Discovery Methods Research Articles

Harnessing network pharmacology in drug discovery: an integrated approach.

Traditional drug discovery approach is based on one drug-one target, that is associated with very lengthy timelines, high costs and very low success rates. Network pharmacology (NP) is a novel method of drug designing, that is based on a multiple-target approach. NP integrates systems such as biology, pharmacology and computational techniques to address the limitations of traditional methods of drug discovery. With help of mapping biological networks, it provides deep insights into biological molecules' interactions and enhances our understanding to the mechanism of drugs, polypharmacology and disease etiology. This review explores the theoretical framework of network pharmacology, discussing the principles and methodologies that enable the construction of drug-target and disease-gene networks. It highlights how data mining, bioinformatics tools and computational models are utilised to predict drug behaviour, repurpose existing drugs and identify novel therapeutic targets. Applications of network pharmacology in the treatment of complex diseases-such as cancer, neurodegenerative disorders, cardiovascular diseases and infectious diseases-are extensively covered, demonstrating its potential to identify multi-target drugs for multifaceted disease mechanisms. Despite the promising results, NP faces challenges due to incomplete and quality of biological data, computational complexities and biological system redundancy. It also faces regulatory challenges in drug approval, demanding revision in regulatory guidelines towards multi-target therapies. Advancements in AI and machine learning, dynamic network modelling and global collaboration can further enhance the efficacy of network pharmacology. This integrative approach has the potential to revolutionise drug discovery, offering new solutions for personalised medicine, drug repurposing and tackling the complexities of modern diseases.

Using parenclitic networks on phaeochromocytoma and paraganglioma tumours provides novel insights on global DNA methylation

Despite the prevalence of sequencing data in biomedical research, the methylome remains underrepresented. Given the importance of DNA methylation in gene regulation and disease, it is crucial to address the need for reliable differential methylation methods. This work presents a novel, transferable approach for extracting information from DNA methylation data. Our agnostic, graph-based pipeline overcomes the limitations of commonly used differential methylation techniques and addresses the “small n, big k” problem. Pheochromocytoma and Paraganglioma (PPGL) tumours with known genetic aetiologies experience extreme hypermethylation genome wide. To highlight the effectiveness of our method in candidate discovery, we present the first phenotypic classifier of PPGLs based on DNA methylation achieving 0.7 ROC-AUC. Each sample is represented by an optimised parenclitic network, a graph representing the deviation of the sample’s DNA methylation from the expected non-aggressive patterns. By extracting meaningful topological features, the dimensionality and, hence, the risk of overfitting is reduced, and the samples can be classified effectively. By using an explainable classification method, in this case logistic regression, the key CG loci influencing the decision can be identified. Our work provides insights into the molecular signature of aggressive PPGLs and we propose candidates for further research. Our optimised parenclitic network implementation improves the potential utility of DNA methylation data and offers an effective and complete pipeline for studying such datasets.

Comprehensive applications of the artificial intelligence technology in new drug research and development.

Target-based strategy is a prevalent means of drug research and development (R&D), since targets provide effector molecules of drug action and offer the foundation of pharmacological investigation. Recently, the artificial intelligence (AI) technology has been utilized in various stages of drug R&D, where AI-assisted experimental methods show higher efficiency than sole experimental ones. It is a critical need to give a comprehensive review of AI applications in drug R &D for biopharmaceutical field. Relevant literatures about AI-assisted drug R&D were collected from the public databases (Including Google Scholar, Web of Science, PubMed, IEEE Xplore Digital Library, Springer, and ScienceDirect) through a keyword searching strategy with the following terms [("Artificial Intelligence" OR "Knowledge Graph" OR "Machine Learning") AND ("Drug Target Identification" OR "New Drug Development")]. In this review, we first introduced common strategies and novel trends of drug R&D, followed by characteristic description of AI algorithms widely used in drug R&D. Subsequently, we depicted detailed applications of AI algorithms in target identification, lead compound identification and optimization, drug repurposing, and drug analytical platform construction. Finally, we discussed the challenges and prospects of AI-assisted methods for drug discovery. Collectively, this review provides comprehensive overview of AI applications in drug R&D and presents future perspectives for biopharmaceutical field, which may promote the development of drug industry.

Physiologically-based pharmacokinetic model of in vitro porcine ear skin permeation for drug delivery research.

In silico techniques, such as physiologically based pharmacokinetic modeling (PBKP), are recently gaining importance. Computational methods in drug discovery and development and the generic drugs industry enhance research effectiveness by saving time and money and avoiding ethical issues. One key advantage is the ability to conduct toxicology studies without risking harm to living beings. This study aimed to repurpose the multi-phase multi-layer mechanistic dermal absorption (MPML MechDermA) PBPK model for simulation permeation through porcine ear skin under in vitro conditions. The work was divided into four steps: (1) the development of a pig ear skin model based on a previously collected dataset; (2) testing the model's ability to discriminate permeation between pig ear, human abdomen, and human back skin; (3) development of a caffeine permeation model; and (4) testing the caffeine model's performance against in vitro generated data sourced from the scientific literature. Data from 31 manuscripts were used for the development of the pig skin model. Based on these data, values specific to pig skin were found for 22 parameters of the MPML MechDermA model. The model was able to discriminate permeation between pig and human skin. A caffeine model was developed and used to simulate seven experiments identified in the literature. The model's performance was assessed by comparing simulated to observed results. Based on a visual check, all simulations were considered acceptable, whereas three out of seven experiments met the twofold difference criterion. The variability of the experimental data was considered the biggest challenge for reliable model assessment.

Three-way concept lattice based on Boolean formal context

The theory of formal concept analysis(FCA) is an important mathematical method for knowledge representation and knowledge discovery. The Boolean formal context is proposed by introducing Boolean matrices and logical operations into FCA. Based on the concept lattice of the Boolean formal context and the column vector(row-vector)-oriented concept lattice of the Boolean formal context, this paper proposes the column vector(row vector)-induced three-way concept lattice of the Boolean formal context and the column vector(row vector)-induced three-way column vector(row vector)-oriented concept lattice of the Boolean formal context, and proves their rationality. Then, the isomorphism between the three-way concept lattice of the Boolean formal context and the three-way concept lattice of the general formal context is proved.

Islamic Family Law Reform in Indonesia: A Review of the Supreme Court's Decision on the Postponement of the Distribution of Joint Property

The settlement of joint property between husband and wife after divorce often ends up in the Religious Court, even reaching the level of cassation and judicial review (PK), such as in the Supreme Court Decision Number Register 159/K/AG/2018. In this case, especially related to disputes over joint property in marriages with minor children, the Supreme Court not only refers to the normative law contained in the law, but also considers a sense of justice and benefit. This research uses a qualitative descriptive method with an empirical juridical approach. This approach is referred to as empirical legal research because it focuses on analyzing the implementation of normative legal provisions directly (in action) in certain legal events in society. Primary data was obtained from Supreme Court decision No 159 K/Ag/2018, No 618/Pdt.G/2012/PA.Bkt, No 38/Pdt.G/2013/PTA.Pdg, No 88K/Ag/2015, No 159/K/AG/2018, No 99/Pdt.G/2023/MS, as well as laws related to the research being discussed. Secondary data is obtained from books, scientific articles and previous research results related to joint property and those that have the same discussion. Data collection techniques using literature study and qualitative data analysis are used to explore certain phenomena and find out the causes. The results showed that the Supreme Court judge in deciding this case used the method of legal discovery through legal interpretation, especially systematic interpretation and sociological interpretation. In the case, the judge postponed the division of joint property until the children of the disputing parties reached the age of mumayyiz, thus showing attention to aspects of substantive justice. Systematic and sociological legal interpretations by judges show that in resolving post-divorce joint property disputes, especially those involving minors, judges must consider the social realities that live in society to actualize justice. This emphasizes the role of judges not only as law enforcers, but also as guardians of the public good. This research concludes that legal discovery by judges through interpretation based on systematic and sociological approaches can realize concrete justice in society, especially in divorce cases involving children.

Single-Cell Multiomics Identifies Glycan Epitope LacNAc as a Potential Cell-Surface Effector Marker of Peripheral T Cells in Bladder Cancer Patients.

Cancer is a systemic disease continuously monitored and responded to by the human global immune system. Peripheral blood immune cells, integral to this surveillance, exhibit variable phenotypes during tumor progression. Glycosylation, as one of the most prevalent and significant post-translational modifications of proteins, plays a crucial role in immune system recognition and response. Glycan analysis has become a key method for biomarker discovery. LacNAc, a prominent glycosylation modification, regulates immune cell activity and function. Therefore, we applied our previously developed single-cell glycomic multiomics to analyze peripheral blood in cancer patients. This platform utilizes chemoenzymatic labeling with DNA barcodes for detecting and quantifying LacNAc levels at single-cell resolution without altering the transcriptional status of immune cells. For the first time, we systematically integrated single-cell transcriptome, T cell receptor (TCR) repertoire, and glycan epitope LacNAc analyses in tumor-patient-derived peripheral blood. Our integrated analysis reveals that lower-stage bladder cancer patients showed significantly higher levels of LacNAc in peripheral T cells, and peripheral T cells with high levels of cell-surface LacNAc exhibit higher cytotoxicity and TCR clonal expansion. In summary, we identified LacNAc as a potential cell-surface effector marker for peripheral T cells in bladder cancer patients, which enhances our understanding of peripheral immune cells and offers potential advancements in liquid biopsy.

Physics-guided symbolic neural network reveals optimal functional forms describing ground motions

This study presents a novel framework for ground motion modelling utilizing Physics-Guided Symbolic Neural Networks (PGSNN). Symbolic neural networks offer a new method for knowledge discovery, providing a unique perspective for automatically uncovering predictive functional forms from data. This approach differs from traditional methods as it does not rely on predefined equations. Instead, it employs symbolic operators to freely combine input parameters in a high-dimensional space. This method addresses the problem of data imbalance by incorporating physical guidance to ensure that the model produces results that are consistent with established physical principles. The resulting equations align with the expectations of the engineering seismology community, particularly within the magnitude-distance ranges, where classical equations are well calibrated. The prediction performance of the PGSNN, evaluated across different intensity measures (PGA, PGV, and PSA), was assessed by calculating the residuals between measured and predicted values and their standard deviations. The predictive capability of this model was verified using new event records. The results indicate that the prediction performance of the PGSNN is comparable to those of traditional methods.

Recent Advances in Peptide Drug Discovery: Novel Strategies and Targeted Protein Degradation.

Recent technological advancements, including computer-assisted drug discovery, gene-editing techniques, and high-throughput screening approaches, have greatly expanded the palette of methods for the discovery of peptides available to researchers. These emerging strategies, driven by recent advances in bioinformatics and multi-omics, have significantly improved the efficiency of peptide drug discovery when compared with traditional in vitro and in vivo methods, cutting costs and improving their reliability. An added benefit of peptide-based drugs is the ability to precisely target protein-protein interactions, which are normally a particularly challenging aspect of drug discovery. Another recent breakthrough in this field is targeted protein degradation through proteolysis-targeting chimeras. These revolutionary compounds represent a noteworthy advancement over traditional small-molecule inhibitors due to their unique mechanism of action, which allows for the degradation of specific proteins with unprecedented specificity. The inclusion of a peptide as a protein-of-interest-targeting moiety allows for improved versatility and the possibility of targeting otherwise undruggable proteins. In this review, we discuss various novel wet-lab and computational multi-omic methods for peptide drug discovery, provide an overview of therapeutic agents discovered through these cutting-edge techniques, and discuss the potential for the therapeutic delivery of peptide-based drugs.

Rapid Exploration of Chemical Space by High-Throughput Desorption Electrospray Ionization Mass Spectrometry.

This study leverages accelerated reactions at the solution/air interface of microdroplets generated by desorption electrospray ionization (DESI) to explore the chemical space. DESI is utilized to synthesize drug analogs at an overall rate of 1 reaction mixture per second, working on the low-nanogram scale. Transformations of multiple drug molecules at specific functionalities (phenol, hydroxyl, amino, carbonyl, phenyl, thiophenyl, and alkenyl) are achieved using electrophilic/nucleophilic, redox, C-H functionalization, and coupling reactions. These transformations occur under ambient conditions on the millisecond time scale with direct detection of products being successful in all but three of the reaction types studied. The large scope (22 bioactive compounds, >20 chemical transformations, and >300 functionalization reagents) and high speed (>3000 reactions/hour) provide access to a wide array of drug analogs that can be used for bioactivity testing. A total of ∼6800 unique reactions were examined through a data-driven workflow, and more than 3000 unique derivatives (∼44%) were identified tentatively by the m/z value and signal-to-control ratio in single-stage mass spectrometry (MS) analysis, with over 1000 being further characterized by tandem MS. The speed of the DESI-MS reaction screen provides potential advantages for emerging machine learning-based predictions of organic synthesis, and it sets the stage for future online DESI-MS bioassays and scaled-up microdroplet synthesis before formal characterization of hit compounds is sought using traditional methods of drug discovery.

Discovering CRISPR-Cas system with self-processing pre-crRNA capability by foundation models

The discovery of CRISPR-Cas systems has paved the way for advanced gene editing tools. However, traditional Cas discovery methods relying on sequence similarity may miss distant homologs and aren’t suitable for functional recognition. With protein large language models (LLMs) evolving, there is potential for Cas system modeling without extensive training data. Here, we introduce CHOOSER (Cas HOmlog Observing and SElf-processing scReening), an AI framework for alignment-free discovery of CRISPR-Cas systems with self-processing pre-crRNA capability using protein foundation models. By using CHOOSER, we identify 11 Casλ homologs, nearly doubling the known catalog. Notably, one homolog, EphcCasλ, is experimentally validated for self-processing pre-crRNA, DNA cleavage, and trans-cleavage, showing promise for CRISPR-based pathogen detection. This study highlights an innovative approach for discovering CRISPR-Cas systems with specific functions, emphasizing their potential in gene editing.

CAnDOIT: Causal Discovery with Observational and Interventional Data from Time Series

The study of cause and effect is of the utmost importance in many branches of science, but also for many practical applications of intelligent systems. In particular, identifying causal relationships in situations that include hidden factors is a major challenge for methods that rely solely on observational data for building causal models. This article proposes CAnDOIT, a causal discovery method to reconstruct causal models using both observational and interventional time‐series data. The use of interventional data in the causal analysis is crucial for real‐world applications, such as robotics, where the scenario is highly complex and observational data alone are often insufficient to uncover the correct causal structure. Validation of the method is performed initially on randomly generated synthetic models and subsequently on a well‐known benchmark for causal structure learning in a robotic manipulation environment. The experiments demonstrate that the approach can effectively handle data from interventions and exploit them to enhance the accuracy of the causal analysis. A Python implementation of CAnDOIT is developed and is publicly available on GitHub: https://github.com/lcastri/causalflow.

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

Improving breast-cancer diagnostics: A study using AI-ML with computational prognostics and feature extraction

: Though and even though advancements are high during last two decades, yet ‘breast—cancer’ continues to be a sizable causal transience amongst females (womankind) globally. Whilst mammography has distinctly lowered death rates, detection and categorization and accuracy of breast sizes (forms in quantities) in mammograms remains argue. Thus we present a new method of detection and discovery of key region-of-interest(ROI) plus its labeling as natural usual ‘0’, benign ‘1’, or cancerous ‘2’ sizes. : For sorting of identified ‘ROIs’ as usual, benign or malicious exactly. : We use the removal of temporally sequential digital mammograms, merged via machine-learning (ML). We estimated feature-selection algorithmic techniques (ATs) over the novel database (data—set) comprises images~circa352 as of 88 diseased exactly noted by sites-of-mass. 98 feature extractions were done and classified by 8 dissimilar feature-selection ATs to detect the best discriminative features. We then computed 10classifiers via first in first out (FIFO) diseased plus a k-fold cross validation. In our computation, we observed the artificial neural nets (ANNs) appeared as the highest classifier with 99.5%accuracy, 0.98AUC (for label ‘0’), 0.92AUC (for label ‘1’, plus 0.94AUC (for class ‘2’). : Our findings showed significant progress in divergence (disparity) through existing advanced high-tech algorithms and techniques, which feature worth of using temporally successive mammographs in amalgamation, with advanced computational intelligence ML, aimed at the exact sorting of identified ‘ROIs’ as usual, benign or malicious. : Clinical significancy/ when clinically applied, this study improves the clinical prognosis precision of breast-cancer possibly steering to reliable diseased outcomes plus added personalized therapeutic attempts.

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.

Cavity-Based Discovery of New Fatty Acid Photodecarboxylases.

Light-dependent fatty acid photodecarboxylases (FAPs) hold significant potential for biotechnology, due to their capability to produce alka(e)nes directly from the corresponding (un)saturated natural fatty acids requiring light as the only reagent. This study expands the family of FAPs through cavity-based enzyme discovery methods. Thirty enzyme candidates with potential photodecarboxylation activity were identified by matching the cavities of four related template structures against the Protein Data Bank's flavoproteins, a library of proteins identified via the Foldseek Search Server, and homology models of sequences resulting from BLAST. Subsequent docking experiments narrowed this library to ten promising enzymes, which were expressed and assessed in vitro, identifying four photodecarboxylases. Out of these enzymes, the GMC oxidoreductase from Coccomyxa sp. Obi (CoFAP) was characterized in detail, which revealed high activity in the decarboxylation reactions of palmitic acid and octanoic acid and a broad pH tolerance (pH 6.5-9.5).

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.