Corrections of Molecular Morphology and Hydrogen Bond for Improved Crystal Density Prediction

Amentoflavone derivatives significantly act towards the main protease (3CLPRO/MPRO) of SARS-CoV-2: in silico admet profiling, molecular docking, molecular dynamics simulation, network pharmacology

SARS-CoV-2 is the foremost culprit of the novel coronavirus disease 2019 (nCoV-19 and/or simply COVID-19) and poses a threat to the continued life of humans on the planet and create pandemic issue globally. The 3-chymotrypsin-like protease (MPRO or 3CLPRO) is the crucial protease enzyme of SARS-CoV-2, which directly involves the processing and release of translated non-structural proteins (nsps), and therefore involves the development of virus pathogenesis along with outbreak the forecasting of COVID-19 symptoms. Moreover, SARS-CoV-2 infections can be inhibited by plant-derived chemicals like amentoflavone derivatives, which could be used to develop an anti-COVID-19 drug. Our research study is designed to conduct an in silico analysis on derivatives of amentoflavone (isoginkgetin, putraflavone, 4''''''-methylamentoflavone, bilobetin, ginkgetin, sotetsuflavone, sequoiaflavone, heveaflavone, kayaflavone, and sciadopitysin) for targeting the non-structural protein of SARS-CoV-2, and subsequently further validate to confirm their antiviral ability. To conduct all the in silico experiments with the derivatives of amentoflavone against the MPRO protein, both computerized tools and online servers were applied; notably the software used is UCSF Chimera (version 1.14), PyRx, PyMoL, BIOVIA Discovery Studio tool (version 4.5), YASARA (dynamics simulator), and Cytoscape. Besides, as part of the online tools, the SwissDME and pKCSM were employed. The research study was proposed to implement molecular docking investigations utilizing compounds that were found to be effective against the viral primary protease (MPRO). MPRO protein interacted strongly with 10 amentoflavone derivatives. Every time, amentoflavone compounds outperformed the FDA-approved antiviral medicine that is currently underused in COVID-19 in terms of binding affinity (- 8.9, - 9.4, - 9.7, - 9.1, - 9.3, - 9.0, - 9.7, - 9.3, - 8.8, and - 9.0 kcal/mol, respectively). The best-selected derivatives of amentoflavone also possessed potential results in 100 ns molecular dynamic simulation (MDS) validation. It is conceivable that based on our in silico research these selected amentoflavone derivatives more precisely 4''''''-methylamentoflavone, ginkgetin, and sequoiaflavone have potential for serving as promising lead drugs against SARS-CoV-2 infection. In consequence, it is recommended that additional in vitro as well as in vivo research studies have to be conducted to support the conclusions of this current research study.

Density Prediction Models for Energetic Compounds Merely Using Molecular Topology

Newly developed high-throughput methods for property predictions make the process of materials design faster and more efficient. Density is an important physical property for energetic compounds to assess detonation velocity and detonation pressure, but the time cost of recent density prediction models is still high owing to the time-consuming processes to calculate molecular descriptors. To improve the screening efficiency of potential energetic compounds, new methods for density prediction with more accuracy and less time cost are urgently needed, and a possible solution is to establish direct mappings between the molecular structure and density. We propose three machine learning (ML) models, support vector machine (SVM), random forest (RF), and Graph neural network (GNN), using molecular topology as the only known input. The widely applied quantitative structure-property relationship based on the density functional theory (DFT-QSPR) is adopted as the benchmark to evaluate the accuracies of the models. All these four models are trained and tested by using the same data set enclosing over 2000 reported nitro compounds searched out from the Cambridge Structural Database. The proportions of compounds with prediction error less than 5% are evaluated by using the independent test set, and the values for the models of SVM, RF, DFT-QSPR, and GNN are 48, 63, 85, and 88%, respectively. The results show that, for the models of SVM and RF, fingerprint bit vectors alone are not facilitated to obtain good QSPRs. Mapping between the molecular structure and density can be well established by using GNN and molecular topology, and its accuracy is slightly better than that of the time-consuming DFT-QSPR method. The GNN-based model has higher accuracy and lower computational resource cost than the widely accepted DFT-QSPR model, so it is more suitable for high-throughput screening of energetic compounds.