Wenjing Guo Source Confirmed

Affiliation confirmed via AI analysis of OpenAlex, ORCID, and web sources.

High Impact

Researcher

National Center for Toxicological Research

faculty

30 h-index 194 pubs 3,264 cited

Research Areas

Advanced Neural Network Applications Nanoparticles: synthesis and applications Pharmacological Effects and Toxicity Studies SARS-CoV-2 and COVID-19 Research Protein Structure and Dynamics Remote Sensing in Agriculture Bioinformatics and Genomic Networks Meta-analysis and systematic reviews

Links

ORCID Research Group

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OverviewAI-generated summary

Wenjing Guo's research focuses on the application of computational methods, particularly machine learning and deep learning, to predict the toxicity and biological activity of chemical compounds and nanomaterials. This work aims to inform the design and risk assessment of new materials. Guo has investigated the cytotoxicity of nanomaterials and reviewed existing machine learning and deep learning models used for toxicity prediction.

Further research interests include molecular dynamics simulations to elucidate protein interactions, such as those between the SARS-CoV-2 spike protein and ACE2. Guo has also explored the fibrinolytic activity of cysteine-derived chiral carbon quantum dots for potential applications in Type 2 Diabetes Mellitus. Additional work involves the retrieval and mapping of soil organic carbon using remote sensing data.

Guo leads a research group and has a significant publication record, with 194 total publications and 3,264 citations, achieving an h-index of 30. Key collaborators include Tucker A. Patterson, Sugunadevi Sakkiah, Fan Dong, and Weigong Ge, all from the National Center for Toxicological Research, with whom Guo has multiple shared publications.

Metrics

h-index: 30
Publications: 194
Citations: 3,264

Selected Publications

Analysis of Structures of SARS-CoV-2 Papain-like Protease Bound with Ligands Unveils Structural Features for Inhibiting the Enzyme (2025) DOI
Machine learning and deep learning approaches for enhanced prediction of hERG blockade: a comprehensive QSAR modeling study (2024) DOI
BERT-based language model for accurate drug adverse event extraction from social media: implementation, evaluation, and contributions to pharmacovigilance practices (2024) DOI
Machine learning and deep learning for brain tumor MRI image segmentation (2023) DOI
Review of machine learning and deep learning models for toxicity prediction (2023) DOI
Developing a SARS-CoV-2 main protease binding prediction random forest model for drug repurposing for COVID-19 treatment (2023) DOI
Deep Learning Models for Predicting Gas Adsorption Capacity of Nanomaterials (2022) DOI
Machine learning models for rat multigeneration reproductive toxicity prediction (2022) DOI
Machine Learning Models for Predicting Liver Toxicity (2022) DOI
Assessing reproducibility of inherited variants detected with short-read whole genome sequencing (2022) DOI
Machine Learning Models for Predicting Cytotoxicity of Nanomaterials (2022) DOI
Elucidation of Agonist and Antagonist Dynamic Binding Patterns in ER-α by Integration of Molecular Docking, Molecular Dynamics Simulations and Quantum Mechanical Calculations (2021) DOI
Informing selection of drugs for COVID-19 treatment through adverse events analysis (2021) DOI
Nanomaterial Databases: Data Sources for Promoting Design and Risk Assessment of Nanomaterials (2021) DOI
BPA Replacement Compounds: Current Status and Perspectives (2021) DOI