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Background: Obesity has now become a global issue due to the increase in the population of obese people. It also substantially impacts the individual's social, financial, and psychological well-being, which may contribute to depression. Being overweight induces many metabolic and chronic disorders, urging many researchers to focus on developing the drug for obesity treatment. Pancreatic lipase inhibitors and natural product/compound-derived pancreatic lipase inhibitors have recently received much attention because of their structural variety and low toxicity. Objective: This study aimed to build pharmacophores and QSAR for analyzing the necessary structure of pancreatic lipase inhibitors and designing new molecules with the best activity. Methods: Ligand-based pharmacophore modeling and Atom-Based 3D-QSAR were carried out using the PHASE module of Schrodinger to determine the critical structural properties necessary for pancreatic lipase (PL) inhibitory activity. A total of 157 phytoconstituents and a standard drug, orlistat, were selected for the present study. Considering the important features of pancreatic lipase inhibition, 15 new molecules were designed and subjected to molecular docking studies and molecular dynamics simulations. The activity of designed molecules was predicted using the Atom-Based QSAR tool of the PHASE module. Results: The top docked score molecule is structure-7 with a docking score of -6.094 Kcal/mol, whereas the docking score of orlistat and tristin is -3.80Kcal/mol and -5.63Kcal/mol, respectively. Conclusion: The designed molecules have a high docking score and good stability, are in the desirable ADME range and are derived from natural products, so they might be used as lead molecules for anti-obesity drug development.

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The increasing availability of drug-resistant Plasmodium falciparum infections is putting a strain on the accessibility of potent, safe and cost-effective anti-malarial treatments, necessitating the development of new anti-malarial drug. Malaria deaths were estimated to be around 409000 in 2019. The present study sets to identify novel antimalarial compounds and the virtual screening study reveals that the designed compounds bind more effectively to Plasmodium falciparum chloroquine resistance transporter (PfCRT) and Plasmodium falciparum Multidrug Resistant1 (PfMDR1) than the known inhibitors. Marvin JS was used to design the chemical structure of the molecules and the molecular docking of 75 designed molecules with PfCRT and PfMDR1 was performed to study the interaction between the small molecule and the proteins. The top docked scoring compounds with the respective proteins were subjected to molecular dynamic simulation to study their interaction stability. The ADME/T (absorption, distribution, metabolism and excretion/toxicity) properties of those molecules were also studied and the majority of the properties was found to be within acceptable ranges. After experimental validation to confirm the findings, the screened molecules could be used as potential anti-malarial drugs.

The global spread of Severe Acute Respiratory Syndrome coronavirus 2 (SARS-CoV-2), which causes the disease COVID-19, has increased drastically since the first cases in Wuhan, People's Republic of China, at the end of 2019. There is no single drug that can be used specifically to treat COVID. The crucial stage in the drug development process is screening huge libraries of bioactive molecules against a biological target, usually a receptor or a protein. Virtual Screening (VS) has become a valuable tool in the drug development process as it allows for efficient in silico searches of millions of compounds, resulting in higher yields of possible therapeutic leads, and is cost-effective. The spread of the SARS-CoV-2 virus presents a major threat to world health and has resulted in a global crisis because of the high mortality rate and absence of clinically authorised treatments and vaccines for COVID-19. Finding effective drugs or repurposing available antiviral drugs is a critical need in the fight against COVID-19. VS can be classified as either Structural-Based Virtual Screening or Ligand-Based Virtual Screening. VS techniques have been widely applied in the field of antiviral drug design and have aided in the identification of new compounds as possible anti-viral drugs. Both LBVS and SBVS approaches have proved extremely helpful in identifying several prospective anti-viral drugs with nanomolar range. VS, in contrast to experimental approaches, is quick and cost-effective on the one side but has low prediction accuracy on the other.