents, predictive results for all analyzed variants indicated carcinogenic potential. The predictive model of respiratory sensitivity in this group differentiated microcystins into 2 categories: sensitizer (MC-LR and -YR) and non-sensitizer (MC-HarHar and –RR). Predictive results for humans indicated that MC-LR and –RR are phospholipidosis inducers; on the other hand, MC-LR showed the highest predictive value of permeability in rabbit cornea and probability of crossing lipoprotein barriers (MC-LR>-YR>-HarHar>-RR). Considering bioavailable fractions, microcystins are more likely to cause biological effects in rats than humans, showing significant differences between models. The results of ADMET predictions add valuable information on microcystin toxicity, especially in the case of variants not yet studied experimentally. • The ADMET software was used to predict toxic effects of microcystin variants. • Results from simulations were useful to add information on microcystins toxicity and bioavailability. • ADMET predictions add toxicity information on microcystin variant not yet studied. • Data reinforce the importance of carefully transposing experimental data carried out with rodents to humans. [ABSTRACT FROM AUTHOR]

PubChem (https://pubchem.ncbi.nlm.nih.gov) is one of the top five most visited chemistry web sites in the world, with more than five million unique users per month (as of March 2020). Many of these users are educators, undergraduate students, and graduate students at academic institutions. Therefore, PubChem has a great potential as an online resource for chemical education. This paper describes the PubChem Periodic Table and Element pages, which were recently introduced to celebrate the 150th anniversary of the periodic table. These services help users navigate the abundant chemical element data available within PubChem, while providing a convenient entry point to explore additional chemical content, such as biological activities and health and safety data available in PubChem Compound pages for specific elements and their isotopes. The PubChem Periodic Table and Element pages are also available as widgets, which enable web developers to display PubChem’s element data on web pages they design. The elemental data can be downloaded in common file formats and imported into data analysis programs (e.g., spreadsheet software, like Microsoft Excel and Google Sheets, and computer scripts, such as python and R). Overall, the PubChem Periodic Table and Element pages improve access to chemical element data from authoritative sources.

AXL kinase is a promising target in novel drug discovery for cancer. A ligand-based pharmacophore model was generated with the Pharmit web server. Its inbuilt PubChem molecule database was screened and led to 408 candidate molecules. Docking of the AXL kinase active sites with the identified list of candidate molecules was carried out with Autodock Vina docking software. This resulted in four compounds selected for further investigation. Molecular dynamics simulation of two ligands (PubChem-122421875 and PubChem-78160848) showed considerable binding with AXL kinase. From the MM-PBSA binding free energies investigation, the PubChem-122421875 (G = −179.3 kJ/mol) and PubChem-78160848 (G = −208.3 kJ/mol) ligands had favorable protein-ligand complex stability and binding free energy. Hence, PubChem-122421875 and PubChem-78160848 molecules identified in this work could be a potent starting point for developing novel AXL kinase inhibitor molecules.

Tuberculosis (TB) is caused by Mycobacteriun tuberculosis bacteria (MTB) which is an infectious disease. Tuberculosis usually attacks in lungs, but can also affect other parts of the body. Inspite of newer modalities for diagnosis and treatment of TB, unfortunately, people are still suffering, and worldwide it is among the top 10 killer infectious diseases, second to HIV. DNA gyrase, is a subclass of the enzyme Type II Topoisomerase, it helps to reduces topological strain in an ATP dependent manner while double-stranded DNA is being unwound by elongating RNA-polymerase. Molecular docking has a vital role in drug discovery. Molecular docking is a technique that is used for determining the interaction between the ligands and a target protein to prepare the drug. The protein which was used in docking study was downloaded from the online server Uniprot. All natural compounds were used for docking study and were downloaded from Pubchem. All Ligands L-Arabinose, L-Rhaminose and D-Xylose were selected from different plants. [ABSTRACT FROM AUTHOR]

The software for the IUPAC Chemical Identifier, InChI, is extraordinarily reliable. It has been tested on large databases around the world, and has proved itself to be an essential tool in the handling and integration of large chemical databases. InChI version 1.05 was released in January 2017 and version 1.06 in December 2020. In this paper, we report on the current state of the InChI Software, the details of the improvements in the v1.06 release, and the results of a test of the InChI run on PubChem, a database of more than a hundred million molecules. The upgrade introduces significant new features, including support for pseudo-element atoms and an improved description of polymers. We expect that few, if any, applications using the standard InChI will need to change as a result of the changes in version 1.06. Numerical instability was discovered for 0.002% of this database, and a small number of other molecules were discovered for which the algorithm did not run smoothly. On the basis of PubChem data, we can demonstrate that InChI version 1.05 was 99.996% accurate, and InChI version 1.06 represents a step closer to perfection. Finally, we look forward to future releases and extensions for the InChI Chemical identifier. [ABSTRACT FROM AUTHOR]

Abstract PubChem is a chemical data repository that provides comprehensive information on various chemical entities. It contains a wealth of chemical information from hundreds of data sources. Programmatic access to this large amount of data provides researchers with new opportunities for data-intensive research. PubChem provides several programmatic access routes. One of these is PUG-View, which is a Representational State Transfer (REST)-style web service interface specialized for accessing annotation data contained in PubChem. The present paper describes various aspects of PUG-View, including the scope of data accessible through PUG-View, the syntax for formulating a PUG-View request URL, the difference of PUG-View from other web service interfaces in PubChem, and its limitations and usage policies.

Acacia honey is characterized by high nutritional, antioxidant, antibacterial and immuno-modulatory values. This work investigated the presence of short and cyclic peptides in Acacia and Ziziphus honey samples. Acacia honey samples (Acacia tortilis and Acacia hamulosa) and three Ziziphus honeys (Ziziphus spina-christi) were screened for their short and cyclic peptide contents using the LC-MS and the chemical structure databases. Moreover, the total protein content was determined using the Bradford method. The A. tortilis honey contained three short peptides; HWCC, DSST, and ECH, and the A. hamulosa honey sample contained five short peptides and one cyclic peptide. The short peptides of the A. hamulosa honey were Ac-GMGHG-OH (Ac-MGGHG-OH), Boc-R(Aloc)2-C(Pal)-OH, H-C (1)-NEt2·H-C (1)-NEt2, APAP (AAPP), and GAFQ (deamino-2-pyrid-4-yl-glycyl-DL-alanyl-DL-norvalyl-DL-asparagine). The cyclic peptide of the A. hamulosa honey was cyclo[Aad-RGD-D-F] (cyclo [Aad-Arg-Gly-Asp-D-Phe]). The Ziziphus honey was characterized by the presence of either Almiramide B or Auristatin-6-AQ. A. tortilis, A. hamulosa, and Ziziphus honeys are characterized by the presence of short and cyclic peptides which may contribute to their medicinal values. [ABSTRACT FROM AUTHOR]

Background: Virtual screening (VS) is essential for analyzing potential drug candidates in drug discovery. Often, this involves the conversion of large volumes of compound data into specific formats suitable for computational analysis. Managing and processing this wealth of information, especially when dealing with vast numbers of compounds in various forms, such as names, identifiers, or SMILES strings, can present significant logistical and technical challenges., Methods: To streamline this process, we developed PyComp, a software tool using Python's PyQt5 library, and compiled it into an executable with Pyinstaller. PyComp provides a systematic way for users to retrieve and convert a list of compound names, IDs (even in a range), or SMILES strings into the desired 3D format., Results: PyComp greatly enhances the efficiency of data extraction, conversion, and storage processes involved in VS. It searches for similar compounds coupled with its ability to handle misidentified compounds and offers users an easy-to-use, customizable tool for managing largescale compound data. By streamlining these operations, PyComp allows researchers to save significant time and effort, thus accelerating the pace of drug discovery research., Conclusion: PyComp effectively addresses some of the most pressing challenges in highthroughput VS: efficient management and conversion of large volumes of compound data. As a user-friendly, customizable software tool, PyComp is pivotal in improving the efficiency and success of large-scale drug screening efforts, paving the way for faster discovery of potential therapeutic compounds., (Copyright© Bentham Science Publishers; For any queries, please email at epub@benthamscience.net.)

Biomedical information mining is increasingly recognized as a promising technique to accelerate drug discovery and development. Especially, integrative approaches which mine data from several (open) data sources have become more attractive with the increasing possibilities to programmatically access data through Application Programming Interfaces (APIs). The use of open data in conjunction with free, platform-independent analytic tools provides the additional advantage of flexibility, re-usability, and transparency. Here, we present a strategy for performing ligand-based in silico drug repurposing with the analytics platform KNIME. We demonstrate the usefulness of the developed workflow on the basis of two different use cases: a rare disease (here: Glucose Transporter Type 1 (GLUT-1) deficiency), and a new disease (here: COVID 19). The workflow includes a targeted download of data through web services, data curation, detection of enriched structural patterns, as well as substructure searches in DrugBank and a recently deposited data set of antiviral drugs provided by Chemical Abstracts Service. Developed workflows, tutorials with detailed step-by-step instructions, and the information gained by the analysis of data for GLUT-1 deficiency syndrome and COVID-19 are made freely available to the scientific community. The provided framework can be reused by researchers for other in silico drug repurposing projects, and it should serve as a valuable teaching resource for conveying integrative data mining strategies. [ABSTRACT FROM AUTHOR]

Natural extracts obtained from medicinal plants have proved to be useful for their wound healing properties. The aim of this study was to identify and validate the role of commonly used adjuvant compounds in preparing scaffolds using bioinformatics analysis methods. Compounds under investigation included chitosan, chondroitin sulphate and agar. Primary information on candidate compounds were retrieved from PubChem database and their skin sensitization potentials were evaluated. Their various interacting molecules and targets were identified using target prediction tools. Subsequently, functional roles of each of the targets were validated from UniProt database. The results revealed that all the three adjuvant compounds possess relevant target molecules, though the interaction seems to be weak. The corresponding functional analysis portrayed that they specifically contribute in promoting wound healing process. Hence these results validate the use of adjuvants in developing scaffolds. Moreover, this study also provides insights into novel strategies for bioinformatics analysis of role of scaffold components.

Abstract Background PubChem is a chemical information repository, consisting of three primary databases: Substance, Compound, and BioAssay. When individual data contributors submit chemical substance descriptions to Substance, the unique chemical structures are extracted and stored into Compound through an automated process called structure standardization. The present study describes the PubChem standardization approaches and analyzes them for their success rates, reasons that cause structures to be rejected, and modifications applied to structures during the standardization process. Furthermore, the PubChem standardization is compared to the structure normalization of the IUPAC International Chemical Identifier (InChI) software, as manifested by conversion of the InChI back into a chemical structure. Results The observed rejection rate for substances processed by PubChem standardization was 0.36%, which is predominantly attributed to structures with invalid atom valences that cannot be readily corrected without additional information from contributors. Of all structures that pass standardization, 44% are modified in the process, reducing the count of unique structures from 53,574,724 in substance to 45,808,881 in compound as identified by de-aromatized canonical isomeric SMILES. Even though the processing time is very low on average (only 0.4% of structures have individual standardization time above 0.1 s), total standardization time is completely dominated by edge cases: 90% of the time to standardize all structures in PubChem substance is spent on the 2.05% of structures with the highest individual standardization time. It is worth noting that 60% of the structures obtained from PubChem structure standardization are not identical to the chemical structure resulting from the InChI (primarily due to preferences for a different tautomeric form). Conclusions Standardization of chemical structures is complicated by the diversity of chemical information and their representations approaches. The PubChem standardization is an effective and efficient tool to account for molecular diversity and to eliminate invalid/incomplete structures. Further development will concentrate on improved tautomer consideration and an expanded stereocenter definition. Modifications are difficult to thoroughly validate, with slight changes often affecting many thousands of structures and various edge cases. The PubChem structure standardization service is accessible as a public resource (https://pubchem.ncbi.nlm.nih.gov/standardize), and via programmatic interfaces.

Buasbuas (Premna pubescens) is a plant that is traditionally known to have medicinal properties. This study aims to determine the content of bioactive compounds contained in the petiole by Gas Chromatography Mass Spectrophotometer (GCMS) method. Preparation of ethanol extract of petiole using maceration method with 96% ethanol solvent. The study used gas chromatographic tools and mass spectra which were evaluated using MASSLAB program. The data obtained from the GCMS machine is then analyzed using the NCBI database pubchem software (https://pubchem.ncbi.nlm.nih.gov/). The results of this study indicate that the content of bioactive compounds on the petioles of buasbuas more than 50 libraries contains about 150 species of compounds with a range of RT and% area respectively 4.684 to 28.155 and 0.16 to 15.56%. The content of bioactive compounds shown this data indicates that very large potential of buasbuas plants to be explored and exploitation as a nutritious plant. The results of this study can be used as the foundation in the development program of the potential utilization of bioassemblance of buasbuas plants. In further research it is necessary to analyze the other parts of the plant and make comparisons to complete the available databases.

Artificial intelligence (AI) is undergoing a revolution thanks to the breakthroughs of machine learning algorithms in computer vision, speech recognition, natural language processing and generative modelling. Recent works on publicly available pharmaceutical data showed that AI methods are highly promising for Drug Target prediction. However, the quality of public data might be different than that of industry data due to different labs reporting measurements, different measurement techniques, fewer samples and less diverse and specialized assays. As part of a European funded project (ExCAPE), that brought together expertise from pharmaceutical industry, machine learning, and high-performance computing, we investigated how well machine learning models obtained from public data can be transferred to internal pharmaceutical industry data. Our results show that machine learning models trained on public data can indeed maintain their predictive power to a large degree when applied to industry data. Moreover, we observed that deep learning derived machine learning models outperformed comparable models, which were trained by other machine learning algorithms, when applied to internal pharmaceutical company datasets. To our knowledge, this is the first large-scale study evaluating the potential of machine learning and especially deep learning directly at the level of industry-scale settings and moreover investigating the transferability of publicly learned target prediction models towards industrial bioactivity prediction pipelines. [ABSTRACT FROM AUTHOR]

Severe acute respiratory syndrome coronavirus (SARS-CoV-2) is an emerging new viral pathogen that causes severe respiratory disease. SARS-CoV-2 is responsible for the outbreak of COVID-19 pandemic worldwide. As there are no confirmed antiviral drugs or vaccines currently available for the treatment of COVID-19, discovering potent inhibitors or vaccines are urgently required for the benefit of humanity. The glycosylated Spike protein (S-protein) directly interacts with human angiotensin-converting enzyme 2 (ACE2) receptor through the receptor-binding domain (RBD) of S-protein. As the S-protein is exposed to the surface and is essential for entry into the host, the S-protein can be considered as a first-line therapeutic target for antiviral therapy and vaccine development. In silico screening, docking, and molecular dynamics simulation studies were performed to identify repurposing drugs using DrugBank and PubChem library against the RBD of S-protein. The study identified a laxative drug, Bisoxatin (DB09219), which is used for the treatment of constipation and preparation of the colon for surgical procedures. It binds nicely at the S-protein–ACE2 interface by making substantial π-π interactions with Tyr505 in the ‘Site 1’ hook region of RBD and hydrophilic interactions with Glu406, Ser494, and Thr500. Bisoxatin consistently binds to the protein throughout the 100 ns simulation. Taken together, we propose that the discovered molecule, Bisoxatin may be a promising repurposable drug molecule to develop new chemical libraries for inhibiting SARS-CoV-2 entry into the host. [ABSTRACT FROM AUTHOR]

Ligand efficiency (LE) is a molecular descriptor that probes the ratio of potency vs. heavy atom count (HAC). As an estimator of drug candidates, LE emphasizes a low heavy atom count more than potency. The objective was to design a novel transform where potency and the HAC would be balanced more evenly. A series of novel descriptors SCORE were defined to evaluate the co-influence of potency and the HAC. In particular, the product ligand efficiency (PLE) was designed and tested using the data of the ChEMBL, PubChem as well as the selected series of drugs and drug-fragments. [ABSTRACT FROM AUTHOR]