Virtual screening articles from across Nature Portfolio

Reduced molecular graphs can integrate higher-level chemical information and leverage advantages from atom-level graph neural networks. Here, the authors introduce the Multiple Molecular Graph eXplainable model, investigating the effects of multiple molecular graphs, including Atom, Pharmacophore, JunctionTree, and FunctionalGroup, on model learning and interpretation from various perspectives

Molecular fragmentation plays a pivotal role in medicinal chemistry and drug discovery. Here the authors summarize the methodologies for molecular fragmentation and their application in the AI-based fragment-based drug discovery.

Computational approaches are emerging as powerful tools for the discovery of antibiotics. A study now uses machine learning to discover abaucin, a potent antibiotic that targets the bacterial pathogen Acinetobacter baumannii.

Deep learning approaches have potential to substantially reduce the astronomical costs and long timescales involved in drug discovery. KarmaDock proposes a deep learning workflow for ligand docking that shows improved performance against both benchmark cases and in a real-world virtual screening experiment.

As clinicians and scientists gather more data on the clinical trajectory of COVID-19 and the biology of its causative agent, the SARS-CoV-2 virus, novel strategies are needed to integrate these data to inform new therapies. A recent study by Howell et al. introduces a network model of viral-host interactions to produce explainable and testable predictions for treatment effects. Their model was consistent with experimental data and recommended treatments, and one of its predicted drug combinations was validated through in vitro assays. These findings support the utility of computational strategies for leveraging the vast literature on COVID-19 to generate insights for drug repurposing.

Drug resistance in tropical diseases such as malaria requires constant improvement and development of new drugs. To find potential candidates, generative machine learning methods that can search for novel bioactive molecules can be employed.

A special prize for outstanding research in high-performance computing (HPC) applied to COVID-19-related challenges sheds light on the computational science community’s efforts to fight the ongoing global crisis.

Pesticides assure control over the disease vectors; however, most pests have developed resistance to the major classes of pesticides. Synergistic approaches in which pesticides are combined with inhibitors of the specific resistance-mediating enzymes have been now identified as promising solutions.