AIMC Topic: COVID-19 Drug Treatment
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Discovery of SARS-CoV-2 papain-like protease inhibitors through machine learning and molecular simulation approaches.
Drug discoveries & therapeutics
Jun 27, 2025
The papain-like protease (PLpro), a cysteine protease found in severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), plays a crucial role in viral replication by cleaving the viral polyproteins and interfering with the host's innate immune re...
In-silico study of approved drugs as potential inhibitors against 3CLpro and other viral proteins of CoVID-19.
PloS one
Jun 25, 2025
The global pandemic, due to the emergence of COVID-19, has created a public health crisis. It has a huge morbidity rate that was never comprehended in the recent decades. Despite numerous efforts, potent antiviral drugs are lacking. Repurposing of dr...
Remdesivir associated with reduced mortality in hospitalized COVID-19 patients: treatment effectiveness using real-world data and natural language processing.
BMC infectious diseases
Apr 12, 2025
BACKGROUND: Remdesivir (RDV) was the first antiviral approved for mild-to-moderate COVID-19 and for those patients at risk for progression to severe disease after clinical trials supported its association with improved outcomes. Real-world evidence (...
Virtual reality in drug design: Benefits, applications and industrial perspectives.
Current opinion in structural biology
Apr 7, 2025
Virtual reality (VR) is a tool which has transformative potential in domains which involve the visualization of complex 3D data such as structure-based drug design (SBDD), where it offers new ways to visualize and manipulate complex molecular structu...
A Knowledge-Guided Graph Learning Approach Bridging Phenotype- and Target-Based Drug Discovery.
Advanced science (Weinheim, Baden-Wurttemberg, Germany)
Mar 6, 2025
Discovering therapeutic molecules requires the integration of both phenotype-based drug discovery (PDD) and target-based drug discovery (TDD). However, this integration remains challenging due to the inherent heterogeneity, noise, and bias present in...
Deep mutational learning for the selection of therapeutic antibodies resistant to the evolution of Omicron variants of SARS-CoV-2.
Nature biomedical engineering
Mar 5, 2025
Most antibodies for treating COVID-19 rely on binding the receptor-binding domain (RBD) of SARS-CoV-2 (severe acute respiratory syndrome coronavirus 2). However, Omicron and its sub-lineages, as well as other heavily mutated variants, have rendered m...
Characterizing Public Sentiments and Drug Interactions in the COVID-19 Pandemic Using Social Media: Natural Language Processing and Network Analysis.
Journal of medical Internet research
Mar 5, 2025
BACKGROUND: While the COVID-19 pandemic has induced massive discussion of available medications on social media, traditional studies focused only on limited aspects, such as public opinions, and endured reporting biases, inefficiency, and long collec...
Applications of Machine Learning Approaches for the Discovery of SARS-CoV-2 PLpro Inhibitors.
Journal of chemical information and modeling
Jan 16, 2025
The global impact of SARS-CoV-2 highlights the need for treatments beyond vaccination, given the limited availability of effective medications. While Pfizer introduced , an FDA-approved antiviral targeting the SARS-CoV-2 main protease (Mpro), this st...
MVCL-DTI: Predicting Drug-Target Interactions Using a Multiview Contrastive Learning Model on a Heterogeneous Graph.
Journal of chemical information and modeling
Jan 15, 2025
Accurate prediction of drug-target interactions (DTIs) is pivotal for accelerating the processes of drug discovery and drug repurposing. MVCL-DTI, a novel model leveraging heterogeneous graphs for predicting DTIs, tackles the challenge of synthesizin...
A versatile attention-based neural network for chemical perturbation analysis and its potential to aid surgical treatment: an experimental study.
International journal of surgery (London, England)
Dec 1, 2024
Deep learning models have emerged as rapid, accurate, and effective approaches for clinical decisions. Through a combination of drug screening and deep learning models, drugs that may benefit patients before and after surgery can be discovered to red...
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