GraphMS: Drug Target Prediction Using Graph Representation Learning with Substructures

The prediction of drug–target interactions is always a key task in the field of drug redirection. However, traditional methods of predicting drug–target interactions are either mediocre or rely heavily on data stacking. In this work, we proposed our model named GraphMS. We merged heterogeneous graph information and obtained effective node information and substructure information based on mutual information in graph embeddings. We then learned high quality representations for downstream tasks, and proposed an end–to–end auto–encoder model to complete the task of link prediction. Experimental results show that our method outperforms several state–of–the–art models. The model can achieve the area under the receiver operating characteristics (AUROC) curve of 0.959 and area under the precise recall curve (AUPR) of 0.847. We found that the mutual information between the substructure and graph–level representations contributes most to the mutual information index in a relatively sparse network. And the mutual information between the node–level and graph–level representations contributes most in a relatively dense network.

Drug Target Prediction Using Graph Representation Learning via Substructures Contrast

The prediction of drug--target interactions is always a key task in the field of drug redirection. However, traditional methods of predicting drug--target interactions are either mediocre or rely heavily on data stacking. In this work, we merged heterogeneous graph information and obtained effective node information and substructure information based on mutual information in graph embeddings. We then learned high quality representations for downstream tasks, and proposed an end--to--end auto--encoder model to complete the task of link prediction. Experimental results show that our method outperforms several state--of--art models. The model can achieve the area under the receiver operating characteristics (AUROC) curve of 0.959 and area under the precise recall curve (AUPR) of 0.848. We found that the mutual information between the substructure and graph--level representations contributes most to the mutual information index in a relatively sparse network. And the mutual information between the node--level and graph--level representations contributes most in a relatively dense network.

Interpretable Drug Target Predictions using Self-Expressiveness

The identification of missing drug targets is critical for the development of treatments and for the molecular elucidation of drug side effects. Drug targets have been predicted by exploiting molecular, biological or pharmacological features of drugs and protein targets. Yet, developing integrative and interpretable machine learning models for predicting drug targets remains a challenging task. We present Inception, an integrative and interpretable matrix completion model for predicting drug targets. Inception is a self-expressive model that learns two similarity matrices: one for drugs and another for protein targets. These learned similarity matrices are key for our models’ interpretability: they can explain how a predicted drug-target interaction can be explain in terms of a linear combination of chemical, biological and pharmacological similarities. We develop a novel objective function with efficient closed-form solution. To demonstrate the ability of Inception at recovering missing drug-target interactions (DTIs), we perform cross-validation experiments with stringent controls of data imbalance, chemical similarities between drugs and sequence similarities between targets. We also assess the performance of our model using a simulated prospective approach. Having trained our model with DTIs from a snapshot 2011 of the DrugBank database, we test whether we could predict DTIs from a 2020 snapshot of DrugBank. Inception outperforms two state-of-the-art drug target prediction models in all the scenarios. This suggests that Inception could be useful for predicting missing drug target interactions while providing interpretable predictions.

Disease severity-specific neutrophil signatures in blood transcriptomes stratify COVID-19 patients

Background The SARS-CoV-2 pandemic is currently leading to increasing numbers of COVID-19 patients all over the world. Clinical presentations range from asymptomatic, mild respiratory tract infection, to severe cases with acute respiratory distress syndrome, respiratory failure, and death. Reports on a dysregulated immune system in the severe cases call for a better characterization and understanding of the changes in the immune system. Methods In order to dissect COVID-19-driven immune host responses, we performed RNA-seq of whole blood cell transcriptomes and granulocyte preparations from mild and severe COVID-19 patients and analyzed the data using a combination of conventional and data-driven co-expression analysis. Additionally, publicly available data was used to show the distinction from COVID-19 to other diseases. Reverse drug target prediction was used to identify known or novel drug candidates based on finding from data-driven findings. Results Here, we profiled whole blood transcriptomes of 39 COVID-19 patients and 10 control donors enabling a data-driven stratification based on molecular phenotype. Neutrophil activation-associated signatures were prominently enriched in severe patient groups, which was corroborated in whole blood transcriptomes from an independent second cohort of 30 as well as in granulocyte samples from a third cohort of 16 COVID-19 patients (44 samples). Comparison of COVID-19 blood transcriptomes with those of a collection of over 3100 samples derived from 12 different viral infections, inflammatory diseases, and independent control samples revealed highly specific transcriptome signatures for COVID-19. Further, stratified transcriptomes predicted patient subgroup-specific drug candidates targeting the dysregulated systemic immune response of the host. Conclusions Our study provides novel insights in the distinct molecular subgroups or phenotypes that are not simply explained by clinical parameters. We show that whole blood transcriptomes are extremely informative for COVID-19 since they capture granulocytes which are major drivers of disease severity.

Homobivalent Lamellarin-Like Schiff Bases: In Vitro Evaluation of Their Cancer Cell Cytotoxicity and Multitargeting Anti-Alzheimer’s Disease Potential

Marine alkaloids belonging to the lamellarins family, which incorporate a 5,6-dihydro-1-phenylpyrrolo[2,1-a]isoquinoline (DHPPIQ) moiety, possess various biological activities, spanning from antiviral and antibiotic activities to cytotoxicity against tumor cells and the reversal of multidrug resistance. Expanding a series of previously reported imino adducts of DHPPIQ 2-carbaldehyde, novel aliphatic and aromatic Schiff bases were synthesized and evaluated herein for their cytotoxicity in five diverse tumor cell lines. Most of the newly synthesized compounds were found noncytotoxic in the low micromolar range (<30 μM). Based on a Multi-fingerprint Similarity Search aLgorithm (MuSSeL), mainly conceived for making protein drug target prediction, some DHPPIQ derivatives, especially bis-DHPPIQ Schiff bases linked by a phenylene bridge, were prioritized as potential hits addressing Alzheimer’s disease-related target proteins, such as cholinesterases (ChEs) and monoamine oxidases (MAOs). In agreement with MuSSeL predictions, homobivalent para-phenylene DHPPIQ Schiff base 14 exhibited a noncompetitive/mixed inhibition of human acetylcholinesterase (AChE) with Ki in the low micromolar range (4.69 μM). Interestingly, besides a certain inhibition of MAO A (50% inhibition of the cell population growth (IC50) = 12 μM), the bis-DHPPIQ 14 showed a good inhibitory activity on self-induced β-amyloid (Aβ)1–40 aggregation (IC50 = 13 μM), which resulted 3.5-fold stronger than the respective mono-DHPPIQ Schiff base 9.