Transforming the Fairytale: A Diachronic Study of Utopias of Popular Romance

Popular romance novels have been examined by a number of critics over the past several decades, but each of these studies has analyzed texts within a fixed, synchronic context. Such analyses, while useful, fail to provide the same depth and breadth of a study of a popular culture genre that combines both synchronic and diachronic approaches. This study evaluates the popular romance novels produced during three distinct historical moments: the early mass-market romance novel, popular during the 1960s and 70s; the contemporary erotic romance novel, produced from the 1980s until currently; and the “chick-lit” sub-genre of popular romance, currently rising in popularity. Examining these three snapshots of the popular romance novel and the ways in which the genre has changed over time generates new theoretical paradigms based on the potential of these novels to perform as transformative texts, either culturally and/or economically. Further, a comparison of the structures within the popular romance to those of fairytale allows us to see how the former performs within our culture in ways similar to the latter, which further illustrates the potential of the popular romance novel to perform as a transformative text within our society. Thus, the utopias produced in popular romance are different for each historical moment, as changing social and economic conditions are not only reflected within these texts, but are perhaps even generated as they provide readers with increasingly nontraditional ways of viewing gender performance and heterosexual relationships within the traditional dichotomy ot heterosexual marriage

AI is a viable alternative to high throughput screening: a 318-target study

: High throughput screening (HTS) is routinely used to identify bioactive small molecules. This requires physical compounds, which limits coverage of accessible chemical space. Computational approaches combined with vast on-demand chemical libraries can access far greater chemical space, provided that the predictive accuracy is sufficient to identify useful molecules. Through the largest and most diverse virtual HTS campaign reported to date, comprising 318 individual projects, we demonstrate that our AtomNet® convolutional neural network successfully finds novel hits across every major therapeutic area and protein class. We address historical limitations of computational screening by demonstrating success for target proteins without known binders, high-quality X-ray crystal structures, or manual cherry-picking of compounds. We show that the molecules selected by the AtomNet® model are novel drug-like scaffolds rather than minor modifications to known bioactive compounds. Our empirical results suggest that computational methods can substantially replace HTS as the first step of small-molecule drug discovery