Repurposed drugs bound to fatty acid binding pocket of SARS-CoV-2 spike

PDB files of molecular dynamics structures of top drug repurposing candidates bound to the free fatty acid binding pocket of the SARS-CoV-2 spike protein</p

Computational repurposing of drugs for viral diseases and current and future pandemics

A large fraction of the world’s population is directly impacted by acute or chronic viral infections, many of which have high mortality. As was brought home to us in 2020, viruses also have great potential to generate global pandemics that have killed millions and caused massive damage to economies. Clearly, we need cost-effective and rapid methods for finding drug treatments for poorly met infectious diseases and for responding effectively to the current and future pandemics. Repurposing or off-label use of existing drugs, whose safety and pharmacokinetics are well understood, is one useful way to provide fast drug therapies for patients. Computational methods have an important role to play because of their increasing effectiveness, high speed, and relatively low cost. Here we review the application of the main types of computational drug repurposing methods to discovery of therapies for viral diseases and for future pandemics highly likely to be caused by viral pathogens. </p

Predicting the performance of organic corrosion inhibitors

The withdrawal of effective but toxic corrosion inhibitors has provided an impetus for the discovery of new, benign organic compounds to fill that role. Concurrently, developments in the high-throughput synthesis of organic compounds, the establishment of large libraries of available chemicals, accelerated corrosion inhibition testing technologies, and the increased capability of machine learning methods have made discovery of new corrosion inhibitors much faster and cheaper than it used to be. We summarize these technical developments in the corrosion inhibition field and describe how data-driven machine learning methods can generate models linking molecular properties to corrosion inhibition that can be used to predict the performance of materials not yet synthesized or tested. We briefly summarize the literature on quantitative structure-property relationships models of small organic molecule corrosion inhibitors. The success of these models provides a paradigm for rapid discovery of novel, effective corrosion inhibitors for a range of metals and alloys in diverse environments

Probing the properties of molecules and complex materials using machine learning

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Small organic ligands for the ecdysone receptor – agrochemicals, gene switches, and beyond

While pesticides are essential for the world to meet its increasing demand for food, off-target toxicity in humans and other species is an ongoing environmental issue. There is a strong motivation for developing more selective pesticides that can target pest insects, for example, while being benign for beneficial insects such as bees, and other nontarget species more generally. The ecdysone receptor is absent in vertebrates so constitutes a very useful target for green insecticides. It has also been found to be an extremely useful gene switch in molecular biology. While the natural ecdysone ligands are complex steroidal compounds, a wide range of simpler synthetic agonists and antagonists have been developed. Here we review the diversity of chemotypes that have been shown to bind productively to the ecdysone receptor and have found application as insecticides and gene switches. We discuss the similarities and differences in these chemotypes, the origin of which is the remarkable flexibility of the ligand binding domain in the receptor. We provide a perspective on the discovery of further useful chemotypes with potentially higher binding and selectivity between pest and beneficial insects.</p

Discovery and Design of Radiopharmaceuticals by In silico Methods

No description supplied</p

SARS-Cov-2 spike-ACE2 complexes for multiple species

Final protein complex structure files (.pdb) for SARS-Cov-2 bound to ACE2 proteins from multiple species. Derived from molecular dynamics simulation of complexes after docking

Computational Repurposing of Drugs and Natural Products Against SARS-CoV-2

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Identifying factors controlling cellular uptake of gold nanoparticles by machine learning

No description supplied</p

Computationally repurposed drugs and natural products against RNA dependent RNA polymerase as potential COVID-19 therapies

Repurposing of existing drugs and drug candidates is an ideal approach to identify new potential therapies for SARS-CoV-2 that can be tested without delay in human trials of infected patients. Here we applied a virtual screening approach using Autodock Vina and molecular dynamics simulation in tandem to calculate binding energies for repurposed drugs against the SARS-CoV-2 RNA-dependent RNA polymerase (RdRp). We thereby identified 80 promising compounds with potential activity against SARS-Cov2, consisting of a mixture of antiviral drugs, natural products and drugs with diverse modes of action. A substantial proportion of the top 80 compounds identified in this study had been shown by others to have SARS-CoV-2 antiviral effects in vitro or in vivo, thereby validating our approach. Amongst our top hits not previously reported to have SARS-CoV-2 activity, were eribulin, a macrocyclic ketone analogue of the marine compound halichondrin B and an anticancer drug, the AXL receptor tyrosine kinase inhibitor bemcentinib. Our top hits from our RdRp drug screen may not only have utility in treating COVID-19 but may provide a useful starting point for therapeutics against other coronaviruses. Hence, our modelling approach successfully identified multiple drugs with potential activity against SARS-CoV-2 RdRp