COVID-19Base v3: Update of the knowledgebase for drugs and biomedical entities linked to COVID-19

Abstract

COVID-19 has taken a huge toll on our lives over the last 3 years. Global initiatives put forward by all stakeholders are still in place to combat this pandemic and help us learn lessons for future ones. While the vaccine rollout was not able to curb the spread of the disease for all strains, the research community is still trying to develop effective therapeutics for COVID-19. Although Paxlovid and remdesivir have been approved by the FDA against COVID-19, they are not free of side effects. Therefore, the search for a therapeutic solution with high efficacy continues in the research community. To support this effort, in this latest version (v3) of COVID-19Base, we have summarized the biomedical entities linked to COVID-19 that have been highlighted in the scientific literature after the vaccine rollout. Eight different topic-specific dictionaries, i.e., gene, miRNA, lncRNA, PDB entries, disease, alternative medicines registered under clinical trials, drugs, and the side effects of drugs, were used to build this knowledgebase. We have introduced a BLSTM-based deep-learning model to predict the drug-disease associations that outperforms the existing model for the same purpose proposed in the earlier version of COVID-19Base. For the very first time, we have incorporated disease-gene, disease-miRNA, disease-lncRNA, and drug-PDB associations covering the largest number of biomedical entities related to COVID-19. We have provided examples of and insights into different biomedical entities covered in COVID-19Base to support the research community by incorporating all of these entities under a single platform to provide evidence-based support from the literature. COVID-19Base v3 can be accessed from: https://covidbase-v3.vercel.app/. The GitHub repository for the source code and data dictionaries is available to the community from: https://github.com/91Abdullah/covidbasev3.0.

AI in drug discovery and its clinical relevance

“…All stages of the drug discovery pipeline could benefit from AI [11] , for example, generative models for the design of new synthetic molecules [30] , reinforcement learning (RL) to optimize properties of molecules in a particular direction [31] , GNNs to predict drug-disease associations, drug-repurposing, and the response to a drug [32] . Natural language processing (NLP) could be used to find drugs by mining the scientific literature and to automate FDA approval steps [33] , [34] . These applications of data science to drug discovery are discussed in (Section 2 ).…”

Large Language Models: A Comprehensive Survey of its Applications, Challenges, Limitations, and Future Prospects

“…The selection of these fields based on their significance, relevance, and potential impact within their respective domains. These applications demonstrate the versatility and potential of LLMs in addressing complex challenges and supporting human endeavors [260], [315]. An interactive framework for the integration of LLMs in the healthcare ecosystem is shown in Figure 6.…”

Personalized Assessment of Mortality Risk and Hospital Stay Duration in Hospitalized Patients with COVID-19 Treated with Remdesivir: A Machine Learning Approach

“…Drug repurposing trials have aimed at finding potential treatments for COVID-19, including antiviral therapies, anti-inflammatory drugs, antithrombotic agents, and immunomodulators [37]. In a study by Basit SA et al, a deep learning model was employed to predict the effectiveness of different medications, identifying remdesivir as highly effective against COVID-19 with a 95% positive score [38].…”