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The celebrity profiling is used to predict the sub-profiles like gender, fame, birth-year and occupation of a celebrity for a given textual content. The task of celebrity profiling is introduced in PAN Competition 2019. Most of the researchers in the competition have shown interest on stylistic features to differentiate the writing styles of the celebrities. In this work, a sub-profile based weighted approach is proposed to improve the accuracy of celebrity profiling. In this approach, most frequent terms are used to compute the document weight. The document weights were used to represent the document vectors instead of weights of features. The document vectors forwarded to machine learning algorithms to build the training model. The proposed method achieved competitive accuracies of 77.13% for gender prediction, 87.76% for fame prediction and 91.54% for occupation prediction. The accuracies of the proposed approach for sub-profiles prediction outperform several existing approaches for celebrity profiling.

Poor metabolic stability of the human immunodeficiency virus type-1 (HIV-1) capsid (CA) inhibitor PF-74is a major concern in its development toward clinical use. To improve on the metabolic stability, we employed a novel multistep computationally driven workflow, which facilitated the rapid design of improved PF-74analogs in an efficient manner. Using this workflow, we designed three compounds that interact specifically with the CA interprotomer pocket, inhibit HIV-1 infection, and demonstrate enantiomeric preference. Moreover, using this workflow, we were able to increase the metabolic stability 204-fold in comparison to PF-74in only three analog steps. These results demonstrate our ability to rapidly design CA compounds using a novel computational workflow that has improved metabolic stability over the parental compound. This workflow can be further applied to the redesign of PF-74and other promising inhibitors with a stability shortfall.