Entity Search Research Articles

VAIV bio-discovery service using transformer model and retrieval augmented generation

BackgroundThere has been a considerable advancement in AI technologies like LLM and machine learning to support biomedical knowledge discovery.Main bodyWe propose a novel biomedical neural search service called ‘VAIV Bio-Discovery’, which supports enhanced knowledge discovery and document search on unstructured text such as PubMed. It mainly handles with information related to chemical compound/drugs, gene/proteins, diseases, and their interactions (chemical compounds/drugs-proteins/gene including drugs-targets, drug-drug, and drug-disease). To provide comprehensive knowledge, the system offers four search options: basic search, entity and interaction search, and natural language search. We employ T5slim_dec, which adapts the autoregressive generation task of the T5 (text-to-text transfer transformer) to the interaction extraction task by removing the self-attention layer in the decoder block. It also assists in interpreting research findings by summarizing the retrieved search results for a given natural language query with Retrieval Augmented Generation (RAG). The search engine is built with a hybrid method that combines neural search with the probabilistic search, BM25.ConclusionAs a result, our system can better understand the context, semantics and relationships between terms within the document, enhancing search accuracy. This research contributes to the rapidly evolving biomedical field by introducing a new service to access and discover relevant knowledge.

Synthesis and biological profile of substituted hexahydrofuro[3,4-b]furans, a novel class of bicyclic acyl-acyl carrier protein (ACP) thioesterase inhibitors.

Weed control is a significant challenge for farmers around the globe. Of the various methods available for combatting weeds, small molecules remain the most effective and versatile technology to date. In the search for novel chemical entities with new modes of action toward herbicide-resistant weeds, we have investigated hexahydrofuro[3,4-b]furan-based acyl-acyl carrier protein (ACP) thioesterase inhibitors inspired by X-ray co-crystal structure-based modeling studies. By exploiting scaffold hopping concepts and molecular modeling studies we were able to identify new hexahydrofuro[3,4-b]furan-based lead structures showing promising activity in vivo against commercially important grass weeds in line with strong target affinity. The present work covers a series of novel herbicidal lead structures that possess a hexahydrofuro[3,4-b]furan scaffold as a structural key feature, carrying ortho-substituted aryloxy side chains. Based on an optimized synthetic approach a broad structure-activity relationship (SAR) study was carried out. The new compounds emerging from our modeling-inspired structural variations show good acyl-ACP thioesterase inhibition in line with promising initial herbicidal activity. Glasshouse trials showed that the hexahydrofuro[3,4-b]furans outlined herein display good control of cold and warm season grass-weed species in pre-emergence application. Remarkably, some of the novel acyl-ACP thioesterase-inhibitors also showed promising efficacy against warm season weeds that are difficult to control. © 2024 The Author(s). Pest Management Science published by John Wiley & Sons Ltd on behalf of Society of Chemical Industry.

Fragment-based virtual screening identifies novel leads against Plasmepsin IX (PlmIX) of Plasmodium falciparum: Homology modeling, molecular docking, and simulation approaches

Despite continuous efforts to develop safer and efficient medications, malaria remains a major threat posing great challenges for new drug discovery. The emerging drug resistance, increased toxicities, and impoverished pharmacokinetic profiles exhibited by conventional drugs have hindered the search for new entities. Plasmepsins, a group of Plasmodium-specific, aspartic acid protease enzymes, are involved in many key aspects of parasite biology, and this makes them interesting targets for antimalarial chemotherapy. Among different isoforms, PlmIX serves as an unexplored antimalarial drug target that plays a crucial role along with PlmV and X in the parasite’s survival by digesting hemoglobin in the host’s erythrocytes. In this study, fragment-based virtual screening was performed by modeling the three-dimensional structure of PlmIX and predicting its ligand-binding pocket by using the Sitemap tool. Screening identified the fragments with the XP docking score ≤ −3 kcal/mol from the OTAVA General Fragment Library (≈16,397 fragments), and the selected fragments were chosen for ligand breeding. The resulting ligands (≈69,858 ligands) were subsequently subjected to filtering based on the QikProp properties along with carcinogenicity testing performed using CarcinoPred-EL and then docked in the SP (≈14,078 ligands) as well as XP mode (≈3,104 ligands), and compared with that of control ligands 49C and I0L. The top-ranked ligands were taken further for the calculation of the free energy of binding using Prime MM–GBSA. Overall, a total of six complexes were taken further for MD simulation studies performed at 100 ns to attain a better understanding of the binding mechanisms, and compounds 3 and 4 were found to be the most efficient ones in silico. The analysis of compound 3 revealed that the carbonyl group present in position 1 on the isoindoline moiety (Arg554) was responsible for inhibitory activity against PlmIX. However, the analysis of compound 4 revealed that the amide linkage sandwiched between the phenyl ring and isoquinoline moiety (Lys555 and Ser226) as well as carbonyl oxygen of the carbamoyl group present at position 2 of the pyrazole ring (Gln222) were responsible for PlmIX inhibitory activity, owing to their crucial interactions with key amino acid residues.

Therapeutic Values of Pyridine Molecules from Natural Sources: A Comprehensive Review

Reason for the Study: Many commercial pharmacologically interesting medicinal plant species and their formulations are used in more communities and often in more countries around the world, for their multiple uses of Active compounds from the natural sources. It should be need to extensively explored to get their properties and their benefits. The costs of drugs for resistance of common infective diseases are increased especially bacterial infections and sexually transmitted diseases. The therapeutic approach of herbal medicines is an option for concerted search for new chemical entities for new drugs development. So searching of valuable medicinal plants with their longest track record for their use and their location and distribution is must and essential.
 Aim: This study aims to provide an overview and documentation about pyridine alkaloids and their phytopharmacological activity related some medicinal plants.
 Methodology: By using the key words, the literatures were collected from Science Direct, PubMed and Google Scholar search engines. This review will create a platform to harmonizing the traditional medicine practice in the country, create a synergy between herbal medicines and modern medicine and more harmonized integrated traditional medicine practices in future. It gives an insight into the strategic plan and route map for the development of new formulations and research platform for the practice and development of herbal medicines. The pharmaceutical industry has come to consider traditional medicine as a source for identification of bio-active agents that can be used in the preparation of semi synthetic medicine in different novel formulations.
 Conclusion: This article focused some medicinal plants which contain different types and derivatives of pyridine alkaloids with their therapeutic applications. Further research needs to be done to make Novel pharmaceutical preparations with patent drugs and appropriate therapeutic documentation.

Метод связывания именованных сущностей в тексте с понятиями базы знаний Wikidata.

The paper presents a method for automatically linking named entities in Russian-language texts with concepts from the Wikidata knowledge base. It is based on the use of named entity search tools with subsequent semantic analysis of the correspondence of the found entity to the concept in the knowledge base. The resulting links can later be used to form a linked corpus of texts in any subject area. The difference between the presented method and existing ones is the analysis of both the named entity itself, its attributes, as well as associated words without the use of machine learning methods. This approach allows to increase the accuracy of searching for a corresponding concept in the knowledge base and eliminates the need to constantly retrain the neural network model to recognize new entities added to the knowledge base.

Discovery and optimization of spirocyclic lactams that inhibit acyl-ACP thioesterase.

There are various methods to control weeds, that represent considerable challenges for farmers around the globe, although applying small molecular compounds is still the most effective and versatile technology to date. In the search for novel chemical entities with new modes-of-action that can control weeds displaying resistance, we have investigated two spirocyclic classes of acyl-ACP thioesterase inhibitors based on X-ray co-crystal structures and subsequent modelling studies. By exploiting scaffold-hopping and isostere concepts, we were able to identify new spirolactam-based lead structures showing promising activity in vivo against commercially important grass weeds in line with strong target affinity. The present work covers a series of novel herbicidal lead structures that contain a spirocyclic lactam as a structural key feature carrying ortho-substituted benzyl or heteroarylmethylene side chains. These new compounds show good acyl-ACP thioesterase inhibition in line with strong herbicidal activity. Glasshouse trials showed that the spirolactams outlined herein display promising control of grass-weed species in pre-emergence application combined with dose-response windows that enable partial selectivity in wheat and corn. Remarkably, some of the novel acyl-ACP thioesterase-inhibitors showed efficacy against resistant grass weeds such as Alopecurus myosuroides and Lolium spp. on competitive levels compared with commercial standards. © 2024 Society of Chemical Industry.

Polyamine Metabolism for Drug Intervention in Trypanosomatids.

Neglected tropical diseases transmitted by trypanosomatids include three major human scourges that globally affect the world's poorest people: African trypanosomiasis or sleeping sickness, American trypanosomiasis or Chagas disease and different types of leishmaniasis. Different metabolic pathways have been targeted to find antitrypanosomatid drugs, including polyamine metabolism. Since their discovery, the naturally occurring polyamines, putrescine, spermidine and spermine, have been considered important metabolites involved in cell growth. With a complex metabolism involving biosynthesis, catabolism and interconversion, the synthesis of putrescine and spermidine was targeted by thousands of compounds in an effort to produce cell growth blockade in tumor and infectious processes with limited success. However, the discovery of eflornithine (DFMO) as a curative drug against sleeping sickness encouraged researchers to develop new molecules against these diseases. Polyamine synthesis inhibitors have also provided insight into the peculiarities of this pathway between the host and the parasite, and also among different trypanosomatid species, thus allowing the search for new specific chemical entities aimed to treat these diseases and leading to the investigation of target-based scaffolds. The main molecular targets include the enzymes involved in polyamine biosynthesis (ornithine decarboxylase, S-adenosylmethionine decarboxylase and spermidine synthase), enzymes participating in their uptake from the environment, and the enzymes involved in the redox balance of the parasite. In this review, we summarize the research behind polyamine-based treatments, the current trends, and the main challenges in this field.

A Study in Scaffold Hopping: Discovery and Optimization of Thiazolopyridines as Potent Herbicides That Inhibit Acyl-ACP Thioesterase.

In the search for new chemical entities that can control resistant weeds by addressing novel modes of action (MoAs), we were interested in further exploring a compound class that contained a 1,8-naphthyridine core. By leveraging scaffold hopping methodologies, we were able to discover the new thiazolopyridine compound class that act as potent herbicidal molecules. Further biochemical investigations allowed us to identify that the thiazolopyridines inhibit acyl-acyl carrier protein (ACP) thioesterase (FAT), with this being further confirmed via an X-ray cocrystal structure. Greenhouse trials revealed that the thiazolopyridines display excellent control of grass weed species in pre-emergence application coupled with dose response windows that enable partial selectivity in certain crops.

Development of 1-(2-aminophenyl)pyrrole-based amides acting as human topoisomerase I inhibitors.

Topoisomerases are ubiquitous enzymes in the human body, particularly involved in cancer development and progression. Topoisomerase I (topoI) performs DNA relaxation reactions by "controlled rotation" rather than by "strand passage." The inhibition of topoI has become a useful strategy to control cancer cell proliferation. Nowadays, different compounds have undergone clinical trials, but the search for new molecular entities is necessary and benefits from medicinal chemistry efforts. Pyrrole-based compounds emerged as promising antiproliferative agents, with particular interest in breast cancer therapy and topoI inhibition. Starting from these observations and based on the scaffold-hopping approach, we developed a small library of 1-(2-aminophenyl)pyrrole-based amides (7a-f) as new anticancer agents. Tested on a panel of cancer cell lines, 7a-f displayed the most interesting profile in MDA-MB-231 cells, where the most active compounds, 7d-f, were able to induce death by apoptosis. Direct enzymatic assays and docking simulations on the topoI active site (PDB: 1A35) revealed the inhibitory activity and potential binding site for the newly developed 1-(2-aminophenyl)pyrrole-based amides.

Efficient and Privacy-Preserving Search Over Edge–Cloud Collaborative Entity in IoT

Limited by the storage and computing capacity of Internet of Things (IoT) devices, outsourcing encrypted entity data has become a prevalent trend. The existing IoT entity search methods lack the integration and utilization of both edge and cloud resources and the protection of user privacy. Besides, the traditional searchable encryption mode is inapplicable to state-time-varying entities in IoT. Therefore, in this article, an edge–cloud collaborative entity search method with privacy protection in IoT is proposed, fusing the advantages of edge and cloud resources to fulfill users’ needs for real-time search and privacy protection. Specifically, a secure search architecture and search method for edge–cloud collaboration is designed to support various needs of users, such as real-time search and global search. Then, an adaptive discrimination method for similar interested entities through attribute analysis and feature extraction is proposed to construct attribute-distinguished encrypted index and query vector groups, enabling efficient entity search meanwhile ensuring fast index update. Simulation results demonstrate that the proposed method with privacy protection can effectively improve the efficiency of entity search in IoT while safeguarding user privacy.

A study of essential computational software in medicinal chemistry

Computational software tools are now routinely used to investigate the structure, dynamics, surface properties, and thermodynamics of inorganic, biological, and polymeric systems. These tools are a vital part of the guide for drug discovery and are widely used in rational drug design and structure-based drug design processes. The process of drug design and discovery is essential in the invention of a new chemical entity. For this process, plenty of computational tools are available globally. These computational software tools are fast, free, open online access, and paid. Pharmaceutical software decreases human efforts, errors, and time utilization in a particular task without compromising the quality of work, with great accuracy and efficiency. This software is utilized by various institutes globally related to science and medicine. A computer application applies a library of medicinal chemical transformation rules to an input structure and then permits evaluation of the resulting output structures. Computer-Aided Drug Design (CADD) is now widely recognized as a viable alternative and complements high throughput screening. The search for new molecular entities has led to the construction of high-quality datasets and design libraries that may be optimized for molecular diversity or similarity. Conversely, advances in molecular docking algorithms, combined with improvements in computational infrastructure, are enabling rapid improvement in screening throughput.

The Diversity of Deep-Sea Actinobacteria and Their Natural Products: An Epitome of Curiosity and Drug Discovery

Bioprospecting of novel antibiotics has been the conventional norm of research fostered by researchers worldwide to combat drug resistance. With the exhaustion of incessant leads, the search for new chemical entities moves into uncharted territories such as the deep sea. The deep sea is a furthermost ecosystem with much untapped biodiversity thriving under extreme conditions. Accordingly, it also encompasses a vast pool of ancient natural products. Actinobacteria are frequently regarded as the bacteria of research interest due to their inherent antibiotic-producing capabilities. These interesting groups of bacteria occupy diverse ecological habitats including a multitude of different deep-sea habitats. In this review, we provide a recent update on the novel species and compounds of actinomycetes from the deep-sea environments within a period of 2016–2022. Within this period, a total of 24 new species of actinomycetes were discovered and characterized as well as 101 new compounds of various biological activities. The microbial communities of various deep-sea ecosystems are the emerging frontiers of bioprospecting.

Inspection of in-house designed novel thiochromone amino-acid conjugate derivatives as Lm-NMT inhibitor – An in-silico analysis

Leishmaniasis is a complex neglected tropical disease caused by various leishmanial parasites that primarily affect the world's poorest people. A limited number of standard medications are available for this disease that has been used for several decades, which have drawbacks such as resistance, higher cost, and patient compliance, making it difficult to reach the poor. The search for novel chemical entities to treat leishmaniasis has led to target-based scaffold research. Thiochromone moieties in conjugation with aromatic amino acids have been considered for the study, along with possible substitutions of the electron-withdrawing and electron-donating groups. N-myristoyl transferase (NMT) has been selected as the molecular target for the study responsible for protein-protein interaction and ribosylation of proteins necessary for the growth inside the human body of the parasite. The designed novel thiochromone analogs were docked against the selected leishmanial NMT using thein-silico methods, physicochemical and toxicity properties were predicted, and Structure-Activity Relationship was also established in-silico. Finally, a molecular dynamics simulation study for 100 ns gave an idea about the stability of the protein-ligand complex. A time frame analysis of each 10 ns confirmation was also studied to understand better the putative binding pattern designed analogs.

Exploration of Chemopreventive Potential of Linalool in Targeting Lung Cancer Biomarkers.

Phytochemicals are used to treat lung cancer in contemporary and traditional medicine. The limitations of known chemotherapeutic drugs such as non-specificity, resistance, and toxicity restrict their use for lung cancer treatment. Therefore, the search for target-specific novel entities is required continuously. Linalool, a monoterpene alcohol that possesses antiviral, anti-inflammatory, and antibacterial properties, is present in sweet basil, laurel, jasmine, rosewood, and lavender. Previous reports revealed its anticancer potential against colon, breast, and liver cancer. In this study, linalool's efficacy in targeting biomarkers associated with different lung cancer stages has been investigated. The in silico molecular docking analysis was used to explore drug-receptor interaction, and further, linalools cytotoxicity potential was evaluated on lung adenocarcinoma cell line (A549). The toxicity profiling of linalool was done by ADMET analysis. In the results, Linalool revealed an excellent binding affinity with the selected targets. It showed the highest interaction with BRAF with the binding energy of -5.6 kcal/mol. Furthermore, it successfully interacts within the binding pocket of BRAF, similar to its inhibitor (Sorafenib). In MTT analysis, linalool significantly reduces the percent viability IC30 474.94 ± 43.12, 379.33 ± 49.5, and 183.77 ± 66.7 μM in A549 cell lines for 24, 48, and 72 h, respectively. These results concluded that linalool possesses chemopreventive potential against lung cancer by interacting or modulating selected biomarkers associated with a lung cancer diagnosis, progression, and proliferation.

Adaptive preference transfer for personalized IoT entity recommendation

• An IoT entity recommendation method based on adaptive preference transfer is proposed. • A lightweight user feedback feature embedding method is proposed to solve the problem of sparse user feedback. • The rigorous experiments are designed to verify the validity of the proposed method. Internet of Things (IoT) recommendation can effectively improve the convenience and intelligence of users in obtaining information about interested physical entities in a huge entity search space. However, existing transfer learning-based recommendation methods mainly focus on Internet resources and services, lack personalized transfer weight consideration for different user preferences, and fail to address the entity rating matrix sparsity problem, resulting in limited IoT entity recommendation performance. Thus, we design an adaptive preference transfer IoT entity (APTE) recommendation method in this paper. Firstly, considering various user preference characteristics, an adaptive dual-domain transfer model for item domain and social domain is designed to meet users’ personalized requirements. Then, a lightweight user feedback embedding method is proposed to mine the explicit and implicit features and to embed the entity rating matrix to solve the problem of sparse user feedback information. Simulation results demonstrate that APTE can effectively improve the recommendation performance compared with state-of-the-art baselines.

The Microsoft Academic Knowledge Graph enhanced: Author name disambiguation, publication classification, and embeddings

Abstract Although several large knowledge graphs have been proposed in the scholarly field, such graphs are limited with respect to several data quality dimensions such as accuracy and coverage. In this article, we present methods for enhancing the Microsoft Academic Knowledge Graph (MAKG), a recently published large-scale knowledge graph containing metadata about scientific publications and associated authors, venues, and affiliations. Based on a qualitative analysis of the MAKG, we address three aspects. First, we adopt and evaluate unsupervised approaches for large-scale author name disambiguation. Second, we develop and evaluate methods for tagging publications by their discipline and by keywords, facilitating enhanced search and recommendation of publications and associated entities. Third, we compute and evaluate embeddings for all 239 million publications, 243 million authors, 49,000 journals, and 16,000 conference entities in the MAKG based on several state-of-the-art embedding techniques. Finally, we provide statistics for the updated MAKG. Our final MAKG is publicly available at https://makg.org and can be used for the search or recommendation of scholarly entities, as well as enhanced scientific impact quantification.

HiveRel: hexagons visualization for relationship-based knowledge acquisition

The growing abundance in complex network data models is constantly increasing the challenges for non-expert users who perform an effective exploratory search in large data collections. In such domains, users search for entities related to a topic of interest and acquire knowledge by investigating the relationships between these entities. Designers, in turn, are challenged by the need to provide tools that enable convenient search and exploration to facilitate productive performance on the task. For this purpose, we introduce HiveRel, a search system that presents search results as tiled hexagons on a map-like surface with center-out relevance ordering and allows on-demand display of relationships between search results. HiveRel’s user interface is based on theoretical principles that reflect how users acquire knowledge through relationships. For the search mechanism, we provide a set of information retrieval definitions leading to the formalization of the Maximal n-Bounded Exploration Subgraph problem and present an implementation of a greedy heuristic algorithm that provides non-optimal solutions to this problem. We develop a proof of concept version of HiveRel. We evaluate it in two user studies that compare users’ performance using HiveRel to standard web search over a range of search knowledge acquisition tasks and two different domains. The results indicate that despite the lack of familiarity with the new system, users were generally more accurate and as fast using HiveRel, and provided positive evaluations for the search experience.

Active tag recommendation for interactive entity search: Interaction effectiveness and retrieval performance

We introduce active tag recommendation for interactive entity search, an approach that actively learns to suggest tags from preceding user interactions with the recommended tags. The approach utilizes an online reinforcement learning model and observes user interactions on the recommended tags to reward or penalize the model. Active tag recommendation is implemented as part of a realistic search engine indexing a large collection of movie data. The approach is evaluated in task-based user experiments comparing a complete search system enhanced with active tag recommendation to a control system in which active tag recommendation is not available. In the experiment, participants (N = 45) performed search tasks on the movie domain and the corresponding search interactions, information selections, and entity rankings were logged and analyzed. The results show that active tag recommendation (1) improves the ranking of entities compared to written-query interaction, (2) increases the amount of interaction and effectiveness of interactions to rank entities that end up being selected in a task, and (3) reduces, but does not substitute, the need for written-query interaction (4) without compromising task execution time. The results imply that active learning for search support can help users to interact with entity search systems by reducing the need for writing queries and improve search outcomes without compromising the time used for searching.

A review of similar entity search research based on knowledge graph

In the era of big data, how to help users accurately filter out effective information from a large amount of digital information is the current challenge facing search engines. Knowledge graph has richer semantic information and can effectively conduct more accurate object-level search, and it is a major change to apply knowledge graph to search engine. In this paper, we summarize the classification of similar entity measurement algorithms based on knowledge graphs, review the development history of similar entity measurement algorithms under different algorithm classifications, introduce typical algorithms and prospect the future development.

Sesquiterpene Lactones with Dual Inhibitory Activity against the Trypanosoma brucei Pteridine Reductase 1 and Dihydrofolate Reductase.

The parasite Trypanosoma brucei (T. brucei) is responsible for human African trypanosomiasis (HAT) and the cattle disease “Nagana” which to this day cause severe medical and socio-economic issues for the affected areas in Africa. So far, most of the available treatment options are accompanied by harmful side effects and are constantly challenged by newly emerging drug resistances. Since trypanosomatids are auxotrophic for folate, their pteridine metabolism provides a promising target for an innovative chemotherapeutic treatment. They are equipped with a unique corresponding enzyme system consisting of the bifunctional dihydrofolate reductase-thymidylate synthase (TbDHFR-TS) and the pteridine reductase 1 (TbPTR1). Previously, gene knockout experiments with PTR1 null mutants have underlined the importance of these enzymes for parasite survival. In a search for new chemical entities with a dual inhibitory activity against the TbPTR1 and TbDHFR, a multi-step in silico procedure was employed to pre-select promising candidates against the targeted enzymes from a natural product database. Among others, the sesquiterpene lactones (STLs) cynaropicrin and cnicin were identified as in silico hits. Consequently, an in-house database of 118 STLs was submitted to an in silico screening yielding 29 further virtual hits. Ten STLs were subsequently tested against the target enzymes in vitro in a spectrophotometric inhibition assay. Five compounds displayed an inhibition over 50% against TbPTR1 as well as three compounds against TbDHFR. Cynaropicrin turned out to be the most interesting hit since it inhibited both TbPTR1 and TbDHFR, reaching IC50 values of 12.4 µM and 7.1 µM, respectively.