ArticleBriefings in bioinformatics2021
A novel framework integrating AI model and enzymological experiments promotes identification of SARS-CoV-2 3CL protease inhibitors and activity-based probe.
Article in Briefings in bioinformatics, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 8 papers.
What it found
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Who cites it
8 citing papers in PubMed, 15 citations in OpenAlex.
- IUPAC-GPT: an IUPAC-based large-scale molecular pre-trained model for property prediction and molecule generation.Molecular diversity · 2026Article
- Transformer-Based Molecular Generative Model for Antiviral Drug Design.Journal of chemical information and modeling · 2024Article
- A Multimodal Protein Representation Framework for Quantifying Transferability Across Biochemical Downstream Tasks.Advanced science (Weinheim, Baden-Wurttemberg, Germany) · 2023Article
- Discovery of novel SARS-CoV-2 3CL protease covalent inhibitors using deep learning-based screen.European journal of medicinal chemistry · 2022Article
- Zinc pyrithione is a potent inhibitor of PLJournal of enzyme inhibition and medicinal chemistry · 2022Article
- Prediction of Recurrent Mutations in SARS-CoV-2 Using Artificial Neural Networks.International journal of molecular sciences · 2022Article
- Prediction of Potential Commercially Available Inhibitors against SARS-CoV-2 by Multi-Task Deep Learning Model.Biomolecules · 2022Article
- Deep learning-based molecular dynamics simulation for structure-based drug design against SARS-CoV-2.Computational and structural biotechnology journal · 2022Review
Corrections and comments
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Authors and funding
8 authors at 2 institutions in 1 country.
Funding
Abstract
The identification of protein-ligand interaction plays a key role in biochemical research and drug discovery. Although deep learning has recently shown great promise in discovering new drugs, there remains a gap between deep learning-based and experimental approaches. Here, we propose a novel framework, named AIMEE, integrating AI model and enzymological experiments, to identify inhibitors against 3CL protease of SARS-CoV-2 (Severe acute respiratory syndrome coronavirus 2), which has taken a significant toll on people across the globe. From a bioactive chemical library, we have conducted two rounds of experiments and identified six novel inhibitors with a hit rate of 29.41%, and four of them showed an IC50 value <3 μM. Moreover, we explored the interpretability of the central model in AIMEE, mapping the deep learning extracted features to the domain knowledge of chemical properties. Based on this knowledge, a commercially available compound was selected and was proven to be an activity-based probe of 3CLpro. This work highlights the great potential of combining deep learning models and biochemical experiments for intelligent iteration and for expanding the boundaries of drug discovery. The code and data are available at https://github.com/SIAT-code/AIMEE.
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Registered trials
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