ArticleJournal of molecular modeling2022
A computationally affordable approach for accurate prediction of the binding affinity of JAK2 inhibitors.
Article in Journal of molecular modeling, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
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5 authors at 4 institutions in 1 country.
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Abstract
Janus kinase 2 (JAK2) inhibitors are potential anticancer drugs in the treatment of lymphoma, leukemia, thrombocytosis and particularly myeloproliferative diseases. However, the resemblance among JAK family members has challenged the identification of highly selective inhibitors for JAK2 to reduce undesired side effects. As a result, a robust search for promising JAK2 inhibitors using a computational approach that can effectively nominate new potential candidates to be further analyzed through laborious experimental operations has become necessary. In this study, the binding affinities of JAK2 inhibitors were rapidly and precisely estimated using the fast pulling of ligand (FPL) simulations combined with a modified linear interaction energy (LIE) method. The approach correlates with the experimental binding affinities of JAK2 inhibitors with a correlation coefficient of R = 0.82 and a root-mean-square error of 0.67 kcal•mol
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