Crowdsourced mapping of unexplored target space of kinase inhibitors

Despite decades of intensive search for compounds that modulate the activity of particular protein targets, a large proportion of the human kinome remains as yet undrugged. Effective approaches are therefore required to map the massive space of unexplored compound-kinase interactions for novel and potent activities. Here, we carry out a crowdsourced benchmarking of predictive algorithms for kinase inhibitor potencies across multiple kinase families tested on unpublished bioactivity data. We find the top-performing predictions are based on various models, including kernel learning, gradient boosting and deep learning, and their ensemble leads to a predictive accuracy exceeding that of single-dose kinase activity assays. We design experiments based on the model predictions and identify unexpected activities even for under-studied kinases, thereby accelerating experimental mapping efforts. The open-source prediction algorithms together with the bioactivities between 95 compounds and 295 kinases provide a resource for benchmarking prediction algorithms and for extending the druggable kinome. The IDG-DREAM Challenge carried out crowdsourced benchmarking of predictive algorithms for kinase inhibitor activities on unpublished data. This study provides a resource to compare emerging algorithms and prioritize new kinase activities to accelerate drug discovery and repurposing efforts

On variable Wiener index

1279-1282A variable Wiener index vW, applicable only to acyclic structures, is defined as Σ [n1(e) n2(e)]λ. For the optimal exponent, that value of λ should be selected which gives the smallest standard error of estimate, s, in the structure-property modeling. As a test case, the structure-boiling point modeling of isomeric octanes is used. It is found that the optimal exponent is λ=1/3 which gives a two-parameter model with the lowest value of s (0.72°C) of all tested models. Close to this model is a two-parameter model based on the variable Wiener index with exponent λ=-1/3 (s=0.83°C). Among the published models, only four- and five-parameter models, based on the overall connectivity indices, produce lower values of s (0.71 °C, 0.55°C)

Interpretation of the IR and Raman spectra of morin by density functional theory and comparative analysis

Density functional theory calculations, with M05-2X functional and 6-311++G(d,p) basis set implemented in the Gaussian 09 package, are performed with the aim to support molecular structure and spectroscopic characteristics of morin, a bioflavonoid molecule known for its antiproliferative, antitumor, and anti-inflammatory effects. Detailed vibrational spectral analysis and the assignments of the bands, done on the best-fit basis comparison of the experimentally obtained and theoretically calculated IR and Raman spectra, match quite well indicating DFT calculations as very accurate source of normal mode assignments. The assignment of the most prominent normal modes of morin is qualitatively verified through comparative spectral analysis with quercetin, a structurally isomeric molecule of morin which differs only by the substitution pattern of the B ring. Performed comparative analysis reflects quite accurately all the structural differences between the investigated molecules additionally proving the applied theoretical method