Discovery Methods Research Articles

Untangling the complex mechanisms associated with Alzheimer's disease in elderly patients using high-throughput RNA sequencing data and next-generation knowledge discovery methods: Focus on potential gene signatures and drugs for dementia

Untangling the complex mechanisms associated with Alzheimer's disease in elderly patients using high-throughput RNA sequencing data and next-generation knowledge discovery methods: Focus on potential gene signatures and drugs for dementia

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.

F-CPI: A Multimodal Deep Learning Approach for Predicting Compound Bioactivity Changes Induced by Fluorine Substitution.

Fluorine (F) substitution is a common method of drug discovery and development. However, there are no accurate approaches available for predicting the bioactivity changes after F-substitution, as the effect of substitution on the interactions between compounds and proteins (CPI) remains a mystery. In this study, we constructed a data set with 111,168 pairs of fluorine-substituted and nonfluorine-substituted compounds. We developed a multimodal deep learning model (F-CPI). In comparison with traditional machine learning and popular CPI task models, the accuracy, precision, and recall of F-CPI (∼90, ∼79, and ∼45%) were higher than those of GraphDTA (∼86, ∼58, and ∼40%). The application of the F-CPI for the structural optimization of hit compounds against SARS-CoV-2 3CLpro by F-substitution achieved a more than 100-fold increase in bioactivity (IC50: 0.23 μM vs 28.19 μM). Therefore, the multimodal deep learning model F-CPI would be a veritable and effective tool in the context of drug discovery and design.

Using UX Testing to Optimize Discoverability of Non-traditional Resources

Objective – The accessibility of non-traditional resources presents ongoing challenges for users and librarians. This study investigates methods for optimizing metadata and the placement of search results to enhance the discoverability of these resources within library systems. Researchers conducted A/B testing to compare two features of Ex Libris Primo: the Resource Recommender and Discovery Import Profiles. The objective was to enhance user access to a broader range of informational assets beyond conventional collections. This study posed the research question: Is inclusion in the results list (Discovery Import Profiles) or are visually appealing advertisement-style cards above results (Resource Recommender) a more effective method for discovery of non-traditional library resources? Methods – Researchers identified four key resource types for testing: librarians, frequently asked questions (FAQs), databases, and research guides. An A/B test was conducted with each resource presented in the Discovery Import Profiles and Resource Recommender formats. Following the A/B test, a combined C test was conducted to validate findings. Results – The ad-style cards achieved higher engagement rates, particularly for databases and FAQs, while research guides performed better when embedded directly in search results. This study highlights the strengths and limitations of each method. Databases and FAQs benefited from the visual prominence of the ad-style cards, while research guides were more discoverable within search results. However, minimal engagement with librarians as a resource type across both methods suggests the need for improved tagging and metadata strategies. Conclusion – Findings underscore the importance of institution-specific research and localized assessments to ensure effective implementation of discovery strategies. This study provides a useful method for libraries aiming to enhance the discoverability of their non-traditional resources, ultimately improving user access and satisfaction.

The Application of Machine Learning on Antibody Discovery and Optimization

Antibodies play critical roles in modern medicine, serving as diagnostics and therapeutics for various diseases due to their ability to specifically bind to target antigens. Traditional antibody discovery and optimization methods are time-consuming and resource-intensive, though they have successfully generated antibodies for diagnosing and treating diseases. The advancements in protein data, computational hardware, and machine learning (ML) models have the opportunity to disrupt antibody discovery and optimization research. Machine learning models have demonstrated their abilities in antibody design. These machine learning models enable rapid in silico design of antibody candidates within a few days, achieving approximately a 60% reduction in time and a 50% reduction in cost compared to traditional methods. This review focuses on the latest machine learning-based antibody discovery and optimization developments. We briefly discuss the limitations of traditional methods and then explore the machine learning-based antibody discovery and optimization methodologies. We also focus on future research directions, including developing Antibody Design AI Agents and data foundries, alongside the ethical and regulatory considerations essential for successfully adopting machine learning-driven antibody designs.

Principal Role of EPR Method in Discovery, Identification and Study of Dinitrosyl Iron Complexes in Living Organisms (Review)

Principal Role of EPR Method in Discovery, Identification and Study of Dinitrosyl Iron Complexes in Living Organisms (Review)

Expedited SARS‐CoV‐2 Main Protease Inhibitor Discovery through Modular ‘Direct‐to‐Biology’ Screening

AbstractReactive fragment (RF) screening has emerged as an efficient method for ligand discovery across the proteome, irrespective of a target's perceived tractability. To date, however, the efficiency of subsequent optimisation campaigns has largely been low‐throughput, constrained by the need for synthesis and purification of target compounds. We report an efficient platform for ‘direct‐to‐biology’ (D2B) screening of cysteine‐targeting chloroacetamide RFs, wherein synthesis is performed in 384‐well plates allowing direct assessment in downstream biological assays without purification. Here, the developed platform was used to optimise inhibitors of SARS‐CoV‐2 main protease (MPro), an established drug target for the treatment of COVID‐19. An initial RF hit was developed into a series of potent inhibitors, and further exploration using D2B screening enabled a ‘switch’ to a reversible inhibitor series. This example of ligand discovery for MPro illustrates the acceleration that D2B chemistry can offer for optimising RFs towards covalent inhibitor candidates, as well as providing future impetus to explore the evolution of RFs into non‐covalent ligands.

Expedited SARS-CoV-2 Main Protease Inhibitor Discovery through Modular 'Direct-to-Biology' Screening.

Reactive fragment (RF) screening has emerged as an efficient method for ligand discovery across the proteome, irrespective of a target's perceived tractability. To date, however, the efficiency of subsequent optimisation campaigns has largely been low-throughput, constrained by the need for synthesis and purification of target compounds. We report an efficient platform for 'direct-to-biology' (D2B) screening of cysteine-targeting chloroacetamide RFs, wherein synthesis is performed in 384-well plates allowing direct assessment in downstream biological assays without purification. Here, the developed platform was used to optimise inhibitors of SARS-CoV-2 main protease (MPro), an established drug target for the treatment of COVID-19. An initial RF hit was developed into a series of potent inhibitors, and further exploration using D2B screening enabled a 'switch' to a reversible inhibitor series. This example of ligand discovery for MPro illustrates the acceleration that D2B chemistry can offer for optimising RFs towards covalent inhibitor candidates, as well as providing future impetus to explore the evolution of RFs into non-covalent ligands.

Classification of Test Pads from Clustered PCB images

A robotic flying probe tester can be programmed to move the robotic probe to access all possible locations of test pads in a printed circuit board (PCB), and to record all connection test results like open or short circuits between all possible pairs of test pads in the board. For this purpose, Tan and Kit performed a clustering-based image cluster analysis on the photo image data of printed circuit boards to recover all test pad locations on the board and reported successful results. Their clustered data has been open to the public since 2024. So in this paper, several classification techniques for human comprehension were applied to give the robotic flying probe tester the location of test pads. As the final results of clustering were reviewed and corrected by experts in the original paper, we created machine learning results of classification that are easy for humans to understand, so that it could be easier to review the machine learning results before giving them to the robotic flying probe tester as input. For the classification task, we focused on knowledge discovery methods that can give the coordinates of the grey or test pad to a robot and are readable by humans. Decision trees and rules have the advantage of being relatively easy to understand because the knowledge models are expressed in a single tree structure or a set of rules, so they are widely accepted in the fields where the interpretation of trained knowledge models is important. Three different decision trees and two kinds of rule sets were constructed - J48, Random tree, REP tree (Reduced Error Pruning tree) for the decision trees, and JRIP and PART (PARtial decision Tree) for the rule sets. The accuracy of all four generated knowledge models is 100% except that of the REP tree which is 99.9997%. The size of the generated decision trees was relatively very small compared to the size of the data, 723,552 records, and the generated rule set by JRIP has only two rules. Therefore, we can conclude that the decision trees and the sets of rules for determining the test pads in the PCB have produced very successful results in terms of comprehensibility and accuracy.

Design and Management of Digital Communication Media to Build Stakeholder Trust and Reputation of the Indonesian Ikaboga Organization

Ikatan Ahli Boga Indonesia (Ikaboga Indonesia) was established in 1987. It is the first and oldest organization for Indonesian Culinary Experts. This organization is neither commercial nor political. They are currently experiencing challenges such as the weak commitment of its members, especially in terms of setting aside time, energy, and materials. As a result, many organizational tasks are not carried out according to their duties. This organization is also weak in managing digital communication media. In the digital era, many organizations compete to build stakeholder trust and reputation through digital media. This is the background to this PKM. By paying attention to the three main principles of the SDGs, namely universal, integration, and inclusive, this PKM focuses on and aims to improve the skills (technology and management) of the administrators and members of Ikaboga Indonesia. Such as skills in organizational communication management and the design and management of digital communication media to build stakeholder trust and the reputation of Ikaboga Indonesia. This program is carried out by elaborating ten methods: learning methods, question and answer and discussion methods, demonstration methods, problem-solving methods, practice or skills training methods, design methods, discovery methods, inquiry methods, mind mapping methods, and role-sharing methods. As a result, this activity was positively influenced; 71% of participants attended because they were interested and wanted to learn. This PKM was also considered to impact 81.2% of participants' cognitive changes significantly. The causal factors did not have a 100% effect on increasing participants' cognitive abilities due to obstacles of 56.5% lost and intermittent sound due to signal.; 43.5% had a wrong signal.; 21.77% due to distance obstacles, and 17.4% due to technological barriers, hunger, and other work, so they could not focus.

Lead Discovery and Lead Optimization: A Useful Strategy in Molecular Modification of Lead Compound in Analog Design

The current trend in the drug design is to develop new clinically effective agents through the molecular modification of a lead nucleus. A lead compound in drug discovery is a chemical compound that has pharmacological or biological activity. Lead optimization is the synthetic modification of a biologically active compound, to fulfill all stereoelectronic, physicochemical, pharmacokinetic and toxicologic required for clinical usefulness. The main objective of this review is to discuss the methods of lead discovery, lead optimization and its role in molecular modification of lead compound in analog design.

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.

Optimizing Hepatitis C Virus Inhibitor Identification with LightGBM and Tree-structured Parzen Estimator Sampling

Identifying potent inhibitors against the Hepatitis C Virus (HCV) is crucial due to the continuous emergence of drug-resistant strains. Traditional drug discovery methods, including high-throughput screening, are often resource-intensive and time-consuming. Machine Learning (ML) approaches, particularly Quantitative Structure-Activity Relationship modeling, have been increasingly adopted to address this. This study utilized LightGBM, an efficient gradient-boosting framework, to predict the activity of potential HCV inhibitors. Additionally, the Tree-structured Parzen Estimator (TPE) was employed for hyperparameter optimization to enhance model performance. The optimized LightGBM-TPE model outperformed other ML models, including standard LightGBM, XGBoost, Random Forest, K-Nearest Neighbors, and Support Vector Machines, achieving an accuracy of 86.27%, a precision of 85.47%, a recall of 87.50%, a specificity of 85.03%, and an F1-score of 86.47%. Feature importance analysis identified critical molecular descriptors contributing to the model's predictive power. The results underscore the potential of advanced ML techniques and robust optimization methods to accelerate drug discovery, particularly for challenging targets such as HCV.

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.

Opportunities and challenges of single-cell and spatially resolved genomics methods for neuroscience discovery.

Over the past decade, single-cell genomics technologies have allowed scalable profiling of cell-type-specific features, which has substantially increased our ability to study cellular diversity and transcriptional programs in heterogeneous tissues. Yet our understanding of mechanisms of gene regulation or the rules that govern interactions between cell types is still limited. The advent of new computational pipelines and technologies, such as single-cell epigenomics and spatially resolved transcriptomics, has created opportunities to explore two new axes of biological variation: cell-intrinsic regulation of cell states and expression programs and interactions between cells. Here, we summarize the most promising and robust technologies in these areas, discuss their strengths and limitations and discuss key computational approaches for analysis of these complex datasets. We highlight how data sharing and integration, documentation, visualization and benchmarking of results contribute to transparency, reproducibility, collaboration and democratization in neuroscience, and discuss needs and opportunities for future technology development and analysis.

Molecular Chaperones to Pain Modulators: Heat Shock Proteins the Emerging Players in Chronic Pain Management.

Chronic pain is the most prevailing complex syndrome among clinical disorders affecting approximately 30% of people globally. Various intricate alterations in nociceptive pathways responsible for chronic pain are linked to long-term tissue damage or injury to the peripheral or central nervous systems. These include remolding in the phenotype of cells and fluctuations in the expression of proteins such as ion channels, neurotransmitters, and receptors. Heat shock proteins are important molecular chaperone proteins in cell responses to stress, including inflammation, neurodegeneration, and pain signaling. They play a key role in activating glial and endothelial cells and in the production of inflammatory mediators and excitatory amino acids in both peripheral and central nervous systems. In particular, they contribute to central sensitization and hyperactivation within the dorsal horn of the spinal cord. The expression of some HSPs plays a remarkable role in upregulating pain response by acting as scavengers of ROS, controlling inflammatory cytokines. Different HSPs act by different mechanisms and several important pathways have been implicated in targeting HSPs for the treatment of neuropathic pain including p38-mitogen-activated protein kinases (MAPKs), extracellular signal-regulated kinases (ERKs), brain-derived neurotrophic factors (BDNF). We summarize the role of HSPs in various preclinical and clinical studies and the crosstalk of HSPs with various nociceptors and other pain models. We also highlighted some artificial intelligence tools and machine learning-assisted drug discovery methods for rapid screening of HSPs in various diseases. Thus, targeting HSPs may provide new therapeutic opportunities for the management of chronic pain conditions and expand our understanding of pain in neurological disorders and other pathological conditions.

A generative artificial intelligence approach for antibiotic optimization.

Antimicrobial resistance (AMR) poses a critical global health threat, underscoring the urgent need for innovative antibiotic discovery strategies. While recent advances in peptide design have yielded numerous antimicrobial agents, optimizing these molecules experimentally remains challenging due to unpredictable and resource-intensive trial-and-error approaches. Here, we introduce APEX Generative Optimization (APEX GO ), a generative artificial intelligence (AI) framework that integrates a transformer-based variational autoencoder with Bayesian optimization to design and optimize antimicrobial peptides. Unlike traditional supervised learning approaches that screen fixed databases of existing molecules, APEX GO generates entirely novel peptide sequences through arbitrary modifications of template peptides, representing a paradigm shift in peptide design and antibiotic discovery. Our framework introduces a new peptide variational autoencoder with design and diversity constraints to maintain similarity to specific templates while enabling sequence innovation. This work represents the first in vitro and in vivo experimental validation of generative Bayesian optimization in any setting. Using ten de-extinct peptides as templates, APEX GO generated optimized derivatives with enhanced antimicrobial properties. We synthesized 100 of these optimized peptides and conducted comprehensive in vitro characterizations, including assessments of antimicrobial activity, mechanism of action, secondary structure, and cytotoxicity. Notably, APEX GO achieved an outstanding 85% ground-truth experimental hit rate and a 72% success rate in enhancing antimicrobial activity against clinically relevant Gram-negative pathogens, outperforming previously reported methods for antibiotic discovery and optimization. In preclinical mouse models of Acinetobacter baumannii infection, several AI-optimized molecules-most notably derivatives of mammuthusin-3 and mylodonin-2-exhibited potent anti-infective activity comparable to or exceeding that of polymyxin B, a widely used last-resort antibiotic. These findings highlight the potential of APEX GO as a novel generative AI approach for peptide design and antibiotic optimization, offering a powerful tool to accelerate antibiotic discovery and address the escalating challenge of AMR.

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.

TG-CDDPM: text-guided antimicrobial peptides generation based on conditional denoising diffusion probabilistic model.

Antimicrobial peptides (AMPs) have emerged as a promising substitution to antibiotics thanks to their boarder range of activities, less likelihood of drug resistance, and low toxicity. Traditional biochemical methods for AMP discovery are costly and inefficient. Deep generative models, including the long-short term memory model, variational autoencoder model, and generative adversarial model, have been widely introduced to expedite AMP discovery. However, these models tend to suffer from the lack of diversity in generating AMPs. The denoising diffusion probabilistic model serves as a good candidate for solving this issue. We proposed a three-stage Text-Guided Conditional Denoising Diffusion Probabilistic Model (TG-CDDPM) to generate novel and homologous AMPs. In the first two stages, contrastive learning and inferring models are crafted to create better conditions for guiding AMP generation, respectively. In the last stage, a pre-trained conditional denoising diffusion probabilistic model is leveraged to enrich the peptide knowledge and fine-tuned to learn feature representation in downstream. TG-CDDPM was compared to the state-of-the-art generative models for AMP generation, and it demonstrated competitive or better performance with the assistance of text description as supervised information. The membrane penetration capabilities of the identified candidate AMPs by TG-CDDPM were also validated through molecular weight dynamics experiments.