Ranked binding energies of residues and data fusion to identify the active and selective pyrimidine-based Janus kinases 3 (JAK3) inhibitors

ABSTRACT

The idea of using ranked binding energies of residues and data fusion are presented here for the first time as a valuable tool to classify active and selective inhibitors. Selective inhibitors of JAK3 can inhibit inflammatory cytokine while preventing targeting other subtypes of JAK1 and JAK2. Herein, we report a novel way to identify both active JAK3 and selective JAK1/JAK3 and JAK2/JAK3 inhibitors using the effective activity and selectivity classifications. The most important residues (top 10) responsible for the inhibition mechanism are sorted from high to low energies, which are considered as variables in the classification process. In addition, the ranked energies of ligands’ heteroatoms (top 5), ranked energies of hydrogen bonds (top 5) and important molecular descriptors (top 10) were used to construct different data fusion possibilities. It is shown that the proposed data fusion strategy can increase the accuracy of the activity classification to 100% and the selectivity classification to 96.4%. The proposed strategies represented in this paper can help medicinal or pharmaceutical chemist in evaluation of both active and selective inhibitors before synthesizing new pharmaceuticals.

KEYWORDS:

• Ranked binding energies
• data fusion
• classification
• CPANN
• selectivity index

Disclosure statement

No potential conflict of interest was reported by the author(s).

Supplementary material

Supplemental data for this article can be accessed at: https://doi.org/10.1080/1062936X.2021.2013318.

Additional information

Funding

The author(s) reported that there is no funding associated with the work featured in this article.