Product Database Research Articles

Technology Focus: Intelligent Operations (May 2023)

The first paper selected for this Intelligent Fields Technical Focus presents the use of a unique artificial-intelligence (AI) technique from the fourth industrial revolution—dual heuristic dynamic programming—to address business opportunities associated with optimization. The background, methodology, and novelty presented by the authors are clear; however, they need to benchmark their efforts and claims of improvement with peers. The second paper demonstrates well-structured content, though the topic is detailed, with the introduction of a digital laboratory called Ibtikar toward the enablement of connecting to open-source tools with the use of AI. The collaboration in research and development, as well as addressing challenges of upstream oil and gas requirements, is commendable. The authors, however, do not address associated security concerns concerning the open-source aspect of the technology. The third paper presents a combination of technologies used to automate daily data acquisition and model updates from production databases and run a supervised machine-learning model to continuously calibrate the network model. Recommended additional reading at OnePetro: www.onepetro.org. SPE 207188 Design and Development of a Solar-Powered Pump System With Liquid-Level Sensor and Controller Using Internet of Things Technology by Chinonyelum Ejimuda, Afe Babalola University, et al. SPE 211314 Improved Field Production Planning and Cost Optimization Using Predictive Analytical Choke Performance Model and Intelligent Alarms in an Integrated Platform by Ayesha Alsaeedi, Adnoc, et al. SPE 211367 Multidisciplinary Data Integration for Artificial-Intelligence-Assisted Well Placement and Trajectory-Design Optimization Under Uncertainty by Shi Su, SLB, et al.

Identification of indole-based natural compounds as inhibitors of PARP-1 against triple-negative breast cancer: a computational study

Triple-negative breast cancer (TNBC) is the most aggressive kind of breast cancer known to mankind. It is a heterogeneous disease that is formed due to the missing estrogen, progesterone and human epidermal growth factor 2 receptors. Poly(ADP-ribose) polymerase-1 (PARP-1) protein helps in the development of TNBC by repairing the cancer cells, which proliferate and spread metastatically. To determine the potential PARP-1 inhibitors (PARPi), 0.2 million natural products from Universal Natural Product Database were screened using molecular docking and six hit compounds were selected based on their binding affinity towards PARP-1. The bio-availability and drug-like properties of these natural products were evaluated using ADMET analysis. Molecular dynamics simulations were conducted for these complexes for 200 ns to examine their structural stability and dynamic behaviour and further compared with the complex of talazoparib (TALA), an FDA-approved PARPi. Using MM/PBSA calculations, we conclude that the complexes HIT-3 and HIT-5 (−25.64 and −23.14 kcal/mol, respectively) show stronger binding energies with PARP-1 than TALA with PARP-1 (−10.74 kcal/mol). Strong interactions were observed between the compounds and hotspot residues, Asp770, Ala880, Tyr889, Tyr896, Ala898, Asp899 and Tyr907, of PARP-1 due to the existence of various types of non-covalent interactions between the compounds and PARP-1. This research offers critical information about PARPi, which could potentially be incorporated into the treatment of TNBC. Moreover, these findings were validated by comparing them with an FDA-approved PARPi.

Rapid comparison of antitumor chemical constituents and mechanisms between Dendrobium nobile and Dendrobium officinale by UPLC-IT-TOF, network pharmacology and experimental verification

AbstractDendrobium nobile and Dendrobium officinale as the main varieties of traditional Chinese medicine Dendrobium are widely used in clinic. The study aimed to systematically explore chemical constituents and their antitumor effect of D. nobile and D. officinale by ultra-performance liquid chromatography coupled with ion trap time-of-flight mass spectrometry (UPLC-IT-TOF), network pharmacology and cancer cell experiments. D. nobile extract and D. officinale extract could significantly inhibit the proliferation of human lung cancer A549 cells, human liver cancer HepG2 cells and human breast cancer MCF-7 cells in the dose-dependent manner (P < 0.05), the antitumor effect of D. officinale extract was stronger than that of D. nobile extract at the same drug concentration. A total of 40 chemical constituents of D. nobile and D. officinale including phenanthrenes, bibenzyls and other types of compounds had been identified by UPLC-IT-TOF, LCMSsolution and MetID software according to retention times, accurate mass, MSn fragmentation, reference compounds and natural product databases. Phenanthrenes with good antitumor activity were mainly present in D. nobile, bibenzyls were the main compounds of D. officinale. Integrated networks of Herb-Compounds-Targets-Cancer revealed that gigantol, moscatilin, tristin, moscatin and densiflorol B were regarded as key antitumor compounds of D. nobile and D. officinale, D. nobile and D. officinale shared 7 targets accounting for 70% of the antitumor core targets, more than half of their antitumor KEGG pathways were similar. The results of molecular docking and western blotting experiments indicated that the antitumor mechanisms of D. nobile and D. officinale may be through inhibiting PI3K-Akt and HIF-1α signaling pathways.

Structure‐Based Drug Design: Identification of Glucokinase Activators from Natural Compounds for the Treatment of Type 2 Diabetes

AbstractGlucokinase Activators (GKA) are novel small molecules that target GK and reduce plasma blood glucose by binding GK allosterically. It is considered as a potential therapeutic target to treat T2D. So, in an effort to identify compounds to treat T2D efficiently, a structure‐based virtual screening was performed on compounds from Universal Natural Product Database (UNPD) using FRED. Among 229358 compounds from UNPD, four compounds were identified as potential small molecules. The hit compounds UNPD 1354, UNPD 85595, UNPD 6604, and UNPD 88147 are found to interact strongly with active site residues ARG 63, VAL62, VAL452, TYR215, VAL 455, MET210, MET235, and TYR214. In addition, ADME predictions show that the compounds satisfy drug‐likeness criteria. Finally, the molecular dynamics simulation was carried out for 50 ns using the GROMACS 2020 package, the results confirm the stability of the enzyme‐HIT throughout the simulation time. Thus, this study may provide valuable insights into identifying novel small molecules as GKAs.

Examining the Comprehensiveness of Amended Flavored Tobacco Product Sales Restrictions.

Flavored tobacco products contribute significantly to youth tobacco initiation and tobacco use disparities. In the last decade, 362 jurisdictions have enacted policies restricting the sale of flavored tobacco products; however, many policies are not fully comprehensive due to menthol and adult-only retailer exemptions. Although several of these restrictions have been amended since their original passage, to date, little is known about how amendments have affected policy comprehensiveness. To describe how amendments to flavored tobacco product sales restrictions affect policy comprehensiveness. We identified flavored tobacco product sales restrictions that had been amended at least once using an internal database of US state and local flavored tobacco product sales restrictions. To characterize policy comprehensiveness, we applied a 6-level flavored tobacco policy classification scheme-level 6 being most comprehensive-to amended restrictions. We conducted a descriptive analysis of each initial policy and its most recent amendment to identify changes in retailer, product, and flavor inclusions, as well as overall comprehensiveness. Comprehensiveness of amended flavored tobacco product sales restrictions. As of March 31, 2022, no states and 50 localities had amended their flavored tobacco product sales restriction. Amendments largely increased policy comprehensiveness; most laws prior to amendment were categorized as level 1 (n = 28, 56.0%), while after amendment, the plurality were categorized as level 6 (n = 25, 50.0%). Most commonly, amendments removed menthol exemptions (n ≥ 30, 60.0%) and adult-only retailer exemptions (n = 12, 24.0%). Several local flavored tobacco product sales restrictions have been amended. Nearly all amendments increased policy comprehensiveness, primarily by removing exemptions for menthol products and exemptions for adult-only retailers. While policy advocates remain focused on passing comprehensive policies at initial passage, amendments have served as a tool to strengthen existing sales restrictions. This study along with ongoing flavored tobacco product sales restriction surveillance can inform policy advocacy and evaluation efforts.

Nutritional Description of Organic and Conventional Food Products in Spain: The BADALI Project.

Organic food and drink is undoubtedly a growing market. Consumers perceive organic food as healthy, and nutrition claims (NCs) and fortification may add to this perception. Whether this is true is still a matter of controversy, particularly for organic food products. We present here the first comprehensive study of large samples of six specific organic food types, analysing the nutritional quality (nutrient composition and "healthiness") as well as the use of NCs and fortification. In parallel, a comparison with conventional food is also carried out. For this purpose, the Food Database of products in the Spanish market, BADALI, was used. Four cereal-based and two dairy-substitute food types were analysed. Our results show that as many as 81% of organic foods are considered "less healthy" by the Pan American Health Organization Nutrient Profile Model (PAHO-NPM). Organic foods present a slightly improved nutrient profile compared to conventional foods. However, many of the differences, though statistically significant, are nutritionally irrelevant. Organic foods use NCs very frequently, more than conventional foods, with very little micronutrient fortification. The main conclusion of this work is that consumers' perception that organic food products are healthy is unfounded from a nutritional point of view.

A deep learning and docking simulation-based virtual screening strategy enables the rapid identification of HIF-1α pathway activators from a marine natural product database

Artificial Intelligence is hailed as a cutting-edge technology for accelerating drug discovery efforts, and our goal was to validate its potential in predicting pharmacological inhibitors of EGLN1 using a deep learning-based architecture, one of its subsidiaries. Egl nine homolog 1 (EGLN1) inhibition prevents poly ubiquitination-mediated proteosomal destruction HIF-1α. The pharmacological interventions aimed at stabilizing HIF-1α have the potential to be a promising treatment option for a range of human diseases, including ischemic stroke. To unveil a novel EGLN1 inhibitor from marine natural products, a custom-based virtual screening was carried out using a Deep Convolutional Neural Network (DCNN) architecture, docking, and molecular dynamics simulation. The custom DCNN model was optimized and further employed to screen marine natural products from the CMNPD database. The docking was performed as a secondary strategy for screened hits. Molecular dynamics (MD) and molecular mechanics/generalized Born surface area (MM-GBSA) were used to analyze inhibitor binding and identify key interactions. The findings support the claim that deep learning-based virtual screening is a rapid, reliable and accurate method of identifying highly contributing drug candidates (EGLN1 inhibitors). This study demonstrates that deep learning architecture can significantly accelerate drug discovery and development, and provides a solid foundation for using (Z)-2-ethylhex-2-enedioic acid [(Z)-2-ethylhex-2-enedioic acid] as a potential EGLN1 inhibitor for treating various health complications. Communicated by Ramaswamy H. Sarma

MAPPING OF TOURISM PRODUCTS IN PERAK TENGAH DISTRICTS USING GEOGRAPHIC INFORMATION SYSTEMS

Perak Tengah District had been excluded from being promoted as rural tourism in Perak main tourism clusters. The main problem is the tourism resources have not yet been effectively publicised and utilised and there is no official digital tourism database of tourism products in Perak Tengah District. The aim of this paper is to capture all potential tourism products in Perak Tengah and present it in the interactive map. In this paper, a software QGIS and Google Earth are used to locate and map the distribution of tourism in Perak Tengah District. According to the findings, 229 different tourism products have been located within the study area. The findings show, inadequacy of trade and service and accommodation to support tourism development. Besides that, these tourism products areas are supported by signages, roads, accessibility, parking, and facilities that are sufficient and satisfactory conditions for tourists to come. The mapping of tourism products is categorised into five including place of attraction, food and beverages, accommodation, education, trade, and services. By using coordinates made up of graphic data and qualities for each identified tourist attraction, the results of the identification of the distribution of tourism products are projected on a map.

Adaptive Rider Feedback Artificial Tree Optimization-Based Deep Neuro-Fuzzy Network for Classification of Sentiment Grade

Sentiment analysis is an efficient technique for expressing users’ opinions (neutral, negative or positive) regarding specific services or products. One of the important benefits of analyzing sentiment is in appraising the comments that users provide or service providers or services. In this work, a solution known as adaptive rider feedback artificial tree optimization-based deep neuro-fuzzy network (RFATO-based DNFN) is implemented for efficient sentiment grade classification. Here, the input is pre-processed by employing the process of stemming and stop word removal. Then, important factors, e.g. SentiWordNet-based features, such as the mean value, variance, as well as kurtosis, spam word-based features, term frequency-inverse document frequency (TF-IDF) features and emoticon-based features, are extracted. In addition, angular similarity and the decision tree model are employed for grouping the reviewed data into specific sets. Next, the deep neuro-fuzzy network (DNFN) classifier is used to classify the sentiment grade. The proposed adaptive rider feedback artificial tree optimization (A-RFATO) approach is utilized for the training of DNFN. The A-RFATO technique is a combination of the feedback artificial tree (FAT) approach and the rider optimization algorithm (ROA) with an adaptive concept. The effectiveness of the proposed A-RFATO-based DNFN model is evaluated based on such metrics as sensitivity, accuracy, specificity, and precision. The sentiment grade classification method developed achieves better sensitivity, accuracy, specificity, and precision rates when compared with existing approaches based on Large Movie Review Dataset, Datafiniti Product Database, and Amazon reviews.

Novel hits for autosomal dominated polycystic kidney disease (ADPKD) targeting derived by in silico screening on ZINC-15 natural product database

Autosomal dominant polycystic kidney disease (ADPKD) is the most common genetic kidney disorder that leads to growth cysts in the kidney, ultimately resulting in loss of function. Currently, no effective drug therapy can be safely used in the clinic. So, looking for effective therapeutic drugs is urgent for treating ADPKD. Our natural product library was prepared based on the ZINC-15 database. Lipinski’s rule of five, drug-likeness, and toxicity screening of the designed library were evaluated. Swiss model online server was used for modeling of GANAB target. Finally, docking-based screening against ADPKD targets was done by MOE 2019 software. The top 14 favorable druglike and non-toxic hits were selected for docking studies. Our results showed that compound-10 (ZINC 6073947) as a sesquiterpene coumarin had more negative binding interaction into the active site of PPARG, OXSR1, GANAB, AVPR2, and PC2 with docking scores of −8.22, −7.52, −6.98, −6.61 and −6.05 kcal/mol, respectively, in comparison to Curcumin, as a natural product that is now in phase 4 clinical trial in ADPKD disease, with an affinity of −8.03, −6.42, −6.82, −5.84 and −5.10 kcal/mol, respectively. Furthermore, seven sesquiterpene coumarins similar to compound 10 were generated and docked. Farnesiferol B (16), compared to compound-10, showed binding affinity of −8.16, −6.4, −7.46, −6.92, and −6.11 kcal/mol against the above targets, respectively. Molecular dynamics, which was done on the compound-10 and 16 (Farnesiferol B) in complex with PPARG, GANAB, and AVPR2, showed more negative binding free-energy than Pioglitazone, Miglitol, and Tolvaptan as FDA-approved drugs for each target, respectively. Communicated by Ramaswamy H. Sarma

Eksplorasi Senyawa Penghambat Enzim Salisilat Sintase dari Mycobacterium tuberculosis melalui Studi Penambatan Molekul dan Prediksi Sifat ADME

The emergence of drug-resistant Mycobacterium tuberculosis (MTB) has become a major issue in tuberculosis (TB) treatment, and needs to find and develop new efficient drugs for better TB control. This research aims to discover a potential MTB salicylate synthase inhibitor, which regulates mycobactin biosynthesis, to bind iron ions which facilitate bacteria to replicate by utilizing the molecular docking method. A total of 462 compounds were chosen from the Marine Natural Products database. The test compounds were filtered sequentially based on their molecular weight and binding affinity energy. After re-categorizing the parameters, such as predicted binding energies and molecular interactions, including hydrogen bond and hydrophobic interactions, compounds CMNPD18535 (xylogranatin H), CMNPD20622 (agallochoal O), CMNPD25719 (isoscutellarein 7-O-β-xyloside-2′′-O-sulfate), and CMNPD31560 (28-nor-olean-2a,3ß-dihydroxy-14,17-diene-16-one) were identified as hit compounds. These four compounds demonstrated the greatest ability to inhibit the salicylate synthase enzyme with binding energies of -10.33, -10.1, -10.03, and -9.76 kcal/mol, respectively, compared to RVE (native ligand), which exhibited binding energy of -8.15 kcal/mol. Overall, the hit compounds showed a relatively good safety profile and Lipinski criteria except for CMNPD20622 because it inhibits the enzymes CYP2C9 and CYP3A4, and CMNPD25719, which has excess donors and acceptors hydrogen, thus violating 2 Lipinski criteria as an oral drug. This research could be the first step toward identifying appropriate candidate compounds for further experimental investigation as an alternative to the MTB salicylate synthase inhibitors.

Cheminformatics-Based Study Identifies Potential Ebola VP40 Inhibitors.

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

Constructing a Myxobacterial Natural Product Database to Facilitate NMR-Based Metabolomics Bioprospecting of Myxobacteria.

Myxobacteria are fascinating prokaryotes featuring a potent capacity for producing a wealth of bioactive molecules with intricate chemical topology as well as intriguing enzymology, and thus it is critical to developing an efficient pipeline for bioprospecting. Herein, we construct the database MyxoDB, the first public compendium solely dedicated to myxobacteria, which enabled us to provide an overview of the structural diversity and taxonomic distribution of known myxobacterial natural products. Moreover, we demonstrated that the cutting-edge NMR-based metabolomics was effective to differentiate the biosynthetic priority of myxobacteria, whereby MyxoDB could greatly streamline the dereplication of multifarious known compounds and accordingly speed up the discovery of new compounds. This led to the rapid identification of a class of linear di-lipopeptides (archangimins) and a rare rearranged sterol (corasterol) that were endowed with unique chemical architectures and/or biosynthetic enzymology. We also showcased that NMR-based metabolomics, MyxoDB, and genomics can also work concertedly to accelerate the targeted discovery of a polyketidic compound pyxipyrrolone C. All in all, this study sets the stage for the discovery of many more novel natural products from underexplored myxobacterial resources.

Analysis of PEM and AEM electrolysis by neural network pattern recognition, association rule mining and LIME

In this work, as an extension of previous machine learning studies, three novel techniques, namely local interpretable model-agnostic explanations (LIME), neural network pattern recognition and association rule mining (ARM) were utilized for proton exchange membrane (PEM) and anion exchange membrane (AEM) electrolyzer database for hydrogen production. The main goal of LIME was to determine the positive or negative effects of a variety of descriptor variables on current density, power density and polarization. Using this technique, it was possible to uncover rules or paths that lead to high current density, low power density and low polarization. ARM provided the dominant rules leading to high current density such as using ELAT as the cathode gas diffusion layer, using pure Pt on the cathode surface and using pure carbon as the cathode support. In addition, LIME and neural network pattern recognition successfully uncovered the importance of catalytic materials such as cathode/anode support/surface elements, operational variables like K2CO3 or KOH concentration in the electrolyte, certain membrane types, gas diffusion layers, and applied potential on current density. It was then concluded that machine learning can help determine the ideal conditions for developing a PEM and AEM electrolyzer to maximize hydrogen generation, which can also guide future research.

Comparing the nutritional composition of foods and beverages in the Canadian Nutrient File to a large representative database of Canadian prepackaged foods and beverages.

Nutrient information used to code dietary intakes in the Canadian Community Health Survey (CCHS) may not be reflective of the current Canadian food supply and could result in inaccurate evaluations of nutrient exposures. To compare the nutritional compositions of foods in the CCHS 2015 Food and Ingredient Details (FID) file (n = 2,785) to a large representative Canadian database of branded food and beverage products (Food Label Information Program, FLIP) collected in 2017 (n = 20,625). Food products in the FLIP database were matched to equivalent generic foods from the FID file to create new aggregate food profiles based on FLIP nutrient data. Mann Whitney U tests were used to compare nutrient compositions between the FID and FLIP food profiles. In most food categories and nutrients there were no statistically significant differences between the FLIP and FID food profiles. Nutrients with the largest differences included: saturated fats (n = 9 of 21 categories), fiber (n = 7), cholesterol (n = 6), and total fats (n = 4). The meats and alternatives category had the most nutrients with significant differences. These results can be used to prioritize future updates and collections of food composition databases, while also providing insights for interpreting CCHS 2015 nutrient intakes.

Phytocompound inhibitors of caspase 3 as beta-cell apoptosis treatment development option: An In-silico approach

The prevalence of Diabetes mellitus (DM) is continuously rising worldwide. Among its types, type I is characterized by the destruction of beta cells triggered by various mechanisms, including the activation of Caspase 3. Studies have demonstrated the crucial role of Caspase 3 in initiating the apoptosis of beta cells in DM. Our research aims to identify possible phytocompounds inhibitors of Caspase 3 using computational approach. We obtained 3D structures of Caspase 3 and 6511 phytocompounds from the Protein Data Bank and the African Natural Products Database, respectively. The phytocompounds were assessed for druglikeness properties, topological polar surface area, and preliminary toxicity using DataWarrior. The phytocompounds were subjected to molecular docking simulation (MDS) at Caspase 3 active site using AutoDock-Vina. The frontrunner phytocompounds obtained from the MDS were subjected to protease inhibition prediction on Molinspiration. The pharmacokinetics of the phytocompounds were assessed on SwissADME. The in-depth computational toxicity profile of the phytocompounds was evaluated on the pkCSM web. The binding interactions of the phytocompounds with Caspase 3 were assessed with Discovery Studio Visualizer and Maestro. Seventeen phytocompounds were found to have no violation of Lipinski's rule and had no toxicity based on the preliminary assessment, have better binding affinity and protease inhibitory prediction scores than the references, have optimistic bioactivity radar prediction and similar amino acids interaction, in comparison with the references. Further studies, which include in-vitro and in-vivo studies, will be carried out to validate the results of this study.

Development of the Rumah Karya application in the educational technology study program as a media database for the works of lecturers and students

This research aims to produce Rumah Karya applications that provide a media product database for lecturers and students in the Educational Technology Study Program at Makassar State University. The research method used in this research is RD (Research and Development), with the ADDIE development model divided into five stages: analysis, design, development, implementation, and evaluation. The Rumah Karya application has been validated by the head of a study program as a material expert and an information technology (IT) expert. Validation from material experts obtained an average of 98% very feasible category, and validation from IT experts obtained an average of 92% very feasible category. This application has been tested by lecturers and students in the Educational Technology study program, which produces an average of 83.8% which includes a very feasible category. The Rumah Karya application has been registered as intellectual property rights at Makassar State University. The Rumah Karya application has been implemented and integrated with the Educational Technology study program website. This research has the potential to carry out further research related to additional development features in Rumah Karya that can enhance the user experience in using the application. This research can also be extended to other study programs or other educational institutions to measure the generalization of Rumah Karya's application in improving the quality of media products in educational institutions.

Computational identification of drug-like marine natural products as potential RNA polymerase inhibitors against Nipah virus

Nipah virus (NiV) has been an alarming threat to human populations in southern Asia for more than a decade. It is one of the most deadly viruses in the Mononegavirales order. Despite its high mortality rate and virulence, no chemotherapeutic agent or vaccine is publicly available. Hence, this work was conducted to computationally screen marine natural products database for drug-like potential inhibitors for the viral RNA-dependent RNA polymerase (RdRp). The structural model was subjected to molecular dynamics (MD) simulation to obtain the native ensemble of the protein. The CMNPDB dataset of marine natural products was filtered to retain only compounds following Lipinski's five rules. The molecules were energy minimized and docked into different conformers of the RdRp using AutoDock Vina. The best 35 molecules were rescored by GNINA, a deep learning-based docking software. The resulting nine compounds were evaluated for their pharmacokinetic profiles and medicinal chemistry properties. The best five compounds were subjected to MD simulation for 100 ns, followed by binding free energy estimation via Molecular Mechanics/ Generalized Born Surface Area (MM/GBSA) calculations. The results showed remarkable behavior of five hits as inferred by stable binding pose and orientation to block the exit channel of RNA synthesis products in the RdRp cavity. These hits are promising starting materials for in vitro validation and structural modifications to enhance the pharmacokinetic and medicinal chemistry properties for developing antiviral lead compounds.

Virtual screening and multilevel precision-based prioritisation of natural inhibitors targeting the ATPase domain of human DNA topoisomerase II alpha

Human DNA topoisomerase II alpha (hTopIIα) is a classic chemotherapeutic drug target. The existing hTopIIα poisons cause numerous side effects such as the development of cardiotoxicity, secondary malignancies, and multidrug resistance. The use of catalytic inhibitors targeting the ATP-binding cavity of the enzyme is considered a safer alternative due to the less deleterious mechanism of action. Hence, in this study, we carried out high throughput structure-based virtual screening of the NPASS natural product database against the ATPase domain of hTopIIα and identified the five best ligand hits. This was followed by comprehensive validation through molecular dynamics simulations, binding free energy calculation and ADMET analysis. On stringent multilevel prioritization, we identified promising natural product catalytic inhibitors that showed high binding affinity and stability within the ligand-binding cavity and may serve as ideal hits for anticancer drug development. Communicated by Ramaswamy H. Sarma

Machine Learning Models Using Data Mining for Biomass Production from Yarrowia lipolytica Fermentation

In this paper, a database of biomass production from Yarrowia lipolytica fermentation is prepared and constructed using machine learning and data mining approaches. The database is curated from 15 publications and consists of 301 rows of data with 25 predictors and 1 label. The predictors include inoculum size, temperature, pH, and time, while the label is the corresponding biomass production. The database is then divided into training, validation, and test datasets and analyzed as a supervised machine learning task for regression. Twenty-six regression models are employed and compared for their performance in predicting biomass production. The best-performing model is the Matern 5/2 Gaussian process regression model, which has the lowest root-mean-squared error of 0.75 g/L, the highest R squared of 0.90, and the lowest mean absolute error of 0.52 g/L. The t-test is used to identify the most important predictors, and 14 predictors are sufficient for creating an accurate model. These 14 predictors are fermentation time, peptone, temperature, total Kjeldahl nitrogen, shaking rate, total nitrogen, inoculum size, yeast extract, crude glycerol, glucose, oil and grease, media pH, ammonium sulfate, and olive oil. This research demonstrates the application of machine learning and data mining to estimate biomass production and gives insight into which parameters are essential for Yarrowia lipolytica fermentation.