Explainable drug side effect prediction via biologically informed graph neural network.

Early detection of potential side effects (SE) is a critical and challenging task for drug discovery and patient care. In-vitro or in-vivo approach to detect potential SEs is not scalable for many drug candidates during the preclinical stage. Recent advances in explainable machine learning may facilitate detecting potential SEs of new drugs before market release and elucidating the critical mechanism of biological actions. Here, we leverage multi-modal interactions among molecules to develop a biologically informed graph-based SE prediction model, called HHAN-DSI. HHAN-DSI predicted frequent and even uncommon SEs of the unseen drug with higher or comparable accuracy against benchmark methods. When applying HHAN-DSI to the central nervous system, the organs with the largest number of SEs, the model revealed diverse psychiatric medications' previously unknown but probable SEs, together with the potential mechanisms of actions through a network of genes, biological functions, drugs, and SEs.

Integrating Comorbidity Knowledge for Alzheimer's Disease Drug Repurposing using Multi-task Graph Neural Network.

Alzheimer's Disease (AD) is a multifactorial disease that shares common etiologies with its multiple comorbidities, especially vascular diseases. To predict repurposable drugs for AD utilizing the relatively well-investigated comorbidities' knowledge, we proposed a multi-task graph neural network (GNN)-based pipeline that incorporates the corresponding biomedical interactome of these diseases with their genetic markers and effective therapeutics. Our pipeline can accurately capture the interactions and disease classification in the network. Next, we predicted drugs that might interact with the AD module by the node embedding similarity. Our candidates are mostly BBB permeable, and literature evidence showed their potential for treating AD pathologies, accompanying symptoms, or cotreating AD pathology and its common comorbidities. Our pipeline demonstrated a workable strategy that predicts drug candidates with current knowledge of biological interplays between AD and several vascular diseases.

Synthesize heterogeneous biological knowledge via representation learning for Alzheimer's disease drug repurposing.

Developing drugs for treating Alzheimer's disease has been extremely challenging and costly due to limited knowledge of underlying mechanisms and therapeutic targets. To address the challenge in AD drug development, we developed a multi-task deep learning pipeline that learns biological interactions and AD risk genes, then utilizes multi-level evidence on drug efficacy to identify repurposable drug candidates. Using the embedding derived from the model, we ranked drug candidates based on evidence from post-treatment transcriptomic patterns, efficacy in preclinical models, population-based treatment effects, and clinical trials. We mechanistically validated the top-ranked candidates in neuronal cells, identifying drug combinations with efficacy in reducing oxidative stress and safety in maintaining neuronal viability and morphology. Our neuronal response experiments confirmed several biologically efficacious drug combinations. This pipeline showed that harmonizing heterogeneous and complementary data/knowledge, including human interactome, transcriptome patterns, experimental efficacy, and real-world patient data shed light on the drug development of complex diseases.

An End-to-end In-Silico and In-Vitro Drug Repurposing Pipeline for Glioblastoma.

Our study aims to address the challenges in drug development for glioblastoma, a highly aggressive brain cancer with poor prognosis. We propose a computational framework that utilizes machine learning-based propensity score matching to estimate counterfactual treatment effects and predict synergistic effects of drug combinations. Through our in-silico analysis, we identified promising drug candidates and drug combinations that warrant further investigation. To validate these computational findings, we conducted in-vitro experiments on two GBM cell lines, U87 and T98G. The experimental results demonstrated that some of the identified drugs and drug combinations indeed exhibit strong suppressive effects on GBM cell growth. Our end-to-end pipeline showcases the feasibility of integrating computational models with biological experiments to expedite drug repurposing and discovery efforts. By bridging the gap between in-silico analysis and in-vitro validation, we demonstrate the potential of this approach to accelerate the development of novel and effective treatments for glioblastoma.

MitoTox: a comprehensive mitochondrial toxicity database.

BACKGROUND: Mitochondria play essential roles in regulating cellular functions. Some drug treatments and molecular interventions have been reported to have off-target effects damaging mitochondria and causing severe side effects. The development of a database for the management of mitochondrial toxicity-related molecules and their targets is important for further analyses. RESULTS: To correlate chemical, biological and mechanistic information on clinically relevant mitochondria-related toxicity, a comprehensive mitochondrial toxicity database (MitoTox) was developed. MitoTox is an electronic repository that integrates comprehensive information about mitochondria-related toxins and their targets. Information and data related to mitochondrial toxicity originate from various sources, including scientific journals and other electronic databases. These resources were manually verified and extracted into MitoTox. The database currently contains over 1400 small-molecule compounds, 870 mitochondrial targets, and more than 4100  mitochondrial toxin-target associations. Each MitoTox data record contains over 30 fields, including biochemical properties, therapeutic classification, target proteins, toxicological data, mechanistic information, clinical side effects, and references. CONCLUSIONS: MitoTox provides a fully searchable database with links to references and other databases. Potential applications of MitoTox include toxicity classification, prediction, reference and education. MitoTox is available online at http://www.mitotox.org .