AIMC Topic: COVID-19 Drug Treatment

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Discovery of Tetrahydroisoquinoline-Based SARS-CoV-2 Helicase Inhibitors with Iterative, Deep Learning-Enhanced Virtual Screening.

Journal of chemical information and modeling
In this study, we pursued a structure-based drug discovery campaign targeting the SARS-CoV-2 helicase through three rounds of virtual screening (VS) enhanced with Artificial Intelligence (AI). The third round incorporated a deep neural network (DNN) ...

Deep Learning vs Classical Methods in Potency and ADME Prediction: Insights from a Computational Blind Challenge.

Journal of chemical information and modeling
Reliable prediction of compound potency and the ADME profile is crucial in drug discovery. With the recent surge of AI and deep learning frameworks, it remains unclear whether these modern techniques offer statistically significant improvement over t...

Analytical and machine learning approaches identify a sea star steroid with promising activity for COVID-19 therapeutic development.

Scientific reports
The pressing demand for safe and efficient COVID-19 treatments has intensified interest in Natural products, especially those derived from marine organisms. In this study, a bioactive steroidal compound, 5α-cholesta-9(11)-en-3β,20β-diol, was successf...

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...

Biomimetic magnetobacterial microrobots for active pneumonia therapy.

Nature communications
Immense progress in synthetic micro-/nanorobots with diverse functionalities has been made for biomedical applications during the last decade. However, there is still a huge gap for miniature robots to realize efficient therapy from in vitro to in vi...

Ionic liquids and lysosomotropic detergents as inhibitors of the SARS-CoV-2 main protease: QSAR modeling, synthesis and biological testing.

Biochemical and biophysical research communications
SARS-CoV-2 infection is highly contagious, prompting the World Health Organization to classify it as a global public health emergency. The virus has numerous potential hosts, which complicates efforts for effective prevention, diagnosis, and treatmen...

Discovery of SARS-CoV-2 papain-like protease inhibitors through machine learning and molecular simulation approaches.

Drug discoveries & therapeutics
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
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...

VAE-GANMDA: A microbe-drug association prediction model integrating variational autoencoders and generative adversarial networks.

Artificial intelligence in medicine
Traditional biological experimental methods typically require weeks or even months of experimentation, and the cost of each experiment can reach hundreds or even thousands of dollars, which is quite expensive and time-consuming. To address this, a mo...