Discovery Methods Research Articles

Data Aestheticization: A Cognitively-Inspired Method for Knowledge Discovery in Cognitive IoT Sensor Network

Data Aestheticization: A Cognitively-Inspired Method for Knowledge Discovery in Cognitive IoT Sensor Network

Narrative Communication of Teachers’ Support Needs for Violence-Based Trauma in the Schools

Community violence is a serious public health problem, and research on community violence consistently documents a relationship between exposure to community violence and adolescents’ psychological and behavioral challenges, which, when considered cumulatively, can be understood as collective trauma. Preventing and mediating exposure to community violence by local schools is, thus, an important part of efforts to promote healthy adolescent development. As a societal problem, community violence and its socio-psychological effects lend themselves to an interdisciplinary approach for researching the problem and possible intervention and mitigation in local schools. The integrative, interdisciplinary approach to problem analysis in this study provides a framework for addressing trauma due to community violence as it affects the school environment. Narrative analysis, as a method of discovery, reveals the struggles of high school teachers dealing with the effects of violence-based trauma on their students. Policy recommendations and implications for future research are included.

DDDR-15. UTILIZING DEEP DOCKING AND ARTIFICIAL INTELLIGENCE FOR THE DISCOVERY OF NOVEL PARP1-SELECTIVE INHIBITORS FOR USE AGAINST BRAIN TUMORS

Abstract Brain tumors, including primary brain tumors and central nervous system (CNS) metastases, remain among the most challenging malignancies to treat, with therapeutic options often limited by the inability of drugs to penetrate the blood-brain barrier. Poly(ADP-ribose) polymerase (PARP) is a key enzyme in DNA repair, and inhibition of PARP1 specifically drives synthetic lethality in BRCA-mutated disease. While they’ve achieved commercial success, first generation PARP inhibitors are limited in their utility as they cannot readily pass through the blood-brain barrier and have adverse side effects, likely driven by the collateral inhibition of PARP2. Development of a PARP1-selective, CNS penetrant inhibitor could reduce toxicity, while providing a new therapeutic option for brain tumors. Traditional drug discovery methods are time-consuming and costly, necessitating innovative approaches. Here, we describe the application of Deep Docking, an advanced artificial intelligence (AI) approach, to discover a novel, PARP1-selective inhibitor for use against brain tumors. Deep Docking utilizes deep learning to accelerate the prediction of the binding affinity of a large number of compounds to target proteins, streamlining the virtual screening process and allowing for rapid docking of billions of compounds against the PARP1 protein. Additionally, state-of-the-art generative algorithms and deep learning techniques for predicting drug-like properties such as metabolism, permeability, and safety profiles, can be combined to rapidly perform hit-to-lead optimization. We will present preliminary Deep Docking screening results from billions of compounds, identifying several with predicted high binding affinities for PARP-1. Validating in vitro and in vivo data, including PARP1 selectivity, metabolic stability, pharmacokinetic profile, CNS penetration and safety profile, will be described. Application of the Deep Docking AI platform is being used to significantly accelerate the discovery of a selective PARP-1 inhibitor for brain tumors. This approach will not only improve the efficiency of drug discovery but also enhance the specificity and efficacy of potential therapeutics.

Phytosterols as inhibitors of New Delhi metallo-β-lactamase (NDM-1): an in silico study.

The global emergence of New Delhi metallo-β-lactamase-1 (NDM-1) poses a formidable challenge to antibiotic therapy, as it confers resistance to a wide range of β-lactam antibiotics. This study aims to identify potential inhibitors of NDM-1 and thereby restore the effectiveness of the current antibiotics. Employing a comprehensive computational approach integrating molecular docking and molecular dynamics (MD) simulations, a library of phytosterols was screened to identify promising candidates for inhibiting NDM-1 activity. Using the binding energy of meropenem, a frontline carbapenem antibiotic, as a reference, avenasterol, brassicasterol, and stigmasterol emerged as top phytosterol candidates for further investigation. Subsequent MD simulations confirmed the stability of NDM-1 complexes with avenasterol and stigmasterol over the simulation period, indicating their potential efficacy. These findings suggest that avenasterol and stigmasterol may effectively inhibit NDM-1 activity, warranting validation through in vitro and in vivo studies. Furthermore, these phytosterols hold promise as lead compounds for developing novel NDM-1 inhibitors. Their natural origin and potential inhibitory activity against NDM-1 offer compelling avenues for developing alternative antibacterial therapies to combat multidrug-resistant infections. This study underscores the utility of computational methods in drug discovery and highlights the potential of phytosterols as valuable candidates for addressing antibiotic resistance.

MAMSC: a semantic enhanced representation model for public opinion key node recognition based on multianchor mapping in semantic communities

PurposeInformation is presented in various modalities such as text and images, and it can quickly and widely spread on social networks and among the general public through key communication nodes involved in public opinion events. Therefore, by tracking and identifying key nodes of public opinion, we can determine the direction of public opinion evolution and timely and effectively control public opinion events or curb the spread of false information.Design/methodology/approachThis paper introduces a novel multimodal semantic enhanced representation based on multianchor mapping semantic community (MAMSC) for identifying key nodes in public opinion. MAMSC consists of four core components: multimodal data feature extraction module, feature vector dimensionality reduction module, semantic enhanced representation module and semantic community (SC) recognition module. On this basis, we combine the method of community discovery in complex networks to analyze the aggregation characteristics of different semantic anchors and construct a three-layer network module for public opinion node recognition in the SC with strong, medium and weak associations.FindingsThe experimental results show that compared with its variants and the baseline models, the MAMSC model has better recognition accuracy. This study also provides more systematic, forward-looking and scientific decision-making support for controlling public opinion and curbing the spread of false information.Originality/valueWe creatively combine the construction of variant autoencoder with multianchor mapping to enhance semantic representation and construct a three-layer network module for public opinion node recognition in the SC with strong, medium and weak associations. On this basis, our constructed MAMSC model achieved the best results compared to the baseline models and ablation evaluation models, with a precision of 91.21%.

Machine-Learning-Assisted Materials Discovery from Electronic Band Structure.

Traditional methods of materials discovery, often relying on intuition and trial-and-error experimentation, are time-consuming and limited in their ability to explore the vast design space effectively. The emergence of machine learning (ML) as a powerful tool for pattern recognition has opened exciting opportunities to revolutionize materials discovery. This work explores the application of ML techniques to assist in the discovery of materials using band structure data. The electronic band structure, which describes the energy levels of electrons in a material, holds vital information regarding its electronic and optical properties. The band structure data of 63,588 materials, including metals and insulators, have been retrieved from the Materials Project database. The data were grouped into 85 batches based on the band path in the first Brillouin zone. Three ML clustering algorithms were trained on the band structure data after performing feature selection and engineering, followed by noise reduction. The models were validated by comparing the materials' properties in a cluster.

Advancements in Contemporary Pharmacological Innovation: Mechanistic Insights and Emerging Trends in Drug Discovery and Development

Developing a new drug and bringing it to the market is a complex and time-consuming process that involves multiple phases of drug discovery and development. However, recent advancements in various technologies, such as multi-omics, genome editing, Artificial Intelligence (AI), and Machine Learning (ML), have significantly improved this process. These technologies have made the process more accurate, less time-consuming, and cost-effective compared to the conventional methods of drug discovery and development. In the current age, discovering and developing drugs is a collaborative effort that involves scientific breakthroughs, technological advancements, and regulatory oversight. The pharmaceutical industry is constantly innovating new techniques, fostering interdisciplinary collaboration, and prioritizing patient-centered approaches. In this review, we explore the latest and most updated information about using advanced technologies in drug discovery. The review begins by briefly explaining the conventional drug discovery and development process, and then delves into the applications of multi-omics, genome editing technology, systems biology, artificial intelligence, and machine learning.

Tiny but mighty: small molecules as vaccine adjuvants

Screening small molecule (SM) libraries now replaces traditional methods for vaccine adjuvant discovery. A study by Soni et al. highlights the use of primary human cells in high-throughput screening (HTS), leading to the discovery of a novel SM TLR7/8 agonist, PVP-037. This compound successfully restored vaccine-induced immune responses in aged mice.

CoSD: Balancing behavioral consistency and diversity in unsupervised skill discovery

In hierarchical reinforcement learning, unsupervised skill discovery holds promise for overcoming the challenge of sparse rewards commonly encountered in traditional reinforcement learning. However, previous unsupervised skill discovery methods have excelled at maximizing intrinsic rewards but have overly prioritized skill diversity. Unrestrained pursuit of diversity leads skills to concentrate attention on unexplored domains, overlooking the internal consistency of skills themselves, resulting in the state visit distribution of individual skills lacking concentration. To address this problem, the Constrained Skill Discovery (CoSD) algorithm is proposed to balance the diversity and behavioral consistency of skills. CoSD integrates both the forward and backward decomposition forms of mutual information and uses the maximum entropy policy to maximize the information-theoretic objective of skill learning while requiring that each skill maintain low state entropy internally, which enhances the behavioral consistency of the skills while pursuing the diversity of the skills and ensures that the learned skills have a high degree of stability. Experimental results demonstrated that, compared to other skill discovery methods based on mutual information, skills from CoSD exhibit a more concentrated state visit distribution, indicating higher behavioral consistency and stability. In some complex downstream tasks, these skills with higher behavioral consistency exhibit superior performance.

Developing Generalizable Scoring Functions for Molecular Docking: Challenges and Perspectives.

Structure-based drug discovery methods, such as molecular docking and virtual screening, have become invaluable tools in developing novel drugs. At the core of these methods are Scoring Functions (SFs), which predict the binding affinity between ligands and protein targets. This study aims to review and contextualize the challenges and best practices in training novel scoring functions to improve their accuracy and generalizability in predicting protein-ligand binding affinities. Effective training of scoring functions requires careful attention to the quality of training data and methodologies. We emphasize the need for robust training strategies to produce consistent and generalizable SFs. Key considerations include addressing hidden biases and overfitting in machine-learning models, as well as ensuring the use of high-quality, unbiased datasets for both training and evaluation of SFs. Innovative hybrid methods, combining the advantages of empirical and machine-learning approaches, hold promise for outperforming current scoring functions while displaying greater generalizability and versatility.

Integrating Organ-on-Chip Models In Drug Discovery: A Comprehensive Review on Innovations and Implications

This review article examines the current developments in applying microfluidic technologies in cancer therapy and personalized medicine. This includes the fabrication of cancer cells onto the microfluidic chips, preclinical cancer model simulation development, biomarker detection, tumor heterogeneity detection, integration of microfluidics in robotic drug delivery systems, Artificial Intelligence (AI), and discuss the use of techniques such as Machine Learning (ML) to predict pharmacokinetics and pharmacodynamics of cancer cells. This review article also highlights how integrating cancer models with microfluidic devices helps to simulate disease progression more accurately, thereby improving treatment options. These devices also enable researchers to identify suitable doses for cancer treatment. Moreover, microfluidics chips facilitate cell transformation in many types of cancer, which is important for patient-specific therapy. Microfluidics technology in robotic drug delivery enables precise delivery of targeted drugs, thus reducing the potential side effects of the drugs. Integrating these fields into the medical and pharmaceutical fields helps researchers to develop the pharmaceutical product faster than the traditional method of drug discovery. Overall, this review article highlights the integration of interdisciplinary technologies in the healthcare field, which may decrease the timeline of drug discovery and provide efficient drugs to patients.

Trends of Artificial Intelligence (AI) Use in Drug Targets, Discovery and Development: Current Status and Future Perspectives.

The applications of artificial intelligence (AI) in pharmaceutical sectors have advanced drug discovery and development methods. AI has been applied in virtual drug design, molecule synthesis, advanced research, various screening methods, and decision-making processes. In the fourth industrial revolution, when medical discoveries are happening swiftly, AI technology is essential to reduce the costs, effort, and time in the pharmaceutical industry. Further, it will aid "genome-based medicine" and "drug discovery." AI may prepare proactive databases according to diseases, disorders, and appropriate usage of drugs which will facilitate the required data for the process of drug development. The application of AI has improved clinical trials on patient selection in a population, stratification, and sample assessment such as biomarkers, effectiveness measures, dosage selection, and trial length. Various studies suggest AI could be perform better compared to conventional techniques in drug discovery. The present review focused on the positive impact of AI in drug discovery and development processes in the pharmaceutical industry and beneficial usage in health sectors as well.

CompariPSSM: a PSSM-PSSM comparison tool for motif binding determinant analysis.

Short Linear Motifs (SLiMs) are compact functional modules that mediate low-affinity protein-protein interactions. SLiMs direct the function of many dynamic signalling and regulatory complexes playing a central role in most biological processes of the cell. Motif binding determinants describe the contribution of each residue in a motif-containing peptide to the affinity and specificity of binding to the motif-binding partner. Motif binding determinants are generally defined as a motif consensus pattern or a position-specific scoring matrix (PSSM) encoding quantitative preferences. Motif binding determinant comparison is an important motif analysis task and can be applied to motif annotation, classification, clustering, discovery and benchmarking. Currently, binding determinant comparison is generally performed by analysing consensus similarity, however, this ignores important quantitative information in both the consensus and non-consensus positions. We have created a new tool, CompariPSSM, that quantifies the similarity between motif binding determinants using sliding window PSSM-PSSM comparison and scores PSSM similarity using a randomisation-based probabilistic framework. The tool has been benchmarked on curated data from the Eukaryotic Linear Motif (ELM) database and experimental data from Proteomic Phage Display (ProP-PD). CompariPSSM can be used for peptide classification to validate motif classes, peptide clustering to group functionally related conserved disordered regions, and benchmarking experimental motif discovery methods. CompariPSSM is available at https://slim.icr.ac.uk/projects/comparipssm. Supplementary data are available at Bioinformatics online.

An Extensive Computational Investigation of Mycobacterium tuberculosis Pantothenate Synthetase Inhibitors from Diverse‐Lib Compounds Library

AbstractAntibiotics have played a crucial role in significantly reducing the incidence of tuberculosis (TB) infection worldwide. Even before the mid‐20th century, the mortality rate of TB onset within five years was around 50 %. So, the introduction of antibiotics has changed the scenario of TB from a serious threat to a manageable one. However, the emergence of resistance to anti‐TB drugs poses a significant challenge. So, to overcome this situation the therapeutic approaches and drug targets need to be reformed. This study focused on finding potential inhibitors by targeting Pantothenate Synthetase, a crucial enzyme for Mycobacterium tuberculosis (Mtb) survival, through computational drug discovery methods. Molecular docking and virtual screening were employed to identify potential inhibitors from Diverse‐lib. Four compounds, namely CID2813602, 24357538, CID753354, and CID4798023, exhibited strong binding energies and stable interaction with the target protein. Further assessment of these compounds through MD simulation and Post MD simulation showed significant dynamic stability. The minimum energy transition calculated using the free energy landscape analysis of these compounds when docked with Pantothenate Synthetase confirmed the stability of each complex due to its minimum energy production. The free binding energy calculation of each complex also showed the intramolecular interaction contributes to the strong binding affinity of the compounds within the enzyme's active site clarifying their mechanisms of action. This research showcases the effectiveness of computational methods in promptly identifying potential anti‐TB drugs, paving the way for future experimental validation and optimization. It holds promise for the development of new treatments targeting drug‐resistant TB strains.

Service similarity measurement integrating Bi-LSTM contextual representation and attention mechanism for web service discovery

With the rapid growth and wide adoption of web services, service discovery is becoming commonly used to locate the optimal services that can meet the requirements of users. This paper proposes a bidirectional long short-term memory (Bi-LSTM) service discovery method that integrates contextual representation and attention mechanisms for web service discovery. To conduct the study, FastText word embedding with the N-gram feature is primarily used as the input of the Bi-LSTM model to obtain the context feature vector of the service function description. Moreover, the attention mechanism is employed to calculate the similarity of the service function description to gain the semantic feature vector of the correlation degree between services. Then, the multilayer perceptron network is used to mine the mapping relationship between semantic feature vectors and matching levels among services. Finally, for a given query, target services are retrieved by ranking candidate services according to the prediction cores through the matching method. The proposed method is examined against multiple evaluation metrics, including accuracy and error, and compared with the state-of-the-art searching approaches. The experimental results show that the proposed method is more effective and attains better relevant web services, which helps the request service find more accurate results. Furthermore, this study provides new insights for researchers into web service discovery.

Termination Of The Office Of The Land Deed Maker Who Are Otherwise Unable To Perform Their Duties Due To Health Conditions

The legal issue is motivated by the existence of arrangements related to the dismissal of PPAT with respect contained in Article 10 paragraph (2) letter B which states that PPAT can be dismissed with respect from his position because he is no longer able to carry out his duties due to the state of his health or mental health, after being declared by the authorized health examination team at the request of the Minister/Head or appointed official. The problem then is how when there is a PPAT who is dismissed with respect for health reasons in accordance with Article 10 paragraph (2) letter b, then the PPAT in question successfully recovers from his illness. The purpose of this study was to analyze and find whether PPAT that was dismissed with respect due to health problems can be reappointed after being declared cured and formulate future regulatory formulations related to PPAT dismissal regulations due to health problems. This research uses normative juridical research method, legislation research approach and conceptual approach, and legal materials used consist of primary, secondary, and tertiary legal materials obtained through literature studies in the library of the Faculty of law, Universitas Brawijaya, and also internet access. The results of this study were analyzed by the method of legal discovery, namely the method of interpretation and construction. The results of this study are PPAT dismissed with respect in accordance with Article 10 paragraph (2) letter B PP No. 24 of 2016 if the PPAT has recovered, it still cannot be reappointed to his position as PPAT because there are no rules governing it. This would have been contrary to the theory of legal certainty according to Nurhasanah Ismail. For the formulation of future arrangements related to PPAT dismissal regulations due to health problems can be constructed into a new provision that PPAT who experience health problems, changed from the previous one dismissed with respect, is changed to become a temporary dismissal.

Design & development of adulteration detection system by fumigation method & machine learning techniques

A novel method for discovery of adulteration in edible oil is proposed based on concept of refractive index and electronic sensors. The research work focusses on two distinct methodologies like employing datasets and implementing a fumigation technique that integrates real-time hardware for testing Edible oil Impurities. In the first method, the dataset taken into consideration contains spectral data collected using Advanced ATR-MIR Spectroscopy for pure oil and various levels of adulteration with Vegetable oil. Each and every edible oil has a certain value of refractive index. When such oils are contemned in a change adding adulterants, the value of its refractive indices also changes. This value of refractive index serves as a feature for testing the oil and helps us in detecting the adulteration. If Oil is adulterated with vegetable oils, the refractive index will be lower and with animal fats, the refractive index will be higher than that of pure Oil. While in Fumigation Method a hardware module is develop in which adulterated & pure oil samples are heated at 40–50 °C for 4.66 min and the volatiles that are generated by varying gas concentrations are forcefully passed through to the MEMS Gas Sensor-MISC-2714 and Multichannel Gas sensor. The conductance of the sensors changes according to the gases sensed by the sensors contributes to features extraction. The conductance value serves as a feature for the classifier to determine whether the sample is highly, moderately, or lowly contaminated. Thus, in proposed methods we use different algorithms based on machine learning like KNN, Random Forest, CATBOOST and XGBOOST to accurately reveal the adulteration. Amongst all the applied algorithm Random Forest (RF) Classifier & XGBOOST algorithm outperform well and gives 100% accuracy. The proposed work is used for identifying food adulteration in edible food products which helps us to feed Society with high-quality food.

Structural and free energy landscape analysis for the discovery of antiviral compounds targeting the cap-binding domain of influenza polymerase PB2

Influenza poses a significant threat to global health, with the ability to cause severe epidemics and pandemics. The polymerase basic protein 2 (PB2) of the influenza virus plays a crucial role in the viral replication process, making the CAP-binding domain of PB2 an attractive target for antiviral drug development. This study aimed to identify and evaluate potential inhibitors of the influenza polymerase PB2 CAP-binding domain using computational drug discovery methods. We employed a comprehensive computational approach involving virtual screening, molecular docking, and 500 ns molecular dynamics (MD) simulations. Compounds were selected from the Diverse lib database and assessed for their binding affinity and stability in interaction with the PB2 CAP-binding domain. The study utilized the generalized amber force field (GAFF) for MD simulations to further evaluate the dynamic behaviour and stability of the interactions. Among the screened compounds, compounds 1, 3, and 4 showed promising binding affinities. Compound 4 demonstrated the highest binding stability and the most favourable free energy profile, indicating strong and consistent interaction with the target domain. Compound 3 displayed moderate stability with dynamic conformational changes, while Compound 1 maintained robust interactions throughout the simulations. Comparative analyses of these compounds against a control compound highlighted their potential efficacy. Compound 4 emerged as the most promising inhibitor, with substantial stability and strong binding affinity to the PB2 CAP-binding domain. These findings suggest that compound 4, along with compounds 1 and 3, holds the potential for further development into effective antiviral agents against influenza. Future studies should focus on experimental validation of these compounds and exploration of resistance mechanisms to enhance their therapeutic utility.

Enhancing the Predictive Power of Machine Learning Models through a Chemical Space Complementary DEL Screening Strategy.

DNA-encoded library (DEL) technology is an effective method for small molecule drug discovery, enabling high-throughput screening against target proteins. While DEL screening produces extensive data, it can reveal complex patterns not easily recognized by human analysis. Lead compounds from DEL screens often have higher molecular weights, posing challenges for drug development. This study refines traditional DELs by integrating alternative techniques like photocross-linking screening to enhance chemical diversity. Combining these methods improved predictive performance for small molecule identification models. Using this approach, we predicted active small molecules for BRD4 and p300, achieving hit rates of 26.7 and 35.7%. Notably, the identified compounds exhibit smaller molecular weights and better modification potential compared to traditional DEL molecules. This research demonstrates the synergy between DEL and AI technologies, enhancing drug discovery.

A Survey on Computational Methods in Drug Discovery for Neurodegenerative Diseases.

Currently, the age structure of the world population is changing due to declining birth rates and increasing life expectancy. As a result, physicians worldwide have to treat an increasing number of age-related diseases, of which neurological disorders represent a significant part. In this context, there is an urgent need to discover new therapeutic approaches to counteract the effects of neurodegeneration on human health, and computational science can be of pivotal importance for more effective neurodrug discovery. The knowledge of the molecular structure of the receptors and other biomolecules involved in neurological pathogenesis facilitates the design of new molecules as potential drugs to be used in the fight against diseases of high social relevance such as dementia, Alzheimer's disease (AD) and Parkinson's disease (PD), to cite only a few. However, the absence of comprehensive guidelines regarding the strengths and weaknesses of alternative approaches creates a fragmented and disconnected field, resulting in missed opportunities to enhance performance and achieve successful applications. This review aims to summarize some of the most innovative strategies based on computational methods used for neurodrug development. In particular, recent applications and the state-of-the-art of molecular docking and artificial intelligence for ligand- and target-based approaches in novel drug design were reviewed, highlighting the crucial role of in silico methods in the context of neurodrug discovery for neurodegenerative diseases.