In Silico Investigation of Spice Molecules as Potent Inhibitor of SARS-CoV-2

<p>The severe acute respiratory syndrome Coronavirus 2 (SARS-CoV-2) is a novel infectious disease that is in rapid growth. Several trials are going on worldwide to find a solution for this pandemic. The viral replication can be blocked by inhibiting the SARS-CoV-2 spike protein (SARS-CoV-2 Spro), and the SARS-CoV-2 main protease (SARS-CoV-2 Mpro). The binding of potential small molecules to these proteins can possibly inhibit the replication and transcription of the virus. The spice molecules that are used in our food have the properties of antiviral, antifungal, and antimicrobial nature. As spice molecules are consumed in the diet, hence its antiviral properties against SARS-CoV-2 will benefit in a significant manner. Therefore, in this work, the blind molecular docking of 30 selected spice molecules (through ADME property screening) was performed for the identification of potential inhibitors for the Spro and Mpro of SARS-CoV-2. We found that all the molecules bind actively with the SARS-CoV-2 Spro and Mpro. However, the molecule, Piperine, is found to have the highest binding affinity among the 30 screened molecules. We anticipate immediate wet-lab experiments and clinical trials in support of this computational study might be helpful in inhibiting the SARS-CoV-2 virus.</p>

In Silico Investigation of Spice Molecules as Potent Inhibitor of SARS-CoV-2

<p>The severe acute respiratory syndrome Coronavirus 2 (SARS-CoV-2) is a novel infectious disease that is in rapid growth. Several trials are going on worldwide to find a solution for this pandemic. The viral replication can be blocked by inhibiting the SARS-CoV-2 spike protein (SARS-CoV-2 Spro), and the SARS-CoV-2 main protease (SARS-CoV-2 Mpro). The binding of potential small molecules to these proteins can possibly inhibit the replication and transcription of the virus. The spice molecules that are used in our food have the properties of antiviral, antifungal, and antimicrobial nature. As spice molecules are consumed in the diet, hence its antiviral properties against SARS-CoV-2 will benefit in a significant manner. Therefore, in this work, the blind molecular docking of 30 selected spice molecules (through ADME property screening) was performed for the identification of potential inhibitors for the Spro and Mpro of SARS-CoV-2. We found that all the molecules bind actively with the SARS-CoV-2 Spro and Mpro. However, the molecule, Piperine, is found to have the highest binding affinity among the 30 screened molecules. We anticipate immediate wet-lab experiments and clinical trials in support of this computational study might be helpful in inhibiting the SARS-CoV-2 virus.</p>

Chemi-Net: A Molecular Graph Convolutional Network for Accurate Drug Property Prediction

Absorption, distribution, metabolism, and excretion (ADME) studies are critical for drug discovery. Conventionally, these tasks, together with other chemical property predictions, rely on domain-specific feature descriptors, or fingerprints. Following the recent success of neural networks, we developed Chemi-Net, a completely data-driven, domain knowledge-free, deep learning method for ADME property prediction. To compare the relative performance of Chemi-Net with Cubist, one of the popular machine learning programs used by Amgen, a large-scale ADME property prediction study was performed on-site at Amgen. For all 13 data sets, Chemi-Net resulted in higher R2 values compared with the Cubist benchmark. The median R2 increase rate over Cubist was 26.7%. We expect that the significantly increased accuracy of ADME prediction seen with Chemi-Net over Cubist will greatly accelerate drug discovery.