Machine-Learning-Guided Discovery of RdRp Inhibitors for Hepatitis C Virus with QM/MM Characterization

Bridging the antiviral drug design gap: a combined machine learning and QSAR approach for drug repurposing of host kinase inhibitors

Viral outbreaks combined with rapid emergence of mutated viruses have highlighted an urging need for accelerating antiviral drug discovery pipelines. Unfortunately, current drug discovery remains stuck to conventional methods which are slow especially during pandemics. In this article, we present a literature-based syn...

Machine Learning-Assisted in Silico Identification of Phytoconstituents as Potential NS5 RdRp Inhibitors for Dengue Therapeutics

The escalating global burden of dengue virus (DENV) infection and the lack of specific antiviral therapies necessitate the development of effective therapeutics targeting the highly conserved non-structural protein 5 RNA-dependent RNA polymerase (NS5 RdRp). This study employed an integrated computer-aided drug discover...

Machine Learning‐Based Discovery of Natural PknB Inhibitors Against Drug‐Resistant Mycobacterium tuberculosis With Multi‐Scale Modeling and Quantum Mechanical Validation

An integrated computational workflow to identify natural small molecules with high potential to bind protein kinase B (PknB) identifies promising PknB inhibitors for further experimental validation and offers an efficient platform to accelerate natural‐product‐based anti‐TB drug discovery.

Mutation-Aware Machine Learning Framework for Predicting Binding Affinity of Nirmatrelvir Analogs Targeting Coronavirus Main Proteases

The emergence of resistance-associated mutations in coronavirus main protease (Mpro) poses a significant challenge to the development of broad-spectrum antiviral therapeutics. In this study, we improved and accelerated a mutation-aware machine learning (ML) framework to predict the binding score of Nirmatrelvir analogu...

Machine learning-guided QSAR screening of fluconazole analogs and FDA-approved drugs against Candida albicans, with docking, molecular dynamics, and ADMET analysis.

Discovery of a potent TDP1 inhibitor through machine learning-driven predictive modeling combined with structure-based virtual screening and experimental validation

An integrated computational framework combining machine learning (ML), deep learning (DL), and structure-based docking with experimental validation identifies AO65 as a promising lead for further TDP1-focused investigation.