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The evaluation of compound-target interactions (CTIs) is at the heart of drug discovery efforts. Given the substantial time and monetary costs of classical experimental screening, significant efforts have been dedicated to develop deep learning-based models that can accurately predict CTIs. A comprehensive comparison of these models on a large, curated CTI dataset is, however, still lacking. Here, we perform an in-depth comparison of 12 state-of-the-art deep learning architectures that use different protein and compound representations. The models were selected for their reported performance and architectures. To reliably compare model performance, we curated over 300 thousand binding and non-binding CTIs and established several gold-standard datasets of varying size and information. Based on our findings, DeepConv-DTI consistently outperforms other models in CTI prediction performance across the majority of datasets. It achieves an MCC of 0.6 or higher for most of the datasets and is one of the fastest models in training and inference. These results indicate that utilizing convolutional-based windows as in DeepConv-DTI to traverse trainable embeddings is a highly effective approach for capturing informative protein features. We also observed that physicochemical embeddings of targets increased model performance. We therefore modified DeepConv-DTI to include normalized physicochemical properties, which resulted in the overall best performing model Phys-DeepConv-DTI. This work highlights how the systematic evaluation of input features of compounds and targets, as well as their corresponding neural network architectures, can serve as a roadmap for the future development of improved CTI models.Scientific contributionThis work features comprehensive CTI datasets to allow for the objective comparison and benchmarking of CTI prediction algorithms. Based on this dataset, we gained insights into which embeddings of compounds and targets and which deep learning-based algorithms perform best, providing a blueprint for the future development of CTI algorithms. Using the insights gained from this screen, we provide a novel CTI algorithm with state-of-the-art performance., (© 2024. The Author(s).)

Antineutrophil cytoplasmic antibody (ANCA)-associated vasculitis is a life-threatening autoimmune disease that often results in kidney failure caused by crescentic glomerulonephritis (GN). To date, treatment of most patients with ANCA-GN relies on non-specific immunosuppressive agents, which may have serious adverse effects and be only partially effective. Here, using spatial and single-cell transcriptome analysis, we characterize inflammatory niches in kidney samples from 34 patients with ANCA-GN and identify proinflammatory, cytokine-producing CD4 + and CD8 + T cells as a pathogenic signature. We then utilize these transcriptomic profiles for digital pharmacology and identify ustekinumab, a monoclonal antibody targeting IL-12 and IL-23, as the strongest therapeutic drug to use. Moreover, four patients with relapsing ANCA-GN are treated with ustekinumab in combination with low-dose cyclophosphamide and steroids, with ustekinumab given subcutaneously (90 mg) at weeks 0, 4, 12, and 24. Patients are followed up for 26 weeks to find this treatment well-tolerated and inducing clinical responses, including improved kidney function and Birmingham Vasculitis Activity Score, in all ANCA-GN patients. Our findings thus suggest that targeting of pathogenic T cells in ANCA-GN patients with ustekinumab might represent a potential approach and warrants further investigation in clinical trials., (© 2024. The Author(s).)

Background: Asthma is a heterogeneous disease with high morbidity. Advancement in high-throughput multi-omics approaches has enabled the collection of molecular assessments at different layers, providing a complementary perspective of complex diseases. Numerous computational methods have been developed for the omics-based patient classification or disease outcome prediction. Yet, a systematic benchmarking of those methods using various combinations of omics data for the prediction of asthma development is still lacking., Objective: We aimed to investigate the computational methods in disease status prediction using multi-omics data., Method: We systematically benchmarked 18 computational methods using all the 63 combinations of six omics data (GWAS, miRNA, mRNA, microbiome, metabolome, DNA methylation) collected in The Vitamin D Antenatal Asthma Reduction Trial (VDAART) cohort. We evaluated each method using standard performance metrics for each of the 63 omics combinations., Results: Our results indicate that overall Logistic Regression, Multi-Layer Perceptron, and MOGONET display superior performance, and the combination of transcriptional, genomic and microbiome data achieves the best prediction. Moreover, we find that including the clinical data can further improve the prediction performance for some but not all the omics combinations., Conclusions: Specific omics combinations can reach the optimal prediction of asthma development in children. And certain computational methods showed superior performance than other methods., (© 2023. The Author(s).)