Discovery Process Research Articles

The Transformative Role of Microsoft Azure AI in Healthcare

This paper explores the transformative impact of Azure AI technologies on healthcare, focusing on diagnostics, predictive analytics, operational efficiency, and drug discovery. Azure AI tools are reshaping healthcare delivery by harnessing data-driven insights and advanced machine learning algorithms to enhance patient outcomes significantly. Azure AI enables more precise diagnostics through advanced image recognition and pattern analysis, improving the speed and accuracy of medical assessments. It supports predictive analytics models that personalize treatment plans based on individual patient data, thereby optimizing care and reducing risks. In hospital settings, Azure AI enhances operational efficiencies by predicting maintenance needs and optimizing resource allocation, streamlining workflows and improving overall service delivery. Furthermore, Azure AI accelerates drug discovery processes by analyzing extensive datasets to identify potential drug candidates swiftly. These advancements not only improve the efficiency and effectiveness of healthcare services but also hold promise for addressing healthcare disparities and promoting equitable access to quality care globally.

Exploring the interactions of JAK inhibitor and S1P receptor modulator drugs with the human gut microbiome: Implications for colonic drug delivery and inflammatory bowel disease

The gut microbiota is a complex ecosystem, home to hundreds of bacterial species and a vast repository of enzymes capable of metabolising a wide range of pharmaceuticals. Several drugs have been shown to affect negatively the composition and function of the gut microbial ecosystem. Janus Kinase (JAK) inhibitors and Sphingosine-1-phosphate (S1P) receptor modulators are drugs recently approved for inflammatory bowel disease through an immediate release formulation and would potentially benefit from colonic targeted delivery to enhance the local drug concentration at the diseased site. However, their impact on the human gut microbiota and susceptibility to bacterial metabolism remain unexplored. With the use of calorimetric, optical density measurements, and metagenomics next-generation sequencing, we show that JAK inhibitors (tofacitinib citrate, baricitinib, filgotinib) have a minor impact on the composition of the human gut microbiota, while ozanimod exerts a significant antimicrobial effect, leading to a prevalence of the Enterococcus genus and a markedly different metabolic landscape when compared to the untreated microbiota. Moreover, ozanimod, unlike the JAK inhibitors, is the only drug subject to enzymatic degradation by the human gut microbiota sourced from six healthy donors. Overall, given the crucial role of the gut microbiome in health, screening assays to investigate the interaction of drugs with the microbiota should be encouraged for the pharmaceutical industry as a standard in the drug discovery and development process.

From Gut Microbiomes to Infectious Pathogens: Neurological Disease Game Changers.

Gut microbiota and infectious diseases affect neurological disorders, brain development, and function. Compounds generated in the gastrointestinal system by gut microbiota and infectious pathogens may mediate gut-brain interactions, which may circulate throughout the body and spread to numerous organs, including the brain. Studies shown that gut bacteria and disease-causing organisms may pass molecular signals to the brain, affecting neurological function, neurodevelopment, and neurodegenerative diseases. This article discusses microorganism-producing metabolites with neuromodulator activity, signaling routes from microbial flora to the brain, and the potential direct effects of gut bacteria and infectious pathogens on brain cells. The review also considered the neurological aspects of infectious diseases. The infectious diseases affecting neurological functions and the disease modifications have been discussed thoroughly. Recent discoveries and unique insights in this perspective need further validation. Research on the complex molecular interactions between gut bacteria, infectious pathogens, and the CNS provides valuable insights into the pathogenesis of neurodegenerative, behavioral, and psychiatric illnesses. This study may provide insights into advanced drug discovery processes for neurological disorders by considering the influence of microbial communities inside the human body.

Are lessons being learnt from the replication crisis or will the revolution devour its children? Open Q science from the editor's perspective

Abstract The scientific production system is crucial in how global challenges are addressed. However, scholars have recently begun to voice concerns about structural inefficiencies within the system, as highlighted, for example, by the replication crisis, the p-value debate and various forms of publication bias. Most suggested remedies tend to address only partial aspects of the system's inefficiencies, but there is currently no unifying agenda in favour of an overall transformation of the system. Based on a critical review of the current scientific system and an exploratory pilot study about the state of student training, we argue that a unifying agenda is urgently needed, particularly given the emergence of artificial intelligence (AI) as a tool in scientific writing and the research discovery process. Without appropriate responses from academia, this trend may even compound current issues around credibility due to limited replicability and ritual-based statistical practice while amplifying all forms of existing biases. Naïve openness in the science system alone is unlikely to lead to major improvements. We contribute to the debate and call for a system reform by identifying key elements in the definition of transformation pathways towards open, democratic and conscious learning, teaching, reviewing and publishing supported by openly maintained AI tools. Roles and incentives within the review process will have to adapt and be strengthened in relation to those that apply to authors. Scientists will have to write less, learn differently and review more in the future, but need to be trained better in and for AI even today.

Impact of Artificial Intelligence in Drug Discovery and Development

The field of drug discovery and development has been revolutionized by the integration of artificial intelligence (AI) technologies. AI has significantly impacted various stages of the drug development process, including target identification, lead optimization, pharmacokinetics, and toxicity prediction. This review paper provides an overview of the impact of AI in drug discovery and development, highlighting the advancements, challenges, and future prospects. It discusses the application of machine learning, deep learning, and other AI techniques in accelerating the drug discovery process, improving the efficiency of clinical trials, and reducing the overall cost of drug development. Additionally, this review examines the ethical and regulatory considerations associated with the use of AI in drug development. Overall, this paper emphasizes the transformative potential of AI in revolutionizing the pharmaceutical industry and improving patient outcomes

A novel Q-learning-based secure routing scheme with a robust defensive system against wormhole attacks in flying ad hoc networks

Nowadays, unmanned aerial vehicles (UAVs) organized in a flying ad hoc network (FANET) can successfully carry out complex missions. Due to the limitations of these networks, including the lack of infrastructure, wireless communication channels, dynamic topology, and unreliable communication between UAVs, cyberattacks, especially wormholes, weaken the performance of routing schemes. Therefore, maintaining communication security and guaranteeing the quality of service (QoS) are very challenging. In this paper, a novel Q-learning-based secure routing scheme (QSR) is presented for FANETs. QSR seeks to provide a robust defensive system against wormhole attacks, especially wormhole through encapsulation and wormhole through packet relay. QSR includes a secure neighbor discovery process and a Q-learning-based secure routing process. Firstly, each UAV gets information about its neighboring UAVs securely. To secure communication in this process, a local monitoring system is designed to counteract the wormhole attack through packet relay. This system checks data packets exchanged between neighboring UAVs and defines three rules according to the behavior of wormholes. In the second process, UAVs perform a distributed Q-learning-based routing process to counteract the wormhole attack through encapsulation. To reward the safest paths, a reward function is introduced based on five factors, the average one-hop delay, hop count, data loss ratio, packet transmission frequency (PTF), and packet reception frequency (PRF). Finally, the NS2 simulator is applied for implementing QSR and executing different scenarios. The evaluation results show that QSR works better than TOPCM, MNRiRIP, and MNDA in terms of accuracy, malicious node detection rate, data delivery ratio, and data loss ratio. However, it has more delay than TOPCM.

Aptasensors and Advancement in Molecular Recognition Technology

AbstractSynthetic oligonucleic acids known as aptamers exhibit remarkable selectivity and affinity for target recognition and binding. Selected via an iterative process known as “selective evolution of ligands by exponential enrichment” (SELEX), aptamers fold into defined 3D conformations to interact with their targets. The incorporation of aptamers as recognition elements has driven notable progress in biosensors, giving rise to the development of aptasensors. Here, the process of aptamer discovery and the development of various types of aptasensors are summarized. The fundamental design principles of aptasensors are elaborated along with the superiority of aptamers compared to antibodies. The various modes employed by aptasensors, such as structure‐switching design, hybridization chain reaction amplification, enzyme‐assisted recycling, and split aptamer design are examined. Further light is shed on the diverse landscape of aptasensors, their adaptability to different analytes aptasensors as well as their potential to propel advancements in modern biosensor technology. As a nucleic acids‐based biosensor platform, aptasensors poise to become a next generation of sensitive and cost‐effective technology to shape the future of molecular recognition in biosensing.

Enriching Earth observation datasets through semantics for climate change applications: The EIFFEL ontology

Background Earth Observation (EO) datasets have become vital for decision support applications, particularly from open satellite portals that provide extensive historical datasets. These datasets can be integrated with in-situ data to power artificial intelligence mechanisms for accurate forecasting and trend analysis. However, researchers and data scientists face challenges in finding appropriate EO datasets due to inconsistent metadata structures and varied keyword descriptions. This misalignment hinders the discoverability and usability of EO data. Methods To address this challenge, the EIFFEL ontology (EIFF-O) is proposed. EIFF-O introduces taxonomies and ontologies to provide (i) global classification of EO data and (ii) linkage between different datasets through common concepts. The taxonomies specified by the European Association of Remote Sensing Companies (EARSC) have been formalized and implemented in EIFF-O. Additionally, EIFF-O incorporates: 1. An Essential Climate Variable (ECV) ontology, defined by the Global Climate Observing System (GCOS), is embedded and tailored for Climate Change (CC) applications. 2. The Sustainable Development Goals (SDG) ontology is included to facilitate linking datasets to specific targets. 3. The ontology extends schema.org vocabularies and promotes the use of JavaScript Object Notation for Linked Data (JSON-LD) formats for semantic web integration. Results EIFF-O provides a unified framework that enhances the discoverability, usability, and application of EO datasets. The implementation of EIFF-O allows data providers and users to bridge the gap between varied metadata descriptions and structured classification, thereby facilitating better linkage and integration of EO datasets. Conclusions The EIFFEL ontology represents a significant advancement in the organization and application of EO datasets. By embedding ECV and SDG ontologies and leveraging semantic web technologies, EIFF-O not only streamlines the data discovery process but also supports diverse applications, particularly in Climate Change monitoring and Sustainable Development Goals achievement. The open-source nature of the ontology and its associated tools promotes rapid adoption among developers

Automated Absolute Binding Free Energy Calculation Workflow for Drug Discovery.

Absolute binding free energies play a crucial role in drug development, particularly as part of the lead discovery process. In recent work, we showed how in silico predictions directly could support drug development by ranking and recommending favorable ideas over unfavorable ones. Here, we demonstrate a Python workflow that enables the calculation of ABFEs with minimal manual input effort, such as the receptor PDB and ligand SDF files, and outputs a .tsv file containing the ranked ligands and their corresponding binding free energies. The implementation uses Snakemake to structure and control the execution of tasks, allowing for dynamic control of parameters and execution patterns. We provide an example of a benchmark system that demonstrates the effectiveness of the automated workflow.

Recent advances in protein precipitation-based methods for drug-target screening

Drug targets are biological macromolecules that bind drug molecules in vivo. Therefore, the system-wide identification of drug targets plays a vital role in fully understanding the mechanism of drug action, efficacy, and side effects. The unbiased screening of drug targets may accelerate the process of drug discovery and candidate screening. Mass spectrometry is a key tool for large-scale protein identification and accurate quantification owing to its high acquisition speed, resolution, and sensitivity. Mass spectrometry-based proteomics has been widely used for drug-target screening. It can systematically identify the protein-target landscape of a drug and elucidate drug-protein interactions. Commonly used drug-target characterization methods, such as labeling-based affinity enrichment, require the chemical derivatization of drug molecules, which is not only time-consuming but may also affect the affinity of the drug towards its targets. Furthermore, the spatial effects of the derivatization groups may block interactions between the drug and its targets. Considering the disadvantages of affinity-enrichment methods, strategies that do not require chemical derivatization have received widespread attention. Proteins may undergo denaturation, unfolding, and precipitation under different conditions such as high temperatures, extreme pH, denaturants, and mechanical stress. Binding to small-molecule drugs may alter the folding balance of target proteins. The conformational stability of target proteins can be stabilized by binding with drugs, and protein-drug complexes are more resistant than free proteins to the precipitation induced by different conditions. Based on this mechanism, various large-scale drug-target identification methods using protein precipitation have been developed by combining proteomics and mass spectrometry analysis, including thermal proteome profiling and solvent-, mechanical stress-, and pH-induced protein precipitation. These methods have been successfully applied to the characterization of small-molecule drug targets. In this review, we describe the protein precipitation-based methods used for the high-throughput discovery of drug targets and elucidation of the interactions between drugs and proteins in the past decade. We also summarize the characteristics of each method and discuss their application potential in drug-efficacy evaluation and drug discovery.

Ligand- and Structure-based Approaches for Transmembrane Transporter Modeling.

The study of transporter proteins is key to understanding the mechanism behind multidrug resistance and drug-drug interactions causing severe side effects. While ATP-binding transporters are well-studied, solute carriers illustrate an understudied family with a high number of orphan proteins. To study these transporters, in silico methods can be used to shed light on the basic molecular machinery by studying protein-ligand interactions. Nowadays, computational methods are an integral part of the drug discovery and development process. In this short review, computational approaches, such as machine learning, are discussed, which try to tackle interactions between transport proteins and certain compounds to locate target proteins. Furthermore, a few cases of selected members of the ATP binding transporter and solute carrier family are covered, which are of high interest in clinical drug interaction studies, especially for regulatory agencies. The strengths and limitations of ligand-based and structure-based methods are discussed to highlight their applicability for different studies. Furthermore, the combination of multiple approaches can improve the information obtained to find crucial amino acids that explain important interactions of protein-ligand complexes in more detail. This allows the design of drug candidates with increased activity towards a target protein, which further helps to support future synthetic efforts.

Applying Machine Learning for Antibiotic Development and Prediction of Microbial Resistance.

Antimicrobial resistance (AMR) poses a serious threat to human health worldwide. It is now more challenging than ever to introduce a potent antibiotic to the market considering rapid emergence of antimicrobial resistance, surpassing the rate of antibiotic drug discovery. Hence, new approaches need to be developed to accelerate the rate of drug discovery process and meet the demands for new antibiotics, while reducing the cost of their development. Machine learning holds immense promise of becoming a useful tool, especially since in the last two decades, exponential growth has occurred in computational power and biological big data analytics. Recent advancements in machine learning algorithms for drug discovery have provided significant clues for potential antibiotic classes. Apart from discovery of new scaffolds, the machine learning protocols will significantly impact prediction of AMR patterns and drug metabolism. In this review, we outline power of machine learning in antibiotic drug discovery, metabolic fate, and AMR prediction to support researchers engaged and interested in this field.

A Model for Predicting Dropout of Higher Education Students

Higher education institutions are becoming increasingly concerned with the retention of their students. This work is motivated by the interest in predicting and reducing student dropout, and consequently in reducing the financial loss of said institutions. From the characterization of the dropout problem, and application of a knowledge discovery process, a model (ensemble) to improve dropout prediction is proposed. The ensemble model combines the results of three models: Logistic Regression, Neural Networks, and Decision Tree. As a result, the model can correctly classify 89% of the students (as enrolled and dropped) and accurately identify 98.1% dropouts. When the proposed model is compared with the ensemble method, Random Forest, the proposed model presented desirable characteristics to assist the management in proposing actions to retain students.

Using Discovery Learning Approach in Teaching EFL Grammar to High School Students: Its Possible Effects and Limits

This article presents part of a doctoral research project aimed at investigating the effects and possible limits of the discovery learning approach in teaching grammar to English as a foreign language (EFL) students in high schools in Slovakia from the teachers’ perceptions. The research project deployed design-based research with a total of 7 EFL teachers in Slovak high schools collecting data in three phases. The data presented in this article focused on intervention and were collected during the second phase of the research project. During the intervention, the teachers were taking field notes which were subsequently analysed. The results of the analysis of the teachers’ field notes showed there are mostly positive aspects of the discovery learning approach, pointing out students’ experience with discovery and their engagement when learning grammar. However, the results also showed some limits of this approach in the high school environment. Mostly training is needed for both teachers and students so they can make the most of the discovery process.

Traditional, Phytochemical, Nutritional and Biological Importance of Pithecellobium dulce (Roxib.) Benth

Pithecellobium dulce (P. dulce) is described in this review in terms of its botanical features, traditional uses, phytoconstituents, biological activities, and nutritional value. The aril of the fruit is consumed raw as food in many countries like India for its sweet taste. The plant phytoconstituents possess anti-ulcerogenic, anti-microbial, anti-inflammatory, and anti-diabetic properties. The plant’s different extracts contain a variety of bioactive phytochemicals, including flavonoids, saponins, and tannins. People have been paying attention to medicinal plants over the past few years due to their incredible significance in the medication discovery process, their effectiveness, safety, and lack of negative side effects. P. dulce is a highly regarded plant in traditional medicine because of its diverse biological and nutraceutical properties. This review covers information regarding traditional uses, nutritional values, phytochemicals, and pharmacological activities of the different extracts as well as the pure compounds isolated from P. dulce's different parts and extracts.

HUNGRYGO A RESTAURANT SUGGESTION WEBSITE

Abstract : In today’s digital age, individuals rely heavily on online platforms for various needs, including discovering dining options. HungryGo emerges as a promising solution in this landscape, offering a restaurant suggestion website that provides personalized dining recommendations based on users’ preferences and location. Through the integration of sophisticated algorithms and user input, HungryGo promises a seamless and tailored experience for users seeking culinary options. This research paper delves into the design, implementation, and evaluation of HungryGo, meticulously detailing its features, performance, and potential impact on both the restaurant industry and user satisfaction. Through comprehensive analysis and evaluation, this paper aims to shed light on the efficacy and significance of HungryGo in revolutionizing the restaurant discovery process and enhancing user dining experiences. Keywords – restaurant suggestion website, personalized dining recommendations, online platforms

Micro-CT-assisted identification of the optimal time-window for antifibrotic treatment in a bleomycin mouse model of long-lasting pulmonary fibrosis

Idiopathic Pulmonary Fibrosis (IPF) is a debilitating and fatal lung disease characterized by the excessive formation of scar tissue and decline of lung function. Despite extensive research, only two FDA-approved drugs exist for IPF, with limited efficacy and relevant side effects. Thus, there is an urgent need for new effective therapies, whose discovery strongly relies on IPF animal models. Despite some limitations, the Bleomycin (BLM)-induced lung fibrosis mouse model is widely used for antifibrotic drug discovery and for investigating disease pathogenesis. The initial acute inflammation triggered by BLM instillation and the spontaneous fibrosis resolution that occurs after 3 weeks are the major drawbacks of this system. In the present study, we applied micro-CT technology to a longer-lasting, triple BLM administration fibrosis mouse model to define the best time-window for Nintedanib (NINT) treatment. Two different treatment regimens were examined, with a daily NINT administration from day 7 to 28 (NINT 7–28), and from day 14 to 28 (NINT 14–28). For the first time, we automatically derived both morphological and functional readouts from longitudinal micro-CT. NINT 14–28 showed significant effects on morphological parameters after just 1 week of treatment, while no modulations of these biomarkers were observed during the preceding 7–14-days period, likely due to persistent inflammation. Micro-CT morphological data evaluated on day 28 were confirmed by lung histology and bronchoalveolar lavage fluid (BALF) cells; Once again, the NINT 7–21 regimen did not provide substantial benefits over the NINT 14–28. Interestingly, both NINT treatments failed to improve micro-CT-derived functional parameters. Altogether, our findings support the need for optimized protocols in preclinical studies to expedite the drug discovery process for antifibrotic agents. This study represents a significant advancement in pulmonary fibrosis animal modeling and antifibrotic treatment understanding, with the potential for improved translatability through the concurrent structural–functional analysis offered by longitudinal micro-CT.

Retracted:An integrated protocol based on workflows of imaged capillary isoelectric focusing (icIEF) for in-depth protein heterogenous characterization: High-efficient fractionation and online mass spectrometry detection

Herein we describe an integrated protocol to study the charge heterogeneity of therapeutic proteins. The protocol integrating two workflows, based on imaged capillary isoelectric focusing (icIEF) with fractionation and mass spectrometry (MS) online detection on the same platform, is validated, and can be used for protein charge variant characterization at intact MS and in-depth peptide mapping of collected fractions by high performance liquid chromatography (HPLC). The developed protocol was evaluated based on systematic methodology validation, which is demonstrated to be a robust platform that suits for the heterogeneity characterization of diverse therapeutic proteins with Standard Operating Procedures (SOP), outstanding reproducibility and excellent sensitivity. Especially, the constructed protocol can perform both functions of icIEF fractionation and MS online detection in a unique icIEF platform with high throughput, easy method development and less reagent consumption as comparing to traditional technologies, which “Green Chemistry Concept” helps biopharma industry address urgent challenges during the whole process of discovery, quality control and manufacture release.

PRICE DISCOVERY EFFICIENCY IN BIST LIQUIDITY BANK INDEX USING DEFERRED FUTURES: A MULTILAYER PERCEPTRON NEURAL NETWORK APPROACH

The literature indicates that the process of price discovery in spot and futures can be bidirectional. This study novelty lies in its analysis of the spot data of the BIST liquid bank index, a relatively new index in Turkey, using futures contracts of different maturities with a multi-layer perceptron (MLP) artificial neural network model. The efficacy of the models is evaluated by examining the capacity of futures prices to inform spot price discovery. The effectiveness of the MLP models is measured by low mean squared error (MSE) ratios relative to the out-of-samples test series results. The findings indicate that the one- and two-next futures contracts of the liquid bank index are more effective than the nearest futures contracts in explaining spot prices. Additionally, the nearest expiry contracts are observed to exhibit higher variances than the others. The most efficient pricing model including both spot and futures as explaining variables, is autoregression with three lags for spot and two lags for the two next futures contracts. These results must be considered when implementing risk management strategies for individuals engaged in spot and futures transactions.

Are peptidomimetics the compounds of choice for developing new modulators of the JAK-STAT pathway?

Protein-protein interactions (PPIs) play critical roles in a wide range of biological processes including the dysregulation of cellular pathways leading to the loss of cell function, which in turn leads to diseases. The dysfunction of several signaling pathways is linked to the insurgence of pathological processes such as inflammation, cancer development and neurodegeneration. Thus, there is an urgent need for novel chemical modulators of dysregulated PPIs to drive progress in targeted therapies. Several PPIs have been targeted by bioactive compounds, and, often, to properly cover interacting protein regions and improve the biological activities of modulators, a particular focus concerns the employment of macrocycles as proteomimetics. Indeed, for their physicochemical properties, they occupy an intermediate space between small organic molecules and macromolecular proteins and are prominent in the drug discovery process. Peptide macrocycles can modulate fundamental biological mechanisms and here we will focus on peptidomimetics active on the Janus kinase/signal transducers and activators of transcription (JAK-STAT) pathways.