COVID-19 Knowledge Extractor (COKE): A Curated Repository of Drug-Target Associations Extracted from the CORD-19 Corpus of Scientific Publications on COVID-19

Towards Semantic Smart Cities: A Study on the Conceptualization and Implementation of Semantic Context Inference Systems

Smart cities provide integrated management and operation of urban data emerging within a city, supplying the infrastructure for smart city services and resolving various urban challenges. Nevertheless, cities continue to grapple with substantial issues, such as contagious diseases and terrorism, that pose severe financial and human risks. These problems sporadically arise in various locales, and current smart city frameworks lack the capability to autonomously identify and address these issues. The challenge intensifies especially when trying to recognize and respond to unprecedented problems. The primary objective of this research is to predict potential urban issues and support their resolution proactively. To achieve this, our system makes use of semantic reasoning to understand the ongoing situations within the city. In this process, the 5W1H principles serve as inference rules, guiding the extraction and consolidation of context. Firstly, utilizing domain-specific annotation templates, we craft a semantic graph by amalgamating information from various sources available in the city, such as municipal public data and IoT platforms. Subsequently, the system autonomously infers and accumulates contexts of situations occurring in the city using 5W1H-based reasoning. As a result, the accumulated contexts allow for inferring potential urban problems by identifying repeated disruptions in city services at specific times or locations and establishing connections among them. The main contribution of this paper lies in proposing a comprehensive conceptual model for the suggested system and presenting actual implementation cases and applicable use cases. These contributions facilitate awareness among city administrators and citizens within a smart city regarding potential problem-prone areas or times, thereby aiding in the preemptive identification and mitigation of urban challenges.

Old drugs, new tricks: leveraging known compounds to disrupt coronavirus-induced cytokine storm

A major complication in COVID-19 infection consists in the onset of acute respiratory distress fueled by a dysregulation of the host immune network that leads to a run-away cytokine storm. Here, we present an in silico approach that captures the host immune system’s complex regulatory dynamics, allowing us to identify and rank candidate drugs and drug pairs that engage with minimal subsets of immune mediators such that their downstream interactions effectively disrupt the signaling cascades driving cytokine storm. Drug–target regulatory interactions are extracted from peer-reviewed literature using automated text-mining for over 5000 compounds associated with COVID-induced cytokine storm and elements of the underlying biology. The targets and mode of action of each compound, as well as combinations of compounds, were scored against their functional alignment with sets of competing model-predicted optimal intervention strategies, as well as the availability of like-acting compounds and known off-target effects. Top-ranking individual compounds identified included a number of known immune suppressors such as calcineurin and mTOR inhibitors as well as compounds less frequently associated for their immune-modulatory effects, including antimicrobials, statins, and cholinergic agonists. Pairwise combinations of drugs targeting distinct biological pathways tended to perform significantly better than single drugs with dexamethasone emerging as a frequent high-ranking companion. While these predicted drug combinations aim to disrupt COVID-induced acute respiratory distress syndrome, the approach itself can be applied more broadly to other diseases and may provide a standard tool for drug discovery initiatives in evaluating alternative targets and repurposing approved drugs.

SMILES-based 2D-QSAR and similarity search for identification of potential new scaffolds for development of SARS-CoV-2 MPRO inhibitors

COVID-19, whose etiological agent is the SARS-CoV-2 virus, has caused over 537.5 million cases and killed over 6.3 million people since its discovery in 2019. Despite the recent development of the first drugs indicated for treating people already infected, the great need to develop new anti-SARS-CoV-2 drugs still exists, mainly due to the possible emergence of new variants of this virus and resistant strains of the current variants. Thus, this work presents the results of QSAR and similarity search studies based only on 2D structures from a set of 32 bicycloproline derivatives, aiming to quickly, reproducibly, and reliably identify potentially useful compounds as scaffolds of new major protease inhibitors (M^pro) of the virus. The obtained QSAR model is based only on topological molecular descriptors. The model has good internal and external statistics, is robust, and does not present a chance correlation. This model was used as one of the tools to support the virtual screening stage carried out in the SwissADME web tool. Five molecules, from an initial set of 2695 molecules, proved to be the most promising, as they were within the model’s applicability domain and linearity range, with low potential to cause carcinogenic, teratogenic, and reproductive toxicity effects and promising pharmacokinetic properties. These five compounds were then selected as the most competent to generate, in future studies, new anti-SARS-CoV-2 agents with drug-likeness properties suitable for use in therapy.