A machine learning regression model for the screening and design of potential SARS-CoV-2 protease inhibitors.

The widespread infection caused by the 2019 novel corona virus (SARS-CoV-2) has initiated global efforts to search for antiviral agents. Drug discovery is the first step in the development of commercially viable pharmaceutical products to deal with novel diseases. In an effort to accelerate the screening and drug discovery workflow for potential SARS-CoV-2 protease inhibitors, a machine learning model that can predict the binding free energies of compounds to the SARS-CoV-2 main protease is presented. The optimized multiple linear regression model, which was trained and tested on 226 natural compounds demonstrates reliable prediction performance (r 2 test = 0.81, RMSE test = 0.43), while only requiring five topological descriptors. The externally validated model can help conserve and maximize available resources by limiting biological assays to compounds that yielded favorable outcomes from the model. The emergence of highly infectious diseases will always be a threat to human health and development, which is why the development of computational tools for rapid response is very important. SUPPLEMENTARY INFORMATION: The online version contains supplementary material available at 10.1007/s13721-021-00326-2.

Stable π-π dependent electron conduction band of TPP[M(Pc)L2]2 molecular conductors (TPP = tetraphenylphosphonium; M = Co, Fe; Pc = phthalocyaninato; L = CN, Cl, Br).

The partially-oxidized TPP[M(Pc)L(2)](2) molecular conductors exhibit variable electronic and magnetic transport bulk materials properties due to central metal and axial ligand molecular modifications. The controllable electrical conductivity and giant negative magnetoresistance can be mainly attributable to the varying ligand field energy and physical bulkiness of the axial ligands which cause modulation in the intra-molecular π-d (Pc-M) and inter-molecular π-π (Pc-Pc) interactions in the TPP[M(Pc)L(2)](2) system, respectively. Characterization of the electronic conduction band utilizing one-dimensional (1-D) tight-binding approximation from infrared reflectance and thermoelectric power profile reveal consistent band widths of 0.43 eV-0.62 eV for the Co series (L = Br < Cl < CN) and 0.44-0.56 eV for the Fe series (L = Br < Cl < CN). The fixed band width suggests that stable electron conduction bands (transport pathway) can be constructed which can withstand the molecular π-d interaction modifications that severely alter the bulk electronic and magnetic materials properties of the TPP[M(Pc)L(2)](2) molecular conductors.