Deep Neural Network-Assisted Drug Recommendation Systems for Identifying Potential Drug-Target Interactions

Attention is all you need: utilizing attention in AI-enabled drug discovery

Recently, attention mechanism and derived models have gained significant traction in drug development due to their outstanding performance and interpretability in handling complex data structures. This review offers an in-depth exploration of the principles underlying attention-based models and their advantages in drug discovery. We further elaborate on their applications in various aspects of drug development, from molecular screening and target binding to property prediction and molecule generation. Finally, we discuss the current challenges faced in the application of attention mechanisms and Artificial Intelligence technologies, including data quality, model interpretability and computational resource constraints, along with future directions for research. Given the accelerating pace of technological advancement, we believe that attention-based models will have an increasingly prominent role in future drug discovery. We anticipate that these models will usher in revolutionary breakthroughs in the pharmaceutical domain, significantly accelerating the pace of drug development.

Improvement of multi-task learning by data enrichment: application for drug discovery

Multi-task learning in deep neural networks has become a topic of growing importance in many research fields, including drug discovery. However, applying multi-task learning poses new challenges in improving prediction performance. This study investigated the potential of training data enrichment to enhance multi-task model prediction quality in drug discovery. The study evaluated four scenarios with varying degrees of information capacity of the training data and applied two types of test data to evaluate prediction performance. We used three datasets: ViralChEMBL, which consisted of binary activities of compounds against viral species, was applied for the classification task; pQSAR(159) and pQSAR(4267), which consisted of bio-activities of compounds and assays from the research of the profile-QSAR method, were applied for regression tasks. We built multi-task models based on the feed-forward DNNs using the PyTorch framework. Our findings showed that training data enrichment could be an effective means of enhancing prediction performance in multi-task learning, but the degree of improvement depends on the quality of the training data. The more unique compounds and targets the training data included, the more new compound-target interactions are required for prediction improvement. Also, we found out that even using multi-task learning, one could not predict the interactions of compounds that are highly dissimilar from those used for model training. The study provides some recommendations for effectively employing multi-task learning in drug discovery to improve prediction accuracy and facilitate the discovery of novel drug candidates.

De Novo Design of Imidazopyridine-Tethered Pyrazolines That Target Phosphorylation of STAT3 in Human Breast Cancer Cells

In breast cancer (BC), STAT3 is hyperactivated. This study explored the design of imidazopyridine-tethered pyrazolines as a de novo drug strategy for inhibiting STAT3 phosphorylation in human BC cells. This involved the synthesis and characterization of two series of compounds namely, 1-(3-(2,6-dimethylimidazo [1,2-a]pyridin-3-yl)-5-(3-nitrophenyl)-4,5-dihydro-1H-pyrazol-1-yl)-2-(4-(substituted)piperazin-1-yl)ethanone and N-substituted-3-(2,6-dimethylimidazo[1,2-a]pyridin-3-yl)-5-(3-nitrophenyl)-4,5-dihydro-1H-pyrazoline-1-carbothioamides. Compound 3f with 2,3-dichlorophenyl substitution was recognized among the tested series as a lead structure that inhibited the viability of MCF-7 cells with an IC₅₀ value of 9.2 μM. A dose- and time-dependent inhibition of STAT3 phosphorylation at Tyr705 and Ser727 was observed in MCF-7 and T47D cells when compound 3f was added in vitro. Calculations using density functional theory showed that the title compounds HOMOs and LUMOs are situated on imidazopyridine-pyrazoline and nitrophenyl rings, respectively. Hence, compound 3f effectively inhibited STAT3 phosphorylation in MCF-7 and T47D cells, indicating that these structures may be an alternative synthon to target STAT3 signaling in BC.

A Complex Heterogeneous Network Model of Disease Regulated by Noncoding RNAs: A Case Study of Unstable Angina Pectoris

MicroRNAs (miRNAs) are important types of noncoding RNAs, and there is a lack of holistic and systematic understanding of the functions they play in disease. We proposed a research strategy, including two parts network analysis and network modelling, to analyze, model, and predict the regulatory network of miRNAs from a network perspective, using unstable angina pectoris as an example. In the network analysis section, we proposed the WGCNA & SimCluster method using both correlation and similarity to find hub miRNAs, and validation on two datasets showed better results than the methods using correlation or similarity alone. In the network modelling section, we used six knowledge graph or graph neural network models for link prediction of three types of edges and multilabel classification of two types of nodes. Comparative experiments showed that the RotatE model was a good model for link prediction, while the RGCN model was the best model for multilabel classification. Potential target genes were predicted for hub miRNAs and validation of hub miRNA-target gene interactions, target genes as biomarkers and target gene functions were performed using a three-step validation approach. In conclusion, our study provides a new strategy to analyze and model miRNA regulatory networks.