Objective: This study aims to predict a bioactive compound from Peronema canescens (PC) with mechanisms inhibitor interleukin 6 (IL-6) and tumor necrosis factor-alpha (TNF-α) potential as an immunomodulatory using in silico approach. Methods: Autodock 4 was used to accomplish computer-assisted drug design with molecular docking simulation to discover binding energy, inhibition constant, and interactions with an amino acid in bioactive compounds from PC against IL-6 and TNF-α receptors. Lipinski predicts the drug-likeness of a bioactive compound for the oral route of administration. ADMET profiling of bioactive compounds to predict pharmacokinetic properties with pkCSM ADMET. Results: The results showed that the best binding energy, inhibition constant, and interactions with an amino acid of peronemin C1 against IL-6 and TNF-α receptors were (-7.19 kcal/mol; 5.39 nM; Arg 179, Arg 182, Gln 175), and (-8.86 kcal/mol; 320.42 nM; Tyr 119, Tyr 59, and Gly 121), respectively. All bioactive compounds from PC met Lipinski's rule of five requirements for oral administration. ADMET prediction results all bioactive compounds from PC are non-mutagenic, except peronemin D1 is mutagenic. Conclusion: The peronemin C1 bioactive compounds from PC have good immunomodulatory potential, effectively inhibiting human IL-6 and TNF-α receptors using in silico approach.

Objective: This study aims to find antimalarial candidates from 32 terpenoids and three flavonoid compounds found in miana leaves in silico using plasmepsin protein as a receptor through docking simulations, molecular dynamics simulations, and pharmacokinetic predictions. Methods: The research was conducted in silico through molecular docking simulation, molecular dynamic simulations, analysis of potential compounds using Lipinski’s rule, and prediction of ADMET based on ligands. Results: The results showed isophytol had the best interaction with the plasmepsin II based on the low free binding energy (FBE) and led to hydrogen bonding with the plasmepsin II crucial amino acid, Asp34. Isophytol has the best result in molecular dynamic simulation. Based on pharmacokinetics predictions, toxicity, and Lipinski’s rule of five, most tested compounds, including isophytol, meet the criteria as a promising drug. Conclusion: Isophytol from miana leaves with plasmepsin II protein has the best and most stable interaction based on the results of molecular dynamic simulation, so this compound was a candidate for antimalarial drugs.