Clinical connectivity map for drug repurposing: using laboratory results to bridge drugs and diseases

Innovative applications of artificial intelligence during the COVID-19 pandemic

The COVID-19 pandemic has created unprecedented challenges worldwide. Artificial intelligence (AI) technologies hold tremendous potential for tackling key aspects of pandemic management and response. In the present review, we discuss the tremendous possibilities of AI technology in addressing the global challenges posed by the COVID-19 pandemic. First, we outline the multiple impacts of the current pandemic on public health, the economy, and society. Next, we focus on the innovative applications of advanced AI technologies in key areas such as COVID-19 prediction, detection, control, and drug discovery for treatment. Specifically, AI-based predictive analytics models can use clinical, epidemiological, and omics data to forecast disease spread and patient outcomes. Additionally, deep neural networks enable rapid diagnosis through medical imaging. Intelligent systems can support risk assessment, decision-making, and social sensing, thereby improving epidemic control and public health policies. Furthermore, high-throughput virtual screening enables AI to accelerate the identification of therapeutic drug candidates and opportunities for drug repurposing. Finally, we discuss future research directions for AI technology in combating COVID-19, emphasizing the importance of interdisciplinary collaboration. Though promising, barriers related to model generalization, data quality, infrastructure readiness, and ethical risks must be addressed to fully translate these innovations into real-world impacts. Multidisciplinary collaboration engaging diverse expertise and stakeholders is imperative for developing robust, responsible, and human-centered AI solutions against COVID-19 and future public health emergencies.

Computational drug repurposing based on electronic health records: a scoping review

Computational drug repurposing methods adapt Artificial intelligence (AI) algorithms for the discovery of new applications of approved or investigational drugs. Among the heterogeneous datasets, electronic health records (EHRs) datasets provide rich longitudinal and pathophysiological data that facilitate the generation and validation of drug repurposing. Here, we present an appraisal of recently published research on computational drug repurposing utilizing the EHR. Thirty-three research articles, retrieved from Embase, Medline, Scopus, and Web of Science between January 2000 and January 2022, were included in the final review. Four themes, (1) publication venue, (2) data types and sources, (3) method for data processing and prediction, and (4) targeted disease, validation, and released tools were presented. The review summarized the contribution of EHR used in drug repurposing as well as revealed that the utilization is hindered by the validation, accessibility, and understanding of EHRs. These findings can support researchers in the utilization of medical data resources and the development of computational methods for drug repurposing.

Probing the Action of Screened Anticancer Triazole–Tetrazole Derivatives Against COVID-19 Using Molecular Docking and DFT Investigations

Drugs are continuously being evaluated for novel therapeutic uses. The purpose of this work was to screen anticancer triazole/tetrazole derivatives for effectiveness against the SARS-CoV-2 main protease (Mpro). First, the chemical structures’ activity was derived from conceptual quantum chemical calculations. According to molecular docking analysis, the compounds scored good interactions against SAR-COV-2's Mpro, with binding energies extending from −8.21 to −8.97 kcal/mol. The docked complexes included various bindings with His41 and Cys145, both catalytic residues responsible for cleavage of the SARS-CoV-2 Mpro. Among the 4 studied compounds, TD3 exhibited the highest affinity by achieving the most stable binding energy and lowest value for the inhibition constant. Most striking was that TD3 not only formed strong bonds with the catalytic residues His41 and Cys145, but also captured the residues of the catalytic loop (Cys44 to Pro52), which flank the catalytic dyads in Mpro's active site. As a result, the studied triazole/tetrazole derivatives, notably TD3, must be reviewed as potent drugs that could be repurposed for SARS-CoV-2 treatment.