Product Database Research Articles

Identification of potential MMP-8 inhibitors through virtual screening of natural product databases.

Matrix metalloproteinase-8 (MMP-8), a type II collagenase, is a key enzyme in the degradation of collagens and is implicated in various pathological processes, making it a promising target for drug discovery. Despite advancements in the development of MMP-8 inhibitors, concerns over potential adverse effects persist. This study aims to address these concerns by focusing on the development of novel compounds with improved safety profiles while maintaining efficacy. In this study, we employed a computational approach to screen potent and safe inhibitors of MMP-8 from the Natural Product Activity and Species Source Database (NPASS). Initially, we constructed a pharmacophore model based on the crystal structure of the MMP-8-FIN complex (PDB ID: 4EY6) utilizing the Pharmit tool. This model then guided the selection of 44 promising molecules from NPASS, setting the stage for further analysis and evaluation. We comprehensively evaluated their drug-likeness and toxicity profiles. Molecules 21, 4, and 44 were identified as potentially effective MMP-8 inhibitors through a robust pipeline that included ADMET profiling, molecular docking, and molecular dynamics simulations. Notably, molecule 21 stood out for its low toxicity, high binding stability, and favorable ADMET profile, while molecule 44 demonstrated excellent affinity. These compounds offer structural novelty compared to known MMP-8 inhibitors. These computational results can be combined with in vitro experiments in the future to validate their activity and safety. These findings provide an important reference for drug design of MMP-8 inhibitors.

VIRTUAL SCREENING OF THE ZIMBABWE NATURAL PRODUCT DATABASE FOR GLUCOKINASE ACTIVATORS

Objective: This study aimed to identify potential glucokinase activators within Zimbabwean natural products using virtual screening techniques. Methods: Twenty-one compounds filtered from ChEMBL ID 3820 (pEC50 ≥ 8) were used to generate a pharmacophore model, validated with DUD-E data. The model screened the 6220 compounds in the Zimbabwe Natural Products Database (ZiNaPoD) using LigandScout. Hit compounds were docked with glucokinase (protein ID 4NO7) using AutoDock Vina and AutoDock 4 in PyRx, followed by adsorption, distribution, metabolism, and excretion (ADME) screening by SwissADME. Molecular dynamics simulations were conducted on the resulting complexes using the CHARMM36m force field on GROMACS. Results: The validated pharmacophore model (80% accuracy, 95% sensitivity, 80% specificity) produced 149 hits, 16 of which had binding energies ≤ −8 kcal/mol after the two rounds of molecular docking. The ADME analysis narrowed the selection to four compounds, with binding energies ranging from −8.35 to −9.82 kcal/mol. All four demonstrated stability in molecular dynamic simulations, with average root mean square deviation (RMSD) values ranging from 1.491 to 3.835 Å. The Sphenostylisin I and Dihydroxymethyl dihydroxybenzyl chromanone (DMDBC) complexes exhibited the highest stability with average RMSD values of 1.491±2.794 Å and 2.875±1.452 Å, respectively. They also exhibited low-binding free energies of −30.30±0.38 and −30.20±0.49 kcal/mol, making them promising targets. Conclusion: Four potential glucokinase activators were identified, with Sphenostylisin I and DMDBC showing promise as candidates for developing new diabetes treatments due to their stability, favorable binding, and absence of liver-toxic groups.

Defining "High-In" Saturated Fat, Sugar, and Sodium to Help Inform Front-of-Pack Labeling Efforts for Packaged Foods and Beverages in the United States.

Background: To help consumers make healthier choices, the US Food and Drug Administration (FDA) has been charged with developing a front-of-package label (FOPL) to appear on US packaged foods and beverages. One option being explored is the use of "high-in" FOPLs for added sugar, sodium, and saturated fat using a threshold of ≥20% of the recommended daily value (%DV) per portion/serving size to define "high-in". While research has addressed what FOPL designs are most effective at visually communicating "high-in", less attention has been paid to the nutrient profile model (NPM) used to decide which products should receive these labels. In addition, several established regional NPMs already exist that identify products that are high in nutrients of concern, but it is unclear how these compare to the FDA's %DV approach. Methods: We used a dataset of 51,809 US products from Mintel's Global New Products Database to examine how the FDA's current definition of "high-in" compares to three established regional NPMs: the Canadian NPM, the Pan American Health Organization (PAHO) NPM, and Chile's NPM. Results: Overall agreement between the four NPMs was 51% for foods and 72% for beverages, with highest agreement in categories such as sweetened sodas (87%), and lowest agreement in categories such as bread (14%) and salty snacks (29%). The Canadian NPM showed the highest agreement to the FDA "high-in" criteria while the Chilean and PAHO models had lower agreement. For many food categories, the FDA's definition of "high-in" would require the fewest products to carry a "high-in" label. This issue was particularly pronounced in categories that tend to be served in small portions (e.g., salty snacks, bars), but disappeared or reversed for categories that are served in larger portions (e.g., frozen and non-frozen main dishes). Conclusions: The NPM chosen has important policy implications for an FOPL system's ability to identify unhealthy foods and incentivize companies to reformulate products. Based on these results, the FDA should consider using a stronger NPM similar to those used elsewhere in the Americas region when deciding the final thresholds for "high-in" for US packaged foods and beverages.

Discovery of Non-Peptide GLP-1 Positive Allosteric Modulators from Natural Products: Virtual Screening, Molecular Dynamics, ADMET Profiling, Repurposing, and Chemical Scaffolds Identification.

Background/Objectives: Glucagon-like peptide-1 (GLP-1) receptor is currently one of the most explored targets exploited for the management of diabetes and obesity, with many aspects of its mechanisms behind cardiovascular protection yet to be fully elucidated. Research dedicated towards the development of oral GLP-1 therapy and non-peptide ligands with broader clinical applications is crucial towards unveiling the full therapeutic capacity of this potent class of medicines. Methods: This study describes the virtual screening of a natural product database consisting of 695,133 compounds for positive GLP-1 allosteric modulation. The database, obtained from the Coconut website, was filtered according to a set of physicochemical descriptors, then was shape screened against the crystal ligand conformation. This filtered database consisting of 26,325 compounds was used for virtual screening against the GLP-1 allosteric site. Results: The results identified ten best hits with the XP score ranging from -9.6 to -7.6 and MM-GBSA scores ranging from -50.8 to -32.4 and another 58 hits from docked pose filter and a second round of XP docking and MM-GBSA calculation followed by molecular dynamics. The analysis of results identified hits from various natural products (NPs) classes, to whom attributed antidiabetic and anti-obesity effects have been previously reported. The results also pointed to β-lactam antibiotics that may be evaluated in drug repurposing studies for off-target effects. The calculated ADMET properties for those hits revealed suitable profiles for further development in terms of bioavailability and toxicity. Conclusions: The current study identified several NPs as potential GLP-1 positive allosteric modulators and revealed common structural scaffolds including peptidomimetics, lactams, coumarins, and sulfonamides with peptidomimetics being the most prominent especially in indole and coumarin cores.

Vibration characteristics analysis of multi-layer composite cylindrical shell casing with different layer thickness ratios under external pressure using energy method

PurposeAero-engine casings commonly use composite cylindrical shell structures with excellent properties such as corrosion resistance and fatigue resistance. Still, their vibration behavior is relatively complex and may cause fatigue vibration damage, so it is essential to analyze the vibration characteristics of composite cylindrical shells. The purpose of this paper is to analyze the vibration characteristics of multilayer composite cylindrical shells subjected to external pressures and having different interlayer thickness ratios and provide some theoretical basis for the fatigue damage prediction of cylindrical shell casing to ensure the safety and stability of the engine during flight.Design/methodology/approachFirstly, the vibration differential equation with external pressure is established based on Soedel theory considering nonlinear effects, while four symmetric boundary conditions are chosen to constrain the cylindrical shell. Then the Rayleigh–Ritz method, which is more efficient and accurate in calculating large structural systems, is applied to solve the problem, and the theoretical model of three-layer cylindrical shell under external pressure is established. The accuracy of the model is verified by comparing the data with the specialized literature. Subsequently, the effects of different external pressures and different thickness-to-diameter ratios, different length-to-diameter ratios and different interlayer thickness percentages on the natural frequency of multilayer composite cylindrical shells were investigated by control variable analysis.FindingsThe conclusions obtained show that the external pressure increases the natural frequency of the cylindrical shell and that the frequency characteristics of the cylindrical shell vary for different boundary conditions. The effect of length-to-diameter ratio, thickness-to-diameter ratio and the percentage of the thickness of the intermediate layer on the natural frequency of the cylindrical shell are significantly increased under external pressure. Because the presence of external pressure increases the frequency of the cylindrical shell by about 70%, it has almost no effect on the frequency at the minimum number of circumferential waves, and the effect on the frequency at the maximum number of circumferential waves is reduced to about 50%. The frequencies in the SL-SL boundary condition are all in perfect agreement with the S-S boundary condition under the influence of different influencing factors.Originality/valueIn this paper, the effect of external pressure and the natural properties of the cylindrical shell under external pressure on the cylindrical shell’s frequency is considered, emphasizing the effect of different layer thickness ratios on the frequency. This paper aims to summarize the changing law between the natural frequency of the cylindrical shell itself and different design parameters during the flight pressure process. Reliable theoretical predictions are provided for analyzing the vibrational behavior of shells subjected to external pressures in aerospace, as well as a database for the practical production of cylindrical shells.

Exploring Marine Compounds for Inhibition of the BRAF V600E Mutation in Lung Cancer: A High Throughput Virtual Screening Approach

The BRAF V600E mutation is present in a variety of cancer cell types, including lung cancer, which is a leading cause of cancer-related death globally. Drugs like Dabrafenib and Vermaifenib were specifically used in targeted therapy for patients with BRAF V600E mutation. Drug resistance is typical, though, and more study is required to improve therapeutic strategies for this subset of lung cancer. Recently, marine natural compounds have emerged as a resource for the development of novel cancer therapeutics. In this study, we used virtual screening of marine compounds to find potential therapeutic candidates for NSCLC patients with the BRAF V600E mutation. We developed a receptor-based pharmacophore model and screened a library of 31,561 compounds from the Comprehensive Marine Natural Products Database (CMNPD) using a virtual screening protocol that was constructed and validated. Molecular docking was performed using CDOCKER to investigate potential inhibitors and ADMET for their toxicity and other properties. Seven compounds exhibited higher efficacy than the experimentally verified compound of BRAF mutation. A molecular dynamics simulation was performed to confirm the stability of the docked compounds and compare them with the experimentally verified compound. Our study identified, out of seven, the CMNPD7700 compound as a potential drug candidate for further development due to its high, effectiveness and molecular stability.

The impact of Gender Diversity on Innovative Performance: Empirical Analysis in the Caribbean Region

This paper investigates the impact of gender diversity on the innovative performance of Caribbean firms, specifically analyzing women’s representation across three organizational areas: the overall workforce, management, and skilled production and non-production roles. The study used data from the Innovation, Firm Productivity, and Gender (IFPG) database, encompassing 1,979 firms across 13 Caribbean countries from 2017 to 2020. Probit models were employed for econometric analysis. The results confirm that diversity significantly enhances the likelihood of innovation, though the magnitude of its impact varies depending on women’s roles within firms. Gender diversity has stronger effects within the overall workforce and skilled production and non-production roles than within management teams. These findings suggest important managerial and policy implications for developing measures to reduce gender disparities in innovation.

Quantifying EDC Emissions from Consumer Products: A Novel Rapid Method and Its Application for Systematic Evaluation of Health Impacts.

Endocrine-disrupting chemicals (EDCs) are widely used in consumer products and have been associated with adverse public health outcomes and significant economic costs. We developed a rapid chamber method for measuring EDC emissions from consumer products, significantly reducing the time to reach steady state from weeks or months to minutes or hours. Using this method, we quantified EDC emissions from a wide range of products, determined the emission-control parameters, and established their relationship with the EDC content (Wf) and physicochemical properties. By incorporating Wf data from consumer product databases and applying stochastic models, we systematically estimated emissions for 400 EDC-product combinations and assessed the associated exposure and disease burden for the U.S. population. Our results suggest that more than 60% of these combinations could result in carcinogenic disability-adjusted life years (DALYs) above the acceptable threshold. The overall disease burden caused by EDCs in consumer products can be substantial, with DALYs exceeding those associated with other pollutants, such as particulate matter, in a worst-case scenario. This study provides a valuable tool for prioritizing hazardous EDCs in consumer products, evaluating safer alternatives, and formulating effective intervention strategies, thereby supporting policymakers and manufacturers in making informed, sustainable decisions.

Omega-3 Levels in Prenatal Supplements.

This study aimed to systematically document the reported omega-3 levels in commercially available prenatal supplements in the United States and Canada, and compare these levels to recommended intakes in pregnancy. Commercial prenatal supplements were identified using the Dietary Supplement Label Database (n = 50) and the Licensed Natural Health Products Database (n = 18). Mean omega-3 levels and the proportion of products meeting recommendations for pregnant women were tabulated. Average omega-3 levels were 368 ± 33 mg in the United States and 404 ± 72 mg in Canada. Seventy percent of products in the United States and 61% in Canada contained the amount of omega-3 levels recommended for pregnant women with sufficient intakes. Sixteen percent of products in the United States and 28% in Canada contained the dose recommended for pregnant women with insufficient intakes. These results illustrate that commercial prenatal supplements contain a wide variety of omega-3 fatty acid levels that may or may not be consistent with recommended intake levels. · most pregnant women consume inadequate amounts of omega-3 fatty acids.. · omega-3 fatty acids can help prevent preterm birth.. · new guidelines recommend increased intakes of omega-3 fatty acids during pregnancy.. · prenatal supplements contain a wide variety of omega-3 fatty acid levels.. · omega-3 levels in prenatal supplements may or may not be consistent with recommended intake levels..

Novel Hits for Targeting Kidney Failure in Type 2 Diabetes derived via In Silico Screening of the ZINC Natural Product Database

Novel Hits for Targeting Kidney Failure in Type 2 Diabetes derived via In Silico Screening of the ZINC Natural Product Database

A Deep Dive into PDE5 Inhibition: Innovative Discoveries via Virtual Screening

Background: PDE5 inhibitors have had a surge in popularity over the last decade owing to their efficacy in the treatment of erectile dysfunction, coronary vasculopathy, and pulmonary arterial hypertension. These inhibitors exhibit competitive binding with phosphodiesterase type 5 and inhibit the hydrolysis of cyclic guanosine monophosphate, hence elevating the levels of cGMP in smooth muscle cells and prolonging the duration of an erection. However, due to production costs and side effects, further research is needed to discover new PDE5 inhibitors. Objective: The study aimed to identify potent PDE5 inhibitors by employing the extensive application of computer-aided drug design. Methods: Three different databases, named Million Molecules Database, Natural Product Database, and NCI Database, have been screened, which has been followed by filtering based on various druglikeness rules, docking, ADME, toxicity, consensus molecular docking, and 100 ns molecular dynamics simulation. Results: Three compounds (ZINC05351336, ZINC12030898, and ZINC17949426) have exhibited stable-binding characteristics at the active site of PDE5, demonstrating a robust binding affinity. These molecules have been found to possess drug-like capabilities, effective ADME features, low toxicity, and high stability. Conclusion: The study has delved into the realm of PDE5 inhibitors, which have been found to be effective in treating erectile dysfunction, but high production costs and side effects necessitate new ones. Through computer-aided drug design and screening, three compounds have been identified with promising binding characteristics, drug-appropriate properties, effective ADME profiles, minimal toxicity, and stability, making them potential candidates for future PDE5 inhibitors

Development of an Antiviral Medicinal Plant and Natural Product Database (avMpNp Database) from Biodiversity.

The construction of compound databases (DB) is a strategy for the rational search of bioactive compounds and drugs for new and old diseases. In order to bring greater impact to drug discovery, we propose the development of a DB of bioactive antiviral compounds. Several research groups have presented evidence of the antiviral activity of medicinal plants and compounds isolated from these plants. We believe that compiling these discoveries in a DB would benefit the scientific research community and increase the speed to discover new potential drugs and medicines. Thus, we present the Antiviral Medicinal Plant and Natural Product DB (avMpNp DB) as an important source for acquiring, organizing, and distributing knowledge related to natural products and antiviral drug discovery. The avMpNp DB contains a series of chemically diverse compounds with drug-like profiles. To test the potential of this DB, SARS-CoV-2 Mpro and PLpro enzymatic inhibition assays were performed for available compounds resulting in IC50 values ranging from 6.308±0.296 to 15.795±0.155 μM. As a perspective, artificial intelligence tools will be added to implement computational predictions, as well as other chemical functionalities that allow data validation.

Integrating natural product research laboratory with artificial intelligence: Advancements and breakthroughs in traditional medicine.

The Natural Product Research Laboratory (NPRL) of China Medical University Hospital (CMUH) was established in collaboration with CMUH and Professor Kuo-Hsiung Lee from the University of North Carolina at Chapel Hill. The laboratory collection features over 6000 natural products worldwide, including pure compounds and semi-synthetic derivatives. This is the most comprehensive and fully operational natural product database in Taiwan. This review article explores the history and development of the NPRL of CMUH. We then provide an overview of the recent applications and impact of artificial intelligence (AI) in new drug discovery. Finally, we examine advanced powerful AI-tools and related software to explain how these resources can be utilized in research on large-scale drug data libraries. This article presents a drug research and development (R&D) platform that combines AI with the NPRL. We believe that this approach will reduce resource wastage and enhance the research capabilities of Taiwan's academic and industrial sectors in biotechnology and pharmaceuticals.

Natural Compounds Targeting Thymic Stromal Lymphopoietin (TSLP): A Promising Therapeutic Strategy for Atopic Dermatitis.

Atopic dermatitis (AD) is a chronic inflammatory skin disease with rising prevalence, marked by eczematous lesions, itching, and a weakened skin barrier often tied to filaggrin gene mutations. This breakdown allows allergen and microbe entry, with thymic stromal lymphopoietin (TSLP) playing a crucial role by activating immune pathways that amplify the allergic response. TSLP's central role in AD pathogenesis makes it a promising therapeutic target. Consequently, in this study, we used the virtual drug screening, molecular dynamics simulation, and binding free energies calculation approaches to explore the African Natural Product Database against the TSLP protein. The molecular screening identified four compounds with high docking scores, namely SA_0090 (-7.37), EA_0131 (-7.10), NA_0018 (-7.03), and WA_0006 (-6.99 kcal/mol). Furthermore, the KD analysis showed a strong binding affinity of these compounds with TSLP, with values of -5.36, -5.36, -5.34, and -5.32 kcal/mol, respectively. Moreover, the strong binding affinity of these compounds was further validated by molecular dynamic simulation analysis, which revealed that the WA_0006-TSLP is the most stable complex with the lowest average RMSD. However, the total binding free energies were -40.5602, -41.0967, -27.3293, and -51.3496 kcal/mol, respectively, showing the strong interaction between the selected compounds and TSLP. Likewise, these compounds showed excellent pharmacokinetics characteristics. In conclusion, this integrative approach provides a foundation for the development of safe and effective treatments for AD, potentially offering relief to millions of patients worldwide.

Molecular exploration of natural and synthetic compounds databases for promising hypoxia inducible factor (HIF) Prolyl-4- hydroxylase domain (PHD) inhibitors using molecular simulation and free energy calculations

Hypoxia-inducible factors (HIFs) are transcription factors that regulate erythropoietin (EPO) synthesis and red blood cell (RBC) production. Prolyl-4-hydroxylase domain (PHD) enzymes are key regulators of HIF’s stability and activity. Inhibiting PHD enzymes can enhance HIF-mediated responses and have therapeutic potential for diseases such as anemia, cancer, stroke, ischemia, neurodegeneration, and inflammation. In this study, we searched for novel PHD inhibitors from four databases of natural products and synthetic compounds: AfroDb Natural Products, AnalytiCon Discovery Natural Product (NP), HIM-Herbal Ingredients In-Vivo Metabolism, and Herbal Ingredients’ Targets, with a total number of 13,597 compounds. We screened the candidate compounds by molecular docking and validated them by molecular dynamics simulations and free energy calculations. We identified four target hits (ZINC36378940, ZINC2005305, ZINC31164438, and ZINC67910437) that showed stronger binding affinity to PHD2 compared to the positive control, Vadadustat (AKB-6548), with docking scores of − 13.34 kcal/mol, − 12.76 kcal/mol, − 11.96 kcal/mol, − 11.41 kcal/mol, and − 9.04 kcal/mol, respectively. The target ligands chelated the active site iron and interacted with key residues (Arg 383, Tyr329, Tyr303) of PHD2, in a similar manner as Vadadustat. Moreover, the dynamic stability-based assessment revealed that they also exhibited stable dynamics and compact trajectories. Then the total binding free energy was calculated for each complex which revealed that the control has a TBE of − 31.26 ± 0.30 kcal/mol, ZINC36378940 reported a TBE of − 38.65 ± 0.51 kcal/mol, for the ZINC31164438 the TBE was − 26.16 ± 0.30 kcal/mol while the ZINC2005305 complex reported electrostatic energy of − 32.75 ± 0.58 kcal/mol. This shows that ZINC36378940 is the best hit than the other and therefore further investigation should be performed for the clinical usage. Our results suggest that these target hits are promising candidates that reserve further in vitro and in vivo validations as potential PHD inhibitors for the treatment of renal anemia, cancer, stroke, ischemia, neurodegeneration, and inflammation.

COCONUT 2.0: a comprehensive overhaul and curation of the collection of open natural products database.

The COCONUT (COlleCtion of Open Natural prodUcTs) database was launched in 2021 as an aggregation of openly available natural product datasets and has been one of the biggest open natural product databases since. Apart from the chemical structures of natural products, COCONUT contains information about names and synonyms, species and organism parts in which the natural product has been found, geographic information about where the respective sample has been collected and literature references, where available. COCONUT is openly accessible at https://coconut.naturalproducts.net. Users can search textual information and perform structure, substructure, and similarity searches. The data in COCONUT areavailable for bulk download as SDF, CSV and a database dump. The web application for accessing the data is open-source. Here, we describe COCONUT 2.0, for which the web application has been completely rewritten, and the data have been newly assembled and extensively curated. New features include data submissions by users and community curation facilitated in various ways.

Pharmacophore-based virtual screening, molecular docking, and molecular dynamics investigation for the identification of novel, marine aromatase inhibitors.

Breast cancer remains a leading cause of mortality among women worldwide. Our current research focuses on identifying effective therapeutic agents by targeting the human aromatase enzyme. Aromatase inhibitors (AIs) have been effective in treating postmenopausal breast cancer but face challenges such as drug resistance and long-term side effects like cognitive decline and osteoporosis. Natural products, especially from marine organisms, are emerging as potential sources for new drug candidates due to their structural diversity and pharmacological properties. This study aims to discover marine natural products capable of inhibiting human aromatase by combining ligand-based and structure-based pharmacophore models for virtual screening against the Comprehensive Marine Natural Products Database. From the initial virtual screening of more than 31,000 compounds, 1,385 marine natural products were identified as possible candidates. Following initial molecular docking analysis, only four compounds managed to pass the criteria this research has introduced to confirm strong binding affinity to aromatase. All four compounds yielded acceptable binding affinities, with CMPND 27987 having the highest -10.1kcal/mol. All four hits were subjected to molecular dynamics, and CMPND 27987 was further confirmed to be the most stable at the protein's active site, with an MM-GBSA free binding energy of -27.75kcal/mol. Our in silico studies indicate that CMPND 27987 interacts effectively within the binding site of the human aromatase, maintaining high affinity and stability. Based on these findings, we propose that CMPND 27987 could hold significant potential for further lead optimization and drug development.

Identification of Potential Inhibitors of TGFβR1 for the Treatment of Cancer through Structure-Based Virtual Screening and Molecular Dynamics Simulations.

Globally, cancer is one of the leading causes of death. Resistance to conventional medications, such as chemotherapy and radiation, continues to be a significant challenge in the treatment of cancer despite the availability of numerous medicines. Therefore, the highest priority is to hunt for new therapeutic agents. Transforming growth factor-beta is a pivotal regulatory cytokine that exerts significant influence over cellular processes, particularly emphasizing its role in facilitating and modulating cell proliferation. TGF-β receptor 1, identified as the most promising active site of the TGF-β signaling, is a potent drug target site that has garnered wide attention for developing new anticancer agents. The present investigation investigates the potential natural products as TGFβR1 inhibitors. The SB431542 complexed TGFβR1 protein model was used to screen the natural product database to obtain a compound with high binding potential. NPC247629 has emerged as the best-scored compound among all the screened compounds, demonstrating the highest affinity towards the TGFβR1 regarding docking score -17.54 kcal/mol. The all-atoms MD simulation study indicated that all proposed hits are retained inside the receptor in dynamic states. Additionally, principal component and free energy landscape analysis were performed to explore the binding mechanism of top-hit natural products. The best-screened hits, NPC247629 and NPC60735, have excellent binding affinity and hold a massive potential for TGFβR1 inhibition, paving the way for promising future investigations in cancer treatment.

Application of Machine Learning to Predict the Capacity of Fractured Horizontal Wells in Shale Reservoirs

Shale oil wells typically have numerous volume fracturing segments in their horizontal sections, resulting in significant variability in productivity across these segments. Conventional productivity prediction and fracturing effect evaluation methods are challenging to apply effectively. Establishing a stable and efficient intelligent productivity prediction method using machine learning is a promising approach for the effective development of shale oil reservoirs. This study is based on geological data, fracturing records, and a production database of 91 production wells in a shale oil reservoir in a specific area. Fourteen key parameters affecting productivity were selected from geological and engineering perspectives, and the recursive feature elimination method based on support vector machines identified five optimal main controlling factors. Three machine learning methods—decision tree, random forest, and gradient boosting decision tree (GBDT)—were used to model productivity prediction, with root mean square error (RMSE) employed to evaluate model performance. The study results indicate that formation coefficient, cluster spacing, treatment volume, sand volume, and fracturing segment length are the main controlling factors influencing productivity in fractured horizontal wells. Among the models, the random forest algorithm with bootstrap sampling produced the most stable prediction results, achieving a prediction accuracy of 94% and an RMSE of 0.934 on the test set, outperforming the decision tree and GBDT models in terms of minimum RMSE on the test set.

Latin American Natural Product Database (LANaPDB): An Update.

Natural product (NP) databases are crucial tools in computer-aided drug design (CADD). Over the past decade, there has been a worldwide effort to assemble information regarding natural products (NPs) isolated and characterized in certain geographical regions. In 2023, it was published LANaPDB, and to our knowledge, this is the first attempt to gather and standardize all the NP databases of Latin America. Herein, we present and analyze in detail the contents of an updated version of LANaPDB, which includes 619 newly added compounds from Colombia, Costa Rica, and Mexico. The present version of LANaPDB has a total of 13 578 compounds, coming from ten databases of seven Latin American countries. A chemoinformatic characterization of LANaPDB was carried out, which includes the structural classification of the compounds, calculation of six physicochemical properties of pharmaceutical interest, and visualization of the chemical space by employing and comparing two different fingerprints (MACCS keys (166-bit) and Morgan2 (2048-bit)). Furthermore, additional analyses were made, and valuable information not included in the first version of LANaPDB was added, which includes structural diversity, molecular complexity, synthetic feasibility, commercial availability, and reported and predicted biological activity. In addition, the LANaPDB compounds were cross-referenced to two of the largest public chemical compound databases annotated with biological activity: ChEMBL and PubChem.