Screening Of Candidates Research Articles

The SARS-CoV-2 Mpro Dimer-Based Screening System: A Synthetic Biology Tool for Identifying Compounds with Dimerization Inhibitory Potential.

The COVID-19 endemic remains a global concern. The search for effective antiviral candidates is still needed to reduce disease risk. However, the availability of high biosafety level laboratory facilities for drug screening is limited in number. To address this issue, a screening system that could be utilized at lower biosafety levels remains essential. This study aimed to develop a novel SARS-CoV-2 main protease (Mpro) dimer-based screening system (DBSS) utilizing synthetic biology in Escherichia coli BL21(DE3). We linked the SARS-CoV-2 Mpro with the DNA-binding domain of AraC regulatory protein, which regulates the reporter gene expression. Protein modeling and molecular docking showed that saquinavir could bind to AraC-Mpro both in its monomer and dimer forms. The constructed DBSS assay indicated the screening system could detect saquinavir inhibitory activity at a concentration range of 4-10 μg/mL compared to the untreated control (P ≤ 0.05). The Vero E6 cell assay validated the DBSS result that saquinavir at 4-10 μg/mL exhibited antiviral activity against SARS-CoV-2. Our DBSS could be used for preliminary screening of numerous drug candidates that possess a dimerization inhibitor activity of SARS-CoV-2 Mpro and also minimize the use of a high biosafety level laboratory.

Computational Screening for mAb Colloidal Stability with Coarse-Grained, Molecular-Scale Simulations.

Monoclonal antibodies (mAbs) are an important modality of protein therapeutics with broad applications for numerous diseases. However, colloidal instabilities occurring at high protein concentrations can limit the ability to develop stable, high-concentration liquid dosage forms that are required for patient-centric, device-mediated products. Therefore, it is advantageous to identify colloidally stable mAbs early in the discovery process to ensure that they are selected for development. Experimental screening for colloidal stability can be time- and resource-consuming and is most feasible at the later stages of drug development due to material requirements. Alternatively, computational approaches have emerging potential to provide efficient screening and focus developmental efforts on mAbs with the greatest developability potential, while providing mechanistic relationships for colloidal instability. In this work, coarse-grained, molecular-scale models were fine-tuned to screen for colloidal stability at amino-acid resolution. This model parameterization provides a framework to screen for mAb self-interactions and extrapolate to bulk solution behavior. This approach was applied to a wide array of mAbs under multiple buffer conditions, demonstrating the utility of the presented computational approach to augment early candidate screening and later formulation strategies for protein therapeutics.

The Potential of Salivary Biomarkers in Early Detection of Pancreatic Ductal Adenocarcinoma: A Systematic Review.

Pancreatic ductal adenocarcinoma (PDAC) is a formidable global health concern with a dire prognosis, highlighting the critical need for early detection strategies. This systematic review delves into the potential of salivary biomarkers as a non-invasive means for identifying PDAC at its incipient stages. Saliva's proximity to the circulatory system enables the detection of tumor-derived biomolecules, making it an ideal candidate for mass screening. The analysis of three selected studies reveals promising candidates such as Neisseria mucosa, Fusobacterium periodonticum, polyamines, and specific long non-coding RNAs (lncRNAs). Notably, polyamines like spermine show potential in distinguishing PDAC, while lncRNAs HOX transcript antisense RNA (HOTAIR) and plasmacytoma variant translocation 1 (PVT1) exhibit superior sensitivity and specificity compared to traditional serum markers. However, challenges, including small sample sizes and a lack of validation, underscore the need for standardized diagnostic panels and large-scale collaborative studies. Advancements in nanotechnology, machine learning, and ethical considerations are crucial for harnessing the diagnostic potential of saliva. The review emphasizes the imperative for extensive clinical trials to validate salivary biomarkers, ensuring not only diagnostic accuracy but also cost-effectiveness, patient compliance, and long-term benefits in the realm of PDAC screening. Longitudinal studies are recommended to unravel temporal changes in salivary biomarkers, shedding light on disease progression and treatment response.

TRANSFORMATION OF RECRUITMENT PROCESS THROUGH IMPLEMENTATION OF AI SOLUTIONS

This article reviews how the recruitment process across various industries has been transformed due to recent advancements in artificial intelligence (AI) solutions. A literature review summarizes key findings on AI-enabled improvements in sourcing, screening, assessment, interviewing, and onboarding of candidates. Both recruitment analytics using big data and conversational AI chatbots are discussed as drivers reshaping traditional human resource workflows. Challenges around AI bias, privacy, and job automation fears are also analyzed. Overall, AI implementation led to enhanced efficiency, quality, and experience in talent acquisition. However, maintaining a human touch remains vital, suggesting an optimal balance between AI and human recruiters.

Saliva direct PCR protocol for HLA-DQB1*02 genotyping.

Celiac disease (CD) is an immune disorder, that is triggered by gluten ingestion in genetically predisposed individuals. The HLA-DQB1*02 allele is the main predisposing genetic factor and a candidate for first-line genotyping screening. We designed and validated a simple, DNA purification-free PCR protocol directly from crude saliva, enabling the detection of the DQB1*02 allele. This assay also distinguishes homozygous from heterozygous carriers. We propose this method for use in mass screening and/or epidemiological studies.

Multiplexed effector screening for recognition by endogenous resistance genes using positive defense reporters in wheat protoplasts.

Plant resistance (R) and pathogen avirulence (Avr) gene interactions play a vital role in pathogen resistance. Efficient molecular screening tools for crops lack far behind their model organism counterparts, yet they are essential to rapidly identify agriculturally important molecular interactions that trigger host resistance. Here, we have developed a novel wheat protoplast assay that enables efficient screening of Avr/R interactions at scale. Our assay allows access to the extensive gene pool of phenotypically described R genes because it does not require the overexpression of cloned R genes. It is suitable for multiplexed Avr screening, with interactions tested in pools of up to 50 Avr candidates. We identified Avr/R-induced defense genes to create a promoter-luciferase reporter. Then, we combined this with a dual-color ratiometric reporter system that normalizes read-outs accounting for experimental variability and Avr/R-induced cell death. Moreover, we introduced a self-replicative plasmid reducing the amount of plasmid used in the assay. Our assay increases the throughput of Avr candidate screening, accelerating the study of cellular defense signaling and resistance gene identification in wheat. We anticipate that our assay will significantly accelerate Avr identification for many wheat pathogens, leading to improved genome-guided pathogen surveillance and breeding of disease-resistant crops.

Transformative Horizons: Navigating the Evolution of HR through AI and Cloud Technologies

Artificial intelligence (AI) and cloud computing are transforming human resources (HR) throughout the digital revolution. This white paper examines how these technologies are converging and how they affect HR practices. First, the report charts the evolution of HR technology from manual methods to complex digital ecosystems. This essay stresses the vital moment when AI and cloud technologies drive HR innovation, altering talent acquisition, employee engagement, performance management, and other areas. This article discusses AI’s position in HR and its applications throughout HR. Real-world case studies show how AI-powered recruitment improves candidate screening and predictive analytics informs HR decisions. Cloud HR solutions, fundamental to modern HR technology, are targeted. This article discusses cloud-based HR systems’ versatility, cost savings, and ability to adapt to changing demands. Additionally, this section features companies that have improved HR procedures using cloud technology. Cloud platforms provide AI-powered HR solutions, according to the paper. Addressing bias and ensuring AI fairness are ethical considerations in the analysis. It also stresses cloud-based HR system data privacy and security. HR experts are crucial to this transformation. The paper discusses HR teams’ changing roles in an AI and cloud-centric HR world and offers upskilling and reskilling techniques to maximise HR functions’ strategic value.

Microfluidics in High-Throughput Drug Screening: Organ-on-a-Chip and C. elegans-Based Innovations.

The development of therapeutic interventions for diseases necessitates a crucial step known as drug screening, wherein potential substances with medicinal properties are rigorously evaluated. This process has undergone a transformative evolution, driven by the imperative need for more efficient, rapid, and high-throughput screening platforms. Among these, microfluidic systems have emerged as the epitome of efficiency, enabling the screening of drug candidates with unprecedented speed and minimal sample consumption. This review paper explores the cutting-edge landscape of microfluidic-based drug screening platforms, with a specific emphasis on two pioneering approaches: organ-on-a-chip and C. elegans-based chips. Organ-on-a-chip technology harnesses human-derived cells to recreate the physiological functions of human organs, offering an invaluable tool for assessing drug efficacy and toxicity. In parallel, C. elegans-based chips, boasting up to 60% genetic homology with humans and a remarkable affinity for microfluidic systems, have proven to be robust models for drug screening. Our comprehensive review endeavors to provide readers with a profound understanding of the fundamental principles, advantages, and challenges associated with these innovative drug screening platforms. We delve into the latest breakthroughs and practical applications in this burgeoning field, illuminating the pivotal role these platforms play in expediting drug discovery and development. Furthermore, we engage in a forward-looking discussion to delineate the future directions and untapped potential inherent in these transformative technologies. Through this review, we aim to contribute to the collective knowledge base in the realm of drug screening, providing valuable insights to researchers, clinicians, and stakeholders alike. We invite readers to embark on a journey into the realm of microfluidic-based drug screening platforms, fostering a deeper appreciation for their significance and promising avenues yet to be explored.

Uterus transplantation: what the world’s religions have to say

Uterus transplantation (UTx) has evolved from a purely experimental procedure to a clinical treatment option available outside the clinical trial context, offering women with absolute uterine-factor infertility an opportunity to experience pregnancy. As UTx becomes better established and more widely known and performed, it is likely to be sought out by geographically and culturally diverse patients, particularly those whose religious beliefs impose barriers to other paths to achieve parenthood, such as gestational surrogacy and adoption. Many religions do not currently have official positions on UTx, meaning that clinicians involved in screening candidates can expect questions about how the UTx process aligns with various religious beliefs. This article provides a broad background on the current positions major world religions have taken on UTx (or its components) and the alternative paths to parenthood of gestational surrogacy and adoption. It is intended to help clinicians communicate the information necessary for individuals interested in uterus donation or transplantation to determine—in consultation with their spiritual advisors or religious authorities when needed—how these options align with religious beliefs or teachings.

Evaluation of a Best-Practice Advisory for Primary Aldosteronism Screening

Primary aldosteronism (PA) is a common cause of secondary hypertension and an independent risk factor for cardiovascular morbidity and mortality. Fewer than 2% to 4% of patients at risk are evaluated for PA. To develop and evaluate an electronic health record best-practice advisory (BPA) that assists with PA screening. This prospective quality improvement study was conducted at academic center outpatient clinics. Data analysis was performed between February and June 2023 and included adults with hypertension and at least 1 of the following: 4 or more current antihypertensive medications; hypokalemia; age younger than 35 years; or adrenal nodule(s). Patients previously tested for PA were excluded. A noninterruptive BPA was developed to trigger for PA screening candidates seen in outpatient setting by clinicians who treat hypertension. The BPA included an order set for PA screening and a link to results interpretation guidance. (1) The number of PA screening candidates identified by the BPA between October 1, 2021, and December 31, 2022; (2) the rates of PA screening; and (3) the BPA use patterns, stratified by physician specialty were assessed. Over 15 months, the BPA identified 14 603 unique candidates (mean [SD] age, 65.5 [16.9] years; 7300 women [49.9%]; 371 [2.5%] Asian, 2383 [16.3%] Black, and 11 225 [76.9%] White individuals) for PA screening, including 7028 (48.1%) with treatment-resistant hypertension, 6351 (43.5%) with hypokalemia, 1537 (10.5%) younger than 35 years, and 445 (3.1%) with adrenal nodule(s). In total, 2040 patients (14.0%) received orders for PA screening. Of these, 1439 patients (70.5%) completed the recommended screening within the system, and 250 (17.4%) had positive screening results. Most screening orders were placed by internists (40.0%) and family medicine physicians (28.1%). Family practitioners (80.3%) and internists (68.9%) placed most orders via the embedded order set, while specialists placed most orders (83.0%-95.4%) outside the BPA. Patients who received screening were younger and included more women and Black patients than those not screened. The likelihood of screening was higher among patients with obesity and dyslipidemia and lower in those with chronic kidney disease and established cardiovascular complications. The study results suggest that noninterruptive BPAs are potentially promising PA screening-assistance tools, particularly among primary care physicians. Combined with artificial intelligence algorithms that optimize the detection yield, refined BPAs may contribute to personalized hypertension care.

Application of multidisciplinary team conference for neuromodulation candidates facilitates patient selection and optimization.

Psychological evaluation is required by insurance companies in the United States prior to proceeding with a spinal cord stimulation or a dorsal root ganglion stimulation trial. Since January 2017, we implemented a Multidisciplinary Team Conference for Neuromodulation in our center to facilitate the collaboration between pain physicians and psychologists and to optimize screening of neuromodulation candidates. This study aims to report the impact of this team conference on improvement of neuromodulation outcome in our center. Appropriateness of neuromodulation were discussed in the team conference after initial visit with the pain specialist and psychological evaluation. For this study, we prospectively and retrospectively collected data on neuromodulation candidates who went through the team conference and those who did not as controls. We discussed 461 patients in the team conference sessions from January 2017 to July 2023. Out of these, a spinal cord stimulator or a dorsal root ganglion stimulator trial was performed in 164 patients with 80.5% (132 cases) trial success rate leading to 140 implants. Out of these implants, 26 (18.6%) explanted and 21 (15%) required revision in 41 (29.3%) patients. We performed neuraxial neuromodulation trial for 70 patients without going through the team conference from January 2016 to July 2023 with a trial success rate of 45.7% (32 cases). In this group, 7 (21.9%) and 6 (18.8%) patients underwent explant and revision. The differences between the groups were statistically significant for trial success rate (odds ratio of 4.9 with p-value of <0.01) but not for explant (odds ratio of 0.8 with p-value of 0.627) or revision (odds ratio of 0.8 with p-value of 0.595). Implementing Multidisciplinary Team Conference increased trial success rate in our center. Team conference provides therapeutic benefit for patients, and also provides the opportunity for an educational discussion for trainees.

Evaluation of a Fast Method to Measure High-Frequency Audiometry Based on Bayesian Learning.

This study aimed to assess the validity of a high-frequency audiometry tool based on Bayesian learning to provide a reliable, repeatable, automatic, and fast test to clinics. The study involved 85 people (138 ears) who had their high-frequency thresholds measured with three tests: standard audiometry (SA), alternative forced choice (AFC)-based algorithm, and Bayesian active (BA) learning-based algorithm. The results showed median differences within ±5 dB up to 10 kHz when comparing the BA with the other two tests, and median differences within ±10 dB at higher frequencies. The variability increased from lower to higher frequencies. The BA showed lower thresholds compared to the SA at the majority of the frequencies. The results of the different tests were consistent across groups (age, hearing loss, and tinnitus). The data for the BA showed high test-retest reliability (>90%). The time required for the BA was shorter than for the AFC (4 min vs. 13 min). The data suggest that the BA test for high-frequency audiometry could be a good candidate for clinical screening. It would add reliable and significant information without adding too much time to the visit.

Anti-inflammatory Activity and Computational Biology Study of Indole/Pyrimidine Hybrids

Abstract: This research paper embarks on an interdisciplinary exploration encompassing synthetic chemistry, pharmacology, and computational biology. The development of novel anti-inflammatory agents is an imperative endeavor within pharmaceutical research. Pyrimidines and thienopyrimidines are class of heterocyclic compounds that have gained prominence for their diverse pharmacological properties, including potential anti-inflammatory effects. When augmented with an indole moiety, these compounds exhibit structural diversity that can profoundly influence their biological activities. The integration of computational biology specifically molecular docking, plays a crucial role in predicting and understanding the binding interactions between these compounds and select protein targets associated with inflammatory pathways. This computational approach expedites the screening of potential drug candidates and elucidates the molecular underpinnings of their anti-inflammatory actions. Pyrimidine and thienopyrimidines tethering indole scaffold were obtained according to our reported methods. Subsequently, in vivo evaluation of anti-inflammatory is indispensable to gauge the anti-inflammatory potential of these compounds and establish structure-activity relationships. The experimental and computational biology studies of the target indole-pyrimidines hybrids revealed that these compounds can serve as anti-inflammatory agents. This paper can potentially open new avenues for therapeutic strategies against inflammation-associated disorders. The synergy of synthetic innovation, pharmacological evaluation, and computational insights offers a holistic approach to advance our understanding of pyrimidines with an indole moiety as potential agents for mitigating inflammation.

Virtual screening of potential biofungicide candidate for sustainable fungal disease control

Captafol is widely used as a fungicide compound. However, it is banned in Thailand due to its carcinogen risk level. This study aimed to search for a potential biofungicide candidate via a computational approach to compensate the using of chemical fungicide. Based on the goal, natural compounds showing structures and properties similar to those of captafol were retrieved from various databases. The fungicide-likeness properties were screened. The binding pocket of chitin synthase I was identified, after which virtual screening was performed by AutoDock Vina, and interaction patterns were analysed by Discovery Studio. Finally, kaurane-16,18-diol 18-acetate (NPC132839) extracted from plants was selected as a potential biofungicide candidate with -7.0 kcal/mol of binding energy. The outcomes of this study could be utilised as a highly useful resource to increase the successful exposure of bioactive compounds of plant extracts without damage the environment.

Preliminary screening of biomarkers and drug candidates in a mouse model of β-thalassemia based on quasi-targeted metabolomics.

β-thalassemia (β-TH) is a hereditary hemolytic anemia that results in deficient hemoglobin (Hb) synthesis. It is characterized by ineffective erythropoiesis, anemia, splenomegaly, and systemic iron overload. Exploration new potential biomarkers and drug candidates is important to facilitate the prevention and treatment of β-TH. We applied quasi-targeted metabolomics between wild type (Wt) and heterozygous β-TH mice (Th3/+), a model of non-transfusion-dependent β-TH intermedia, in plasma and peripheral blood (PB) cells. Futher data was deeply mined by Kyoto Encyclopedia of Genomes (KEGG) and machine algorithms methods. Using KEGG enrichment analysis, we found that taurine and hypotaurine metabolism disorders in plasma and alanine, aspartate and glutamate metabolism disorders in PB cells. After systematically anatomize the metabolites by machine algorithms, we confirmed that alpha-muricholic acidUP and N-acetyl-DL-phenylalanineUP in plasma and Dl-3-hydroxynorvalineUP, O-acetyl-L-serineUP, H-abu-OHUP, S-(Methyl) glutathioneUP, sepiapterinDOWN, and imidazoleacetic acidDOWN in PB cells play key roles in predicting the occurrence of β-TH. Furthermore, Sepiapterin, Imidazoleacetic acid, Methyl alpha-D-glucopyranoside and alpha-ketoglutaric acid have a good binding capacity to hemoglobin E through molecular docking and are considered to be potential drug candidates for β-TH. Those results may help in identify useful molecular targets in the diagnosis and treatment of β-TH and lays a strong foundation for further research.

Unveiling potential drug targets for lung squamous cell carcinoma through the integration of druggable genome and genome-wide association data.

Background: Lung squamous cell carcinoma (LSCC) is a major subtype of lung cancer with poor prognosis and low survival rate. Compared with lung adenocarcinoma, yet no FDA-approved targeted-therapy has been found for lung squamous cell carcinoma. Methods: To identify potential drug targets for LSCC, Summary-data-based Mendelian randomization (SMR) analysis was used to examine the potential association between 4,543 druggable genes and LSCC, followed by colocalization analysis and HEIDI tests to confirm the robustness of the result. Phenome-wide association study (PheWAS) explored potential side effects of candidate drug targets. Enrichment analysis and protein-protein interaction networks revealed the function and significance of therapeutic targets. Single-cell expression analysis was used to examine cell types with enrichment expression of druggable genes in LSCC tissue. Drug prediction included screening potential drug candidates and evaluating their interactions with targets through molecular docking. Results: This research has identified ten significant drug targets for LSCC through a comprehensive SMR analysis. These targets included (COPA, PKD2L1, CCR1, C2, CYP21A2, and NCSTN as risk factors, and CCNA2, C4A, APOM, and LPAR2 as protective factors). PheWAS demonstrated that C2, CCNA2, LPAR2, and NCSTN exhibited associations with other phenotypes at the genetic level. Then, we found four potentially effective drugs with the Dsigdb database. Subsequently, molecular docking indicated that favorable binding interactions between drug candidates and potential target molecules. In the druggability evaluation, five out of ten drug target genes have been used in drug development (APOM, C4A, CCNA2, COPA, and PKD2L1). Six out of ten druggable genes showed significant expression in LSCC tissues (COPA, PKD2L1, CCR1, C2, NCSTN, LPAR2). Besides, Single-cell expression analysis revealed that C2 and CCNA2 were primarily enriched in macrophages, while COPA and NCSTN were enriched in both macrophages and epithelial cells. Conclusion: Our research revealed ten potential druggable genes for LSCC treatment, which might help to advance the precise and efficient therapeutic approaches of LSCC.

Insights from Rodent Models for Improving Bench-to-Bedside Translation in Traumatic Brain Injury.

Road accidents, domestic falls, and persons associated with sports and military services exhibited the concussion or contusion type of traumatic brain injury (TBI) that resulted in chronic traumatic encephalopathy. In some instances, these complex neurological aberrations pose severe brain damage and devastating long-term neurological sequelae. Several preclinical (rat and mouse) TBI models simulate the clinical TBI endophenotypes. Moreover, many investigational neuroprotective candidates showed promising effects in these models; however, the therapeutic success of these screening candidates has been discouraging at various stages of clinical trials. Thus, a correct selection of screening model that recapitulates the clinical neurobiology and endophenotypes of concussion or contusion is essential. Herein, we summarize the advantages and caveats of different preclinical models adopted for TBI research. We suggest that an accurate selection of experimental TBI models may improve the translational viability of the investigational entity.

The development process of a candidate screening test for cladding products

AbstractThe ongoing cladding crisis within the United Kingdom has elevated the need for designers and risk assessors to have the knowledge and tools to evaluate the fire safety of proposed and existing cladding systems. This paper documents efforts to develop a test that could be used to evaluate the fire safety hazards of cladding products that were either proposed for use in design or that were found on existing buildings. Specifically, the products of interest were composite products (i.e., those comprised of multiple layers). The conceptual approach of the European harmonised system was used as a basis for investigating whether a small‐scale test could be used to evaluate product fire hazards. A relevant fire scenario was identified, this was linked to candidate large‐scale reference tests, and this was linked to performance in a candidate small‐scale test. The candidate test showed remarkable agreement with the reference large‐scale test, however, many issues were also identified. It was found that, even when specifically intended to accommodate composite products, the small‐scale test was unable to always evaluate hazards. Thus the authors were left with the conclusion—regardless of the testing system, there are always products that will not fit the testing system, however hard one tries.

Digital transformation of human capital management in the Gulf Cooperation Council

Digital human capital management (HCM) has emerged as a pivotal strategy in the Gulf Cooperation Council (GCC) region’s pursuit of modernizing and optimizing workforce management practices. This article examines the growing dynamics of digital transformation within the GCC’s human resources landscape. This transformation transcends conventional human resource (HR) practices, ushering in the rapid evolution of digital HCM. The thematic analysis results reveal two main strategies for HR/HC managers to employ digital transformation, categorized into primary themes and sub-themes. Critical areas impacted include human capital (HC), recruitment and selection (R&amp;S), learning and development (L&amp;D), compensation and benefits (C&amp;B), organizational development (OD), performance management (PM), and employee relations (ER). Specific results include the use of technology for workforce prediction, acceleration of planning procedures through human capital management systems (HCMS), and advanced tools such as psychographic assessments and online simulations for candidate screening. This study underlines the transformative impact of digital HCM in the GCC, while highlighting challenges such as resistance to change, digital skills gaps, and data security concerns, providing a comprehensive assessment of digital transformation efforts in the GCC, and offering insights into strategies and outcomes of digital HCM implementation in the region.

Advancing Bioactivity Prediction through Molecular Docking and Self-Attention.

Bioactivity refers to the ability of a substance to induce biological effects within living systems, often describing the influence of molecules, drugs, or chemicals on organisms. In drug discovery, predicting bioactivity streamlines early-stage candidate screening by swiftly identifying potential active molecules. The popular deep learning methods in bioactivity prediction primarily model the ligand structure-bioactivity relationship under the premise of Quantitative Structure-Activity Relationship (QSAR). However, bioactivity is determined by multiple factors, including not only the ligand structure but also drug-target interactions, signaling pathways, reaction environments, pharmacokinetic properties, and species differences. Our study first integrates drug-target interactions into bioactivity prediction using protein-ligand complex data from molecular docking. We devise a Drug-Target Interaction Graph Neural Network (DTIGN), infusing interatomic forces into intermolecular graphs. DTIGN employs multi-head self-attention to identify native-like binding pockets and poses within molecular docking results. To validate the fidelity of the self-attention mechanism, we gather ground truth data from crystal structure databases. Subsequently, we employ these limited native structures to refine bioactivity prediction via semi-supervised learning. For this study, we establish a unique benchmark dataset for evaluating bioactivity prediction models in the context of protein-ligand complexes, showcasing the superior performance of our method (with an average improvement of 27.03%) through comparison with 9 leading deep learning-based bioactivity prediction methods.