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Due to the excessive use of paracetamol (PCM), a significant amount of its metabolite has been released into the surroundings, and its removal from the surroundings must happen quickly and sustainably. Multicomponent adsorption modelling is difficult because it is challenging to anticipate the relationships among the adsorbates in this artificial intelligence-based modelling, a choice among different algorithms. Utilizing various algorithms, many studies assessed the single and binary adsorption of paracetamol on activated carbon. The present study implements that the effectiveness of PCM adsorption on a carbon-activated nanomaterial was predicted using an artificial neural network, a machine learning technology. As a factor of adsorbent particle size, adsorbent dosage, training time, and starting concentrations, the adsorption capacity for each medicinal ingredient was examined. SEM was used to analyze a nanomaterial that had been chemically altered with orthophosphoric acid (FTIR). To determine the residual proportion of PCM in solvent, batch adsorption of PCM was then carried out at various operation conditions, including contact time, temperatures, and initial dosage. The adsorption effectiveness of paracetamol on carbon-activated nanoparticle was calculated using experimental results. Thus, by using machine learning framework, the adsorption efficiency of paracetamol on a carbon-activated nanomaterial was predicted.

At present, the world is facing a pandemic named as COVID-19, caused by SARS-CoV-2. Traditional Chinese medicine has recommended the use of liquorice (Glycyrrhiza species) in the treatment of infections caused by SARS-CoV-2. Therefore, the present investigation was carried out to identify the active molecule from the liquorice against different protein targets of COVID-19 using an in-silico approach. The molecular docking simulation study of 20 compounds along with two standard antiviral drugs (Lopinavir and Rivabirin) was carried out with the help of Autodock vina software using two protein targets from COVID-19 i.e. spike glycoprotein (PDB ID: 6VSB) and Non-structural Protein-15 (Nsp15) endoribonuclease (PDB ID: 6W01). From the observed binding energy and the binding interactions, glyasperin A showed high affinity towards Nsp15 endoribonuclease with uridine specificity, while glycyrrhizic acid was found to be best suited for the binding pocket of spike glycoprotein and also prohibited the entry of the virus into the host cell. Further, the dynamic behavior of the best-docked molecules inside the spike glycoprotein and Nsp15 endoribonuclease were explored through all-atoms molecular dynamics (MD) simulation study. Several parameters from the MD simulation have substantiated the stability of protein-ligand stability. The binding free energy of both glyasperin A and glycyrrhizic acid was calculated from the entire MD simulation trajectory through the MM-PBSA approach and found to high binding affinity towards the respective protein receptor cavity. Thus, glyasperin A and glycyrrhizic acid could be considered as the best molecule from liquorice, which could find useful against COVID-19. Communicated by Ramaswamy H. Sarma