AIMC Topic: Antiviral Agents

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Manifold-constrained nucleus-level denoising diffusion model for structure-based drug design.

Proceedings of the National Academy of Sciences of the United States of America
AI models have shown great potential in structure-based drug design, generating ligands with high binding affinities. However, existing models have often overlooked a crucial physical prior: Atoms must maintain a minimum pairwise distance to avoid at...

Robust Prediction of Protein-Ligand Binding Potency with Multi-modal Customized Gate Control.

Journal of chemical information and modeling
The main protease (Mpro) is a critical target in the design of antiviral drugs against coronaviruses, while accurately predicting the binding affinity between small molecules and this target remains a key challenge. In the recent Polaris challenge of...

Protocol for a core outcome set for pharmacological treatments in hospitalised patients with acute viral respiratory infections (COSAVRI).

PloS one
BACKGROUND: Acute viral respiratory infections (AVRIs) rank among the most common causes of hospitalisation worldwide, imposing significant healthcare burdens and driving the development of pharmacological treatments. However, inconsistent outcome re...

Accelerating Prediction of Antiviral Peptides Using Genetic Algorithm-Based Weighted Multiperspective Descriptors with Self-Normalized Deep Networks.

Journal of chemical information and modeling
The accurate prediction of antiviral peptides (AVPs) plays a crucial role in accelerating the development of peptide-based therapeutics. Despite extensive production of antiviral medications, viral diseases remain a major human health concern. AVPs h...

Serological proteomic characterization for monitoring liver fibrosis regression in chronic hepatitis B patients on treatment.

Nature communications
Longitudinal serological proteomic dynamics during antiviral therapy (AVT) in chronic hepatitis B (CHB) patients with liver fibrosis remain poorly characterized. Here, using four-dimensional data-independent acquisition mass spectrometry (4D-DIA-MS),...

Machine learning-assisted affinity ultrafiltration for bioactive natural products discovery:Application to screening of neuraminidase inhibitors from medicinal herbs.

Analytica chimica acta
BACKGROUND: Bioactive natural products represent a vital resource for combating human diseases. However, their discovery often encounters multiple challenges. Bioactivity-guided isolation can yield bioactive compounds but are labor-intensive and have...

Integrating Physics-Based Simulations with Data-Driven Deep Learning Represents a Robust Strategy for Developing Inhibitors Targeting the Main Protease.

Journal of chemical information and modeling
The coronavirus main protease, essential for viral replication, is a well-validated antiviral target. Here, we present Deep-CovBoost, a computational pipeline integrating deep learning with free energy perturbation (FEP) simulations to guide the stru...

A hybrid supervised and unsupervised machine learning approach for identifying nucleoside drugs using nanopore readouts.

Nanoscale
Nucleoside drugs, mimics of natural nucleosides, have become cornerstone treatments in clinical approaches to combat cancer and viral infections. The analysis of nucleoside drugs is commonly performed using liquid chromatography-tandem mass spectrome...

A dual-view deep learning-driven discovery of cinnamoyl anthranilic acid derivatives against orthopoxvirus through targeting host ITGB3.

European journal of medicinal chemistry
The orthopoxvirus genus, particularly the monkeypox virus (MPXV), continues to pose a significant global public health threat. Therefore, the development of novel anti-orthopoxvirus agents remains an urgent priority. Machine learning has proven to be...

A hybrid framework of generative deep learning for antiviral peptide discovery.

Scientific reports
Antiviral peptides (AVPs) hold great potential for combating viral infections, yet their discovery and development remain challenging. In this study, we present a hybrid model combining Wasserstein Generative Adversarial Networks with Gradient Penalt...