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Janus kinase 1 (JAK1) is a tyrosine kinase that is involved in the initiation of responses to a number of different cytokine receptor families. The JAK1-dependent pathway is a therapeutic target, and several JAK inhibitors have been developed thanks to intensive research. However, since the ATP binding sites of JAK family members are quite alike, JAK1 inhibitors can thus be less selective, resulting in unanticipated adverse effects. Despite this, minor variations in the ATP-binding site have been extensively used to find a variety of small compounds with different inhibitory properties. Stronger binding affinity of JAK1 inhibitors is believed to be able to reduce the negative effects, leading to better treatment results. Therefore, a thorough computational search that can effectively identify ligands with extremely high binding affinity for JAK1 to serve as promising inhibitors is required. Here, a method combining steered-molecular dynamic (SMD) simulations with a modified linear interaction energy (LIE) model has been developed to evaluate the binding affinities of known JAK1 inhibitors. The correlation coefficient between the estimated and experimental values was 0.72 and a root-mean-square error was 0.97 kcal•mol −1 , revealing that the SMD/LIE method can precisely and quickly predict the binding free energies of JAK1 inhibitors. Furthermore, three marine fungus-derived compounds, namely  hansforesters  E,  hansforesters  G and  tetroazolemycins  B, were identified to be particularly promising JAK1 inhibitors, accordingly. These findings show that the SMD/LIE method has a lot of promise for  in silico  screening of possible JAK1 inhibitors from a vast number of compounds that are now accessible. Communicated by Ramaswamy H. Sarma

Calculation of binding affinity of JAK1 inhibitors via accurately computational estimation