On the robustness of generalization of drug-drug interaction models

Learning self-supervised molecular representations for drug-drug interaction prediction

Drug-drug interactions (DDI) are a critical concern in healthcare due to their potential to cause adverse effects and compromise patient safety. Supervised machine learning models for DDI prediction need to be optimized to learn abstract, transferable features, and generalize to larger chemical spaces, primarily due to the scarcity of high-quality labeled DDI data. Inspired by recent advances in computer vision, we present SMR-DDI, a self-supervised framework that leverages contrastive learning to embed drugs into a scaffold-based feature space. Molecular scaffolds represent the core structural motifs that drive pharmacological activities, making them valuable for learning informative representations. Specifically, we pre-trained SMR-DDI on a large-scale unlabeled molecular dataset. We generated augmented views for each molecule via SMILES enumeration and optimized the embedding process through contrastive loss minimization between views. This enables the model to capture relevant and robust molecular features while reducing noise. We then transfer the learned representations for the downstream prediction of DDI. Experiments show that the new feature space has comparable expressivity to state-of-the-art molecular representations and achieved competitive DDI prediction results while training on less data. Additional investigations also revealed that pre-training on more extensive and diverse unlabeled molecular datasets improved the model's capability to embed molecules more effectively. Our results highlight contrastive learning as a promising approach for DDI prediction that can identify potentially hazardous drug combinations using only structural information.

Therapies for Tau-associated neurodegenerative disorders: targeting molecules, synapses, and cells

Advances in experimental and computational technologies continue to grow rapidly to provide novel avenues for the treatment of neurodegenerative disorders. Despite this, there remain only a handful of drugs that have shown success in late-stage clinical trials for Tau-associated neurodegenerative disorders. The most commonly prescribed treatments are symptomatic treatments such as cholinesterase inhibitors and N-methyl-D-aspartate receptor blockers that were approved for use in Alzheimer's disease. As diagnostic screening can detect disorders at earlier time points, the field needs pre-symptomatic treatments that can prevent, or significantly delay the progression of these disorders (Koychev et al., 2019). These approaches may be different from late-stage treatments that may help to ameliorate symptoms and slow progression once symptoms have become more advanced should early diagnostic screening fail. This mini-review will highlight five key avenues of academic and industrial research for identifying therapeutic strategies to treat Tau-associated neurodegenerative disorders. These avenues include investigating (1) the broad class of chemicals termed "small molecules"; (2) adaptive immunity through both passive and active antibody treatments; (3) innate immunity with an emphasis on microglial modulation; (4) synaptic compartments with the view that Tau-associated neurodegenerative disorders are synaptopathies. Although this mini-review will focus on Alzheimer's disease due to its prevalence, it will also argue the need to target other tauopathies, as through understanding Alzheimer's disease as a Tau-associated neurodegenerative disorder, we may be able to generalize treatment options. For this reason, added detail linking back specifically to Tau protein as a direct therapeutic target will be added to each topic.

HCDT: an integrated highly confident drug-target resource

Drug-target association plays an important role in drug discovery, drug repositioning, drug synergy prediction, etc. Currently, a lot of drug-related databases, such as DrugBank and BindingDB, have emerged. However, these databases are separate, incomplete and non-uniform with different criteria. Here, we integrated eight drug-related databases; collected, filtered and supplemented drugs, target genes and experimentally validated (highly confident) associations and built a highly confident drug-target (HCDT: http://hainmu-biobigdata.com/hcdt) database. HCDT database includes 500 681 HCDT associations between 299 458 drugs and 5618 target genes. Compared to individual databases, HCDT database contains 1.1 to 254.2 times drugs, 1.8-5.5 times target genes and 1.4-27.7 times drug-target associations. It is normative, publicly available and easy for searching, browsing and downloading. Together with multi-omics data, it will be a good resource in analyzing the drug functional mechanism, mining drug-related biological pathways, predicting drug synergy, etc. Database URL: http://hainmu-biobigdata.com/hcdt.