Graph neural pre-training based drug-target affinity prediction.

Computational drug-target affinity prediction has the potential to accelerate drug discovery. Currently, pre-training models have achieved significant success in various fields due to their ability to train the model using vast amounts of unlabeled data. However, given the scarcity of drug-target interaction data, pre-training models can only be trained separately on drug and target data, resulting in features that are insufficient for drug-target affinity prediction. To address this issue, in this paper, we design a graph neural pre-training-based drug-target affinity prediction method (GNPDTA). This approach comprises three stages. In the first stage, two pre-training models are utilized to extract low-level features from drug atom graphs and target residue graphs, leveraging a large number of unlabeled training samples. In the second stage, two 2D convolutional neural networks are employed to combine the extracted drug atom features and target residue features into high-level representations of drugs and targets. Finally, in the third stage, a predictor is used to predict the drug-target affinity. This approach fully utilizes both unlabeled and labeled training samples, enhancing the effectiveness of pre-training models for drug-target affinity prediction. In our experiments, GNPDTA outperforms other deep learning methods, validating the efficacy of our approach.

Causal effects of lipid-lowering drugs on inflammatory skin diseases: Evidence from drug target Mendelian randomisation.

Clinical research has revealed that inflammatory skin diseases are associated with dyslipidaemia. Modulating lipids is also a rising potential treatment option. However, there is heterogeneity in the existing evidence and a lack of large-scale clinical trials. Observational research is prone to bias, making it difficult to determine causality. This study aimed to evaluate the causal association between lipid-lowering drugs and inflammatory skin diseases. A drug target Mendelian randomisation (MR) analysis was conducted. Genetic targets of lipid-lowering drugs, including proprotein convertase subtilis kexin 9 (PCSK9) and 3-hydroxy-3-methylglutaryl-assisted enzyme A reductase (HMGCR) inhibitor, were screened. Common inflammatory skin diseases, including psoriasis, allergic urticaria, rosacea, atopic dermatitis, systemic sclerosis and seborrhoeic dermatitis, were considered as outcomes. Gene-predicted inhibition of PCSK9 was causally associated with a decreased risk of psoriasis (ORIVW [95%CI] = 0.600 [0.474-0.761], p = 2.48 × 10-5) and atopic dermatitis (ORIVW [95%CI] = 0.781 [0.633-0.964], p = 2.17 × 10-2). Gene-predicted inhibition of HMGCR decreased the risk of seborrhoeic dermatitis (ORIVW [95%CI] = 0.407 [0.168-0.984], p = 4.61 × 10-2) but increased the risk of allergic urticaria (ORIVW [95%CI] = 3.421 [1.374-8.520], p = 8.24 × 10-3) and rosacea (ORIVW [95%CI] = 3.132 [1.260-7.786], p = 1.40 × 10-2). Among all causal associations, only PCSK9 inhibition demonstrated a robust causal effect on psoriasis after a more rigorous Bonferroni test (p < 4.17 × 10-3, which is 0.05/12). Modulating lipids via PCSK9 inhibition may offer potential therapeutic targets for psoriasis and atopic dermatitis. Given the potential cutaneous side effects associated with HMGCR inhibitors, PCSK9 inhibitors could be considered viable alternatives in lipid-lowering medication.

Genetic association of lipid traits and lipid-related drug targets with normal tension glaucoma: a Mendelian randomization study for predictive preventive and personalized medicine.

Background: Glaucoma is the leading cause of irreversible blindness worldwide. Normal tension glaucoma (NTG) is a distinct subtype characterized by intraocular pressures (IOP) within the normal range (< 21 mm Hg). Due to its insidious onset and optic nerve damage, patients often present with advanced conditions upon diagnosis. NTG poses an additional challenge as it is difficult to identify with normal IOP, complicating its prediction, prevention, and treatment. Observational studies suggest a potential association between NTG and abnormal lipid metabolism, yet conclusive evidence establishing a direct causal relationship is lacking. This study aims to explore the causal link between serum lipids and NTG, while identifying lipid-related therapeutic targets. From the perspective of predictive, preventive, and personalized medicine (PPPM), clarifying the role of dyslipidemia in the development of NTG could provide a new strategy for primary prediction, targeted prevention, and personalized treatment of the disease. Working hypothesis and methods: In our study, we hypothesized that individuals with dyslipidemia may be more susceptible to NTG due to a dysregulation of microvasculature in optic nerve head. To verify the working hypothesis, univariable Mendelian randomization (UVMR) and multivariable Mendelian randomization (MVMR) were utilized to estimate the causal effects of lipid traits on NTG. Drug target MR was used to explore possible target genes for NTG treatment. Genetic variants associated with lipid traits and variants of genes encoding seven lipid-related drug targets were extracted from the Global Lipids Genetics Consortium genome-wide association study (GWAS). GWAS data for NTG, primary open angle glaucoma (POAG), and suspected glaucoma (GLAUSUSP) were obtained from FinnGen Consortium. For apolipoproteins, we used summary statistics from a GWAS study by Kettunen et al. in 2016. For metabolic syndrome, summary statistics were extracted from UK Biobank participants. In the end, these findings could help identify individuals at risk of NTG by screening for lipid dyslipidemia, potentially leading to new targeted prevention and personalized treatment approaches. Results: Genetically assessed high-density cholesterol (HDL) was negatively associated with NTG risk (inverse-variance weighted [IVW] model: OR per SD change of HDL level = 0.64; 95% CI, 0.49-0.85; P = 1.84 × 10-3), and the causal effect was independent of apolipoproteins and metabolic syndrome (IVW model: OR = 0.29; 95% CI, 0.14-0.60; P = 0.001 adjusted by ApoB and ApoA1; OR = 0.70; 95% CI, 0.52-0.95; P = 0.023 adjusted by BMI, HTN, and T2DM). Triglyceride (TG) was positively associated with NTG risk (IVW model: OR = 1.62; 95% CI, 1.15-2.29; P = 6.31 × 10-3), and the causal effect was independent of metabolic syndrome (IVW model: OR = 1.66; 95% CI, 1.18-2.34; P = 0.003 adjusted by BMI, HTN, and T2DM), but not apolipoproteins (IVW model: OR = 1.71; 95% CI, 0.99-2.95; P = 0.050 adjusted by ApoB and ApoA1). Genetic mimicry of apolipoprotein B (APOB) enhancement was associated with lower NTG risks (IVW model: OR = 0.09; 95% CI, 0.03-0.26; P = 9.32 × 10-6). Conclusions: Our findings supported dyslipidemia as a predictive causal factor for NTG, independent of other factors such as metabolic comorbidities. Among seven lipid-related drug targets, APOB is a potential candidate drug target for preventing NTG. Personalized health profiles can be developed by integrating lipid metabolism with life styles, visual quality of life such as reading, driving, and walking. This comprehensive approach will aid in shifting from reactive medical services to PPPM in the management of NTG. Supplementary Information: The online version contains supplementary material available at 10.1007/s13167-024-00373-5.

Identification of autophagy-related signatures in doxorubicin-induced cardiotoxicity.

PURPOSE: Doxorubicin is an antibiotic drug used clinically to treat infectious diseases and tumors. Unfortunately, it is cardiotoxic. Autophagy is a cellular self-decomposition process that is essential for maintaining homeostasis in the internal environment. Accordingly, the present study was proposed to characterize the autophagy-related signatures of doxorubicin-induced cardiotoxicity. METHODS: Datasets related to doxorubicin-induced cardiotoxicity were retrieved by searching the GEO database and differentially expressed genes (DEGs) were identified. DEGs were taken to intersect with autophagy-related genes to obtain autophagy-related signatures, and Gene Ontology (GO)/Kyoto Encyclopedia of Genes and Genomes (KEGG) enrichment analysis and protein-protein interaction (PPI) network were performed on them. Further, construction of miRNA-hub gene networks and identification of target drugs to reveal potential molecular mechanisms and therapeutic strategies. Animal models of doxorubicin-induced cardiotoxicity were constructed to validate differences in gene expression for autophagy-related signatures. RESULTS: PBMC and heart samples from the GSE37260 dataset were selected for analysis. There were 995 and 2357 DEGs in PBMC and heart samples, respectively, and they had 23 intersecting genes with autophagy-related genes. RT-qPCR confirmed the differential expression of 23 intersecting genes in doxorubicin-induced cardiotoxicity animal models in general agreement with the bioinformatics results. An autophagy-related signatures consisting of 23 intersecting genes is involved in mediating processes and pathways such as autophagy, oxidative stress, apoptosis, protein ubiquitination and phosphorylation. Moreover, Akt1, Hif1a and Mapk3 are hub genes in autophagy-associated signatures and their upstream miRNAs are mainly rno-miR-1188-5p, rno-miR-150-3p and rno-miR-326-3p, and their drugs are mainly CHEMBL55802, Carboxyamidotriazole and 3-methyladenine. CONCLUSION: This study identifies for the first-time autophagy-related signatures in doxorubicin's cardiotoxicity, which could provide potential molecular mechanisms and therapeutic strategies for doxorubicin-induced cardiotoxicity.

Causal Relationships between Lipid-Lowering Drug Target and Aortic Disease and Calcific Aortic Valve Stenosis: A Two-Sample Mendelian Randomization.

Background: Proprotein convertase subtilisin/kexin type 9 (PCSK9), 3-hydroxy-3-methylglutaryl-coenzyme A reductase (HMGCR), cholesteryl ester transfer protein (CETP) and apolipoprotein C3 (APOC3) are pivotal regulators of lipid metabolism, with licensed drugs targeting these genes. The use of lipid-lowering therapy via the inhibition of these genes has demonstrated a reduction in the risk of cardiovascular disease. However, concerns persist regarding their potential long-term impact on aortic diseases and calcific aortic valve disease (CAVS). This study aims to investigate causal relationships between genetic variants resembling these genes and aortic disease, as well as calcific aortic valve disease using Mendelian randomization (MR). Methods: We conducted drug-target Mendelian randomization employing summary-level statistics of low-density lipoprotein cholesterol (LDL-C) to proxy the loss-of-function of PCSK9, HMGCR, CETP and APOC3. Subsequently, we investigated the association between drug-target genetic variants and calcific aortic valve stenosis and aortic diseases, including thoracic aortic aneurysm (TAA), abdominal aortic aneurysm (AAA), and aortic dissection (AD). Results: The genetically constructed variants mimicking lower LDL-C levels were associated with a decreased risk of coronary artery disease, validating their reliability. Notably, HMGCR inhibition exhibited a robust protective effect against TAA (odds ratio (OR): 0.556, 95% CI: 0.372-0.831, p = 0.004), AAA (OR: 0.202, 95% CI: 0.107-0.315, p = 4.84 × 10-15), and AD (OR: 0.217, 95% CI: 0.098-0.480, p = 0.0002). Similarly, PCSK9, CETP and APOC3 inhibition proxies reduced the risk of AAA (OR: 0.595, 95% CI: 0.485-0.730, p = 6.75 × 10-7, OR: 0.127, 95% CI: 0.066-0.243, p = 4.42 × 10-10, and OR: 0.387, 95% CI: 0.182-0.824, p = 0.014, respectively) while showing a neutral impact on TAA and AD. Inhibition of HMGCR, PCSK9, and APOC3 showed promising potential in preventing CAVS with odds ratios of 0.554 (OR: 0.554, 95% CI: 0.433-0.707, p = 2.27 × 10-6), 0.717 (95% CI: 0.635-0.810, p = 9.28 × 10-8), and 0.540 (95% CI: 0.351-0.829, p = 0.005), respectively. However, CETP inhibition did not demonstrate any significant benefits in preventing CAVS (95% CI: 0.704-1.544, p = 0.836). The consistency of these findings across various Mendelian randomization methods, accounting for different assumptions concerning genetic pleiotropy, enhances the causal inference. Conclusions: Our MR analysis reveals that genetic variants resembling statin administration are associated with a reduced risk of AAA, TAA, AD and CAVS. HMGCR, PCSK9 and APOC3 inhibitors but not CETP inhibitors have positive benefits of reduced CAVS. Notably, PCSK9, CETP and APOC3 inhibitors exhibit a protective impact, primarily against AAA, with no discernible benefits extending to TAA or AD.

Proteome-wide mendelian randomization identifies therapeutic targets for nephrolithiasis.

Kidney Stone Disease (KSD) constitutes a multifaceted disorder, emerging from a confluence of environmental and genetic determinants, and is characterized by a high frequency of occurrence and recurrence. Our objective is to elucidate potential causative proteins and identify prospective pharmacological targets within the context of KSD. This investigation harnessed the unparalleled breadth of plasma protein and KSD pooled genome-wide association study (GWAS) data, sourced from the United Kingdom Biobank Pharma Proteomics Project (UKBPPP) and the FinnGen database version R10. Through Mendelian randomization analysis, proteins exhibiting a causal influence on KSD were pinpointed. Subsequent co-localization analyses affirmed the stability of these findings, while enrichment analyses evaluated their potential for pharmacological intervention. Culminating the study, a phenome-wide association study (PheWAS) was executed, encompassing all phenotypes (2408 phenotypes) catalogued in the FinnGen database version R10. Our MR analysis identified a significant association between elevated plasma levels of proteins FKBPL, ITIH3, and SERPINC1 and increased risk of KSD based on genetic predictors. Conversely, proteins CACYBP, DAG1, ITIH1, and SEMA6C showed a protective effect against KSD, documented with statistical significance (PFDR<0.05). Co-localization analysis confirmed these seven proteins share genetic variants with KSD, signaling a shared genetic basis (PPH3 + PPH4 > 0.8). Enrichment analysis revealed key pathways including hyaluronan metabolism, collagen-rich extracellular matrix, and serine-type endopeptidase inhibition. Additionally, our PheWAS connected the associated proteins with 356 distinct diseases (PFDR<0.05), highlighting intricate disease interrelations. In conclusion, our research elucidated a causal nexus between seven plasma proteins and KSD, enriching our grasp of prospective therapeutic targets.

MFF-DTA: Multi-scale feature fusion for drug-target affinity prediction.

Accurately predicting drug-target affinity is crucial in expediting the discovery and development of new drugs, which is a complex and risky process. Identifying these interactions not only aids in screening potential compounds but also guides further optimization. To address this, we propose a multi-perspective feature fusion model, MFF-DTA, which integrates chemical structure, biological sequence, and other data to comprehensively capture drug-target affinity features. The MFF-DTA model incorporates multiple feature learning components, each of which is capable of extracting drug molecular features and protein target information, respectively. These components are able to obtain key information from both global and local perspectives. Then, these features from different perspectives are efficiently combined using specific splicing strategies to create a comprehensive representation. Finally, the model uses the fused features to predict drug-target affinity. Comparative experiments show that MFF-DTA performs optimally on the Davis and KIBA data sets. Ablation experiments demonstrate that removing specific components results in the loss of unique information, thus confirming the effectiveness of the MFF-DTA design. Improvements in DTA prediction methods will decrease costs and time in drug development, enhancing industry efficiency and ultimately benefiting patients.

Drug Target to Alleviate Mitochondrial Dysfunctions in Alzheimer's Disease: Recent Advances and Therapeutic Implications.

Alzheimer's disease (AD) is a severe progressive neurodegenerative condition associated with neuronal damage and reduced cognitive function that primarily affects the aged worldwide. While there is increasing evidence suggesting that mitochondrial dysfunction is one of the most significant factors contributing to AD, its accurate pathobiology remains unclear. Mitochondrial bioenergetics and homeostasis are impaired and defected during AD pathogenesis. However, the potential of mutations in nuclear or mitochondrial DNA encoding mitochondrial constituents to cause mitochondrial dysfunction has been considered since it is one of the intracellular processes commonly compromised in early AD stages. Additionally, electron transport chain dysfunction and mitochondrial pathological protein interactions are related to mitochondrial dysfunction in AD. Many mitochondrial parameters decline during aging, causing an imbalance in reactive oxygen species (ROS) production, leading to oxidative stress in age-related AD. Moreover, neuroinflammation is another potential causative factor in AD-associated mitochondrial dysfunction. While several treatments targeting mitochondrial dysfunction have undergone preclinical studies, few have been successful in clinical trials. Therefore, this review discusses the molecular mechanisms and different therapeutic approaches for correcting mitochondrial dysfunction in AD, which have the potential to advance the future development of novel drug-based AD interventions.

Predicting drug-target interactions by measuring confidence with consistent causal neighborhood interventions.

Predicting drug-target interactions (DTI) is a crucial stage in drug discovery and development. Understanding the interaction between drugs and targets is essential for pinpointing the specific relationship between drug molecules and targets, akin to solving a link prediction problem using information technology. While knowledge graph (KG) and knowledge graph embedding (KGE) methods have been rapid advancements and demonstrated impressive performance in drug discovery, they often lack authenticity and accuracy in identifying DTI. This leads to increased misjudgment rates and reduced efficiency in drug development. To address these challenges, our focus lies in refining the accuracy of DTI prediction models through KGE, with a specific emphasis on causal intervention confidence measures (CI). These measures aim to assess triplet scores, enhancing the precision of the predictions. Comparative experiments conducted on three datasets and utilizing 9 KGE models reveal that our proposed confidence measure approach via causal intervention, significantly improves the accuracy of DTI link prediction compared to traditional approaches. Furthermore, our experimental analysis delves deeper into the embedding of intervention values, offering valuable insights for guiding the design and development of subsequent drug development experiments. As a result, our predicted outcomes serve as valuable guidance in the pursuit of more efficient drug development processes.

Blocking the WNT/ß-catenin pathway in cancer treatment:pharmacological targets and drug therapeutic potential.

The WNT/ß-catenin signaling pathway plays crucial roles in tumorigenesis and relapse, metastasis, drug resistance, and tumor stemness maintenance. In most tumors, the WNT/ß-catenin signaling pathway is often aberrantly activated. The therapeutic usefulness of inhibition of WNT/ß-catenin signaling has been reported to improve the efficiency of different cancer treatments and this inhibition of signaling has been carried out using different methods including pharmacological agents, short interfering RNA (siRNA), and antibodies. Here, we review the WNT-inhibitory effects of some FDA-approved drugs and natural products in cancer treatment and focus on recent progress of the WNT signaling inhibitors in improving the efficiency of chemotherapy, immunotherapy, gene therapy, and physical therapy. We also classified these FDA-approved drugs and natural products according to their structure and physicochemical properties, and introduced briefly their potential mechanisms of inhibiting the WNT signaling pathway. The review provides a comprehensive understanding of inhibitors of WNT/ß-catenin pathway in various cancer therapeutics. This will benefit novel WNT inhibitor development and optimal clinical use of WNT signaling-related drugs in synergistic cancer therapy.

Lipids, lipid-lowering drugs and lateral epicondylitis of the humerus: a drug-targeted Mendelian randomization study.

Background: Clinical observations indicate that blood lipids may be risk factors for lateral epicondylitis (LE) of the humerus, and lipid-lowering drugs are also used for the prevention and treatment of tendon diseases, but these lack high-quality clinical trial evidence and remain inconclusive. Mendelian randomization (MR) analyses can overcome biases in traditional observational studies and offer more accurate inference of causal relationships. Therefore, we employed this approach to investigate whether blood lipids are risk factors for LE and if lipid-lowering drugs can prevent it. Methods: Genetic variations associated with lipid traits, including low-density lipoprotein cholesterol (LDL-C), triglycerides (TG), and total cholesterol (TC), were obtained from the UK Biobank and the Global Lipids Genetics Consortium (GLGC). Data on genetic variation in LE were sourced from FinnGen, including 24,061 patients and 275,212 controls. Subsequently, MR analyses were conducted to assess the potential correlation between lipid traits and LE. Additionally, drug-target Mendelian randomization analyses were performed on 10 drug targets relevant to LE. For those drug targets that yielded significant results, further analysis was conducted using colocalization techniques. Results: No correlation was found between three blood lipid traits and LE. Lipoprotein lipase (LPL) enhancement is significantly associated with a decreased risk of LE (OR = 0.76, [95% CI, 0.65-0.90], p = 0.001). The expression of LPL in the blood is associated with LE and shares a single causal variant (12.07%), greatly exceeding the probability of different causal variations (1.93%), with a colocalization probability of 86.2%. Conclusion: The three lipid traits are not risk factors for lateral epicondylitis. LPL is a potential drug target for the prevention and treatment of LE.

Assessment of the effect of the SLC5A2 gene on eGFR: a Mendelian randomization study of drug targets for the nephroprotective effect of sodium-glucose cotransporter protein 2 inhibition.

Aim: Sodium-glucose cotransporter protein 2 (SGLT2) inhibitors have been shown to have renoprotective effects in clinical studies. For further validation in terms of genetic variation, drug-targeted Mendelian randomization (MR) was used to investigate the causal role of SGLT2 inhibition on eGFR effects. Methods: Genetic variants representing SGLT2 inhibition were selected as instrumental variables. Drug target Mendelian randomization analysis was used to investigate the relationship between SGLT2 inhibitors and eGFR. The IVW method was used as the primary analysis method. As a sensitivity analysis, GWAS pooled data from another CKDGen consortium was used to validate the findings. Results: MR results showed that hemoglobin A1c (HbA1c) levels, regulated by the SLC5A2 gene, were negatively correlated with eGFR (IVW ß -0.038, 95% CI -0.061 to -0.015, P = 0.001 for multi-ancestry populations; IVW ß -0.053, 95% CI -0.077 to -0.028, P = 2.45E-05 for populations of European ancestry). This suggests that a 1-SD increase in HbA1c levels, regulated by the SLC5A2 gene, is associated with decreased eGFR. Mimicking pharmacological inhibition by lowering HbA1c per 1-SD unit through SGLT2 inhibition reduces the risk of eGFR decline, demonstrating a renoprotective effect of SGLT2 inhibitors. HbA1c, regulated by the SLC5A2 gene, was negatively correlated with eGFR in both validation datasets (IVW ß -0.027, 95% CI -0.046 to -0.007, P=0.007 for multi-ancestry populations, and IVW ß -0.031, 95% CI -0.050 to -0.011, P=0.002 for populations of European origin). Conclusions: The results of this study indicate that the SLC5A2 gene is causally associated with eGFR. Inhibition of SLC5A2 gene expression was linked to higher eGFR. The renoprotective mechanism of SGLT2 inhibitors was verified from the perspective of genetic variation.

Repurposing antihypertensive drugs for pain disorders: a drug-target mendelian randomization study.

Objective: Addressing the rising prevalence of pain disorders and limitations of current analgesics, our study explores repurposing antihypertensive drugs for pain management, inspired by the link between hypertension and pain. We leverage a drug-target Mendelian Randomization (MR) approach to explore their dual benefits and establish causal connections. Methods: A comprehensive compilation of antihypertensive drug classes was undertaken through British National Formulary, with their target genes identified using the DrugBank database. Relevant single nucleotide polymorphisms (SNPs) associated with these targets were selected from published genomic studies on systolic blood pressure (SBP) as genetic instruments. These SNPs were validated through MR against acute coronary artery disease (CAD) to ensure genes not linked to CAD were excluded from acting as proxies for antihypertensive drugs. An MR analysis of 29 pain-related outcomes was conducted using the FinnGen R10 database employing the selected and validated genetic instruments. We utilized the Inverse Variance Weighted (IVW) method for primary analysis, applying Bonferroni correction to control type I error. IVW's multiplicative random effects (MRE) addressed heterogeneity, and MR-PRESSO managed pleiotropy, ensuring accurate causal inference. Results: Our analysis differentiates strong and suggestive evidence in linking antihypertensive drugs to pain disorder risks. Strong evidence was found for adrenergic neuron blockers increasing migraine without aura risk, loop diuretics reducing panniculitis, and vasodilator antihypertensives lowering limb pain risk. Suggestive evidence suggests alpha-adrenoceptor blockers might increase migraine risk, while beta-adrenoceptor blockers could lower radiculopathy risk. Adrenergic neuron blockers also show a potential protective effect against coxarthrosis (hip osteoarthritis) and increased femgenpain risk (pain and other conditions related to female genital organs and menstrual cycle). Additionally, suggestive links were found between vasodilator antihypertensives and reduced radiculopathy risk, and both alpha-adrenoceptor blockers and renin inhibitors possibly decreasing dorsalgianas risk (unspecified dorsalgia). These findings highlight the intricate effects of antihypertensive drugs on pain disorders, underlining the need for further research. Conclusion: The findings indicate that antihypertensive medications may exert varied effects on pain management, suggesting a repurposing potential for treating specific pain disorders. The results advocate for further research to confirm these associations and to explore underlying mechanisms, to optimize pain management practices.

Benchmarking reverse docking through AlphaFold2 human proteome.

Predicting the binding of ligands to the human proteome via reverse-docking methods enables the understanding of ligand's interactions with potential protein targets in the human body, thereby facilitating drug repositioning and the evaluation of potential off-target effects or toxic side effects of drugs. In this study, we constructed 11 reverse docking pipelines by integrating site prediction tools (PointSite and SiteMap), docking programs (Glide and AutoDock Vina), and scoring functions (Glide, Autodock Vina, RTMScore, DeepRMSD, and OnionNet-SFCT), and then thoroughly benchmarked their predictive capabilities. The results show that the Glide_SFCT (PS) pipeline exhibited the best target prediction performance based on the atomic structure models in AlphaFold2 human proteome. It achieved a success rate of 27.8% when considering the top 100 ranked prediction. This pipeline effectively narrows the range of potential targets within the human proteome, laying a foundation for drug target prediction, off-target assessment, and toxicity prediction, ultimately boosting drug development. By facilitating these critical aspects of drug discovery and development, our work has the potential to ultimately accelerate the identification of new therapeutic agents and improve drug safety.

Recent advances in studies on FtsZ inhibitors.

With the abuse of antibiotics, multidrug resistant strains continue to emerge and spread rapidly. Therefore, there is an urgent need to develop new antimicrobial drugs. As a highly conserved cell division protein in bacteria, filamenting temperature-sensitive mutant Z (FtsZ) has been identified as a potential antimicrobial target. This paper reviews the structure, function, and action mechanism of FtsZ and a variety of natural and synthetic compounds targeting FtsZ, including 3-MBA derivatives, taxane derivatives, cinnamaldehyde, curcumin, quinoline and quinazoline derivatives, aromatic compounds, purpurin, and totarol. From these studies, FtsZ has a clear supporting role in the field of antimicrobial drug discovery. The urgent need and interest of antibacterial drugs will contribute to the discovery of new clinical drugs targeting FtsZ.

NR0B2 Is a Key Factor for Gastric Diseases: A GEO Database Analysis Combined with Drug-Target Mendelian Randomization.

Small Heterodimer Partner (SHP; NR0B2) is an orphan receptor that acts as a transcriptional regulator, controlling various metabolic processes, and is a potential therapeutic target for cancer. Examining the correlation between the expression of NR0B2 and the risk of gastric diseases could open a new path for treatment and drug development. The Gene Expression Omnibus (GEO) database was utilized to explore NR0B2 gene expression profiles in gastric diseases. Co-expressed genes were identified through Weighted Correlation Network Analysis (WGCNA), and GO enrichment was performed to identify potential pathways. The Xcell method was employed to analyze immune infiltration relationships. To determine the potential causal relationship between NR0B2 expression and gastric diseases, we identified six single-nucleotide polymorphisms (SNPs) as a proxy for NR0B2 expression located within 100 kilobases of NR0B2 and which are associated with triglyceride homeostasis and performed drug-target Mendelian randomization (MR). Bioinformatics analysis revealed that NR0B2 expression levels were reduced in gastric cancer and increased in gastritis. GO analysis and Gene Set Enrichment Analysis (GSEA) showed that NR0B2 is widely involved in oxidation-related processes. Immune infiltration analyses found that NR0B2 was associated with Treg. Prognostic analyses showed that a low expression of NR0B2 is a risk factor for the poor prognoses of gastric cancer. MR analyses revealed that NR0B2 expression is associated with a risk of gastric diseases (NR0B2 vs. gastric cancer, p = 0.006, OR: 0.073, 95%CI: 0.011-0.478; NR0B2 vs. gastric ulcer, p = 0.03, OR: 0.991, 95%CI: 0.984-0.999; NR0B2 vs. other gastritis, p = 0.006, OR:3.82, 95%CI: 1.468-9.942). Our study confirms the causal relationship between the expression of NR0B2 and the risk of gastric diseases, and highlights its role in the progression of gastric cancer. The present study opens new avenues for exploring the potential of drugs that either activate or inhibit the NR0B2 receptor in the treatment of gastric diseases.

Drug-target interaction prediction with collaborative contrastive learning and adaptive self-paced sampling strategy.

BACKGROUND: Drug-target interaction (DTI) prediction plays a pivotal role in drug discovery and drug repositioning, enabling the identification of potential drug candidates. However, most previous approaches often do not fully utilize the complementary relationships among multiple biological networks, which limits their ability to learn more consistent representations. Additionally, the selection strategy of negative samples significantly affects the performance of contrastive learning methods. RESULTS: In this study, we propose CCL-ASPS, a novel deep learning model that incorporates Collaborative Contrastive Learning (CCL) and Adaptive Self-Paced Sampling strategy (ASPS) for drug-target interaction prediction. CCL-ASPS leverages multiple networks to learn the fused embeddings of drugs and targets, ensuring their consistent representations from individual networks. Furthermore, ASPS dynamically selects more informative negative sample pairs for contrastive learning. Experiment results on the established dataset demonstrate that CCL-ASPS achieves significant improvements compared to current state-of-the-art methods. Moreover, ablation experiments confirm the contributions of the proposed CCL and ASPS strategies. CONCLUSIONS: By integrating Collaborative Contrastive Learning and Adaptive Self-Paced Sampling, the proposed CCL-ASPS effectively addresses the limitations of previous methods. This study demonstrates that CCL-ASPS achieves notable improvements in DTI predictive performance compared to current state-of-the-art approaches. The case study and cold start experiments further illustrate the capability of CCL-ASPS to effectively predict previously unknown DTI, potentially facilitating the identification of new drug-target interactions.

A multi-view feature representation for predicting drugs combination synergy based on ensemble and multi-task attention models.

This paper proposes a novel multi-view ensemble predictor model that is designed to address the challenge of determining synergistic drug combinations by predicting both the synergy score value values and synergy class label of drug combinations with cancer cell lines. The proposed methodology involves representing drug features through four distinct views: Simplified Molecular-Input Line-Entry System (SMILES) features, molecular graph features, fingerprint features, and drug-target features. On the other hand, cell line features are captured through four views: gene expression features, copy number features, mutation features, and proteomics features. To prevent overfitting of the model, two techniques are employed. First, each view feature of a drug is paired with each corresponding cell line view and input into a multi-task attention deep learning model. This multi-task model is trained to simultaneously predict both the synergy score value and synergy class label. This process results in sixteen input view features being fed into the multi-task model, producing sixteen prediction values. Subsequently, these prediction values are utilized as inputs for an ensemble model, which outputs the final prediction value. The 'MVME' model is assessed using the O'Neil dataset, which includes 38 distinct drugs combined across 39 distinct cancer cell lines to output 22,737 drug combination pairs. For the synergy score value, the proposed model scores a mean square error (MSE) of 206.57, a root mean square error (RMSE) of 14.30, and a Pearson score of 0.76. For the synergy class label, the model scores 0.90 for accuracy, 0.96 for precision, 0.57 for kappa, 0.96 for the area under the ROC curve (ROC-AUC), and 0.88 for the area under the precision-recall curve (PR-AUC).

Exploring Genetic Drug Targets in Acne Vulgaris: A Comprehensive Proteome-Wide Mendelian Randomization Study.

BACKGROUND: Acne vulgaris presents a substantial clinical challenge due to its complex pathophysiology and significant impact on quality of life. Identification of novel therapeutic targets for acne using genetic tools can guide the development of more effective treatments. METHODS: Utilizing a dataset comprising 35 559 Icelandic individuals, we performed proteomic analyses to quantify 4709 circulating proteins. We integrated these data with acne-specific genome-wide association studies (GWAS) encompassing 34 422 acne patients and 364 991 controls. Mendelian randomization (MR) analyses employed the TwoSampleMR tool and Summary-data-based Mendelian Randomization (SMR) to estimate the causal effects of identified proteins on acne risk. Colocalization analyses assessed the likelihood of shared genetic etiology between protein levels and acne using the "coloc" R package. RESULTS: Our proteome-wide MR analysis initially identified 128 proteins potentially associated with acne risk. Following multiple testing corrections using the Benjamini-Hochberg method, fatty acid synthase (FASN) and tissue inhibitor of metalloproteinases 4 (TIMP4) remained significantly associated with acne risk. FASN exhibited a protective effect against acne (OR = 0.768, 95% CI: 0.676-0.872, p = 4.685E-05), while TIMP4 was associated with an increased risk (OR = 1.169, 95% CI: 1.103-1.241, p = 1.956E-07). Colocalization analysis supported a shared genetic basis for these protein-acne associations, with posterior probabilities indicating strong evidence of shared causal variants. CONCLUSION: Our findings highlight the utility of integrative genomic approaches in identifying potential therapeutic targets for acne. FASN and TIMP4, in particular, demonstrate strong potential as targets for therapeutic intervention, pending further validation through clinical research. These results offer a foundation for targeted acne treatment development, aligning with personalized medicine principles.