Whistleblowers as regulatory intermediaries: Instrumental and reflexive considerations in decentralizing regulation

This article frames whistleblowers as regulatory intermediaries who provide a response to the problem posed by the fragmentation of knowledge in a complex society and market economy. I identify two ways in which whistleblowers become regulatory intermediaries: The first is by remedying informational asymmetries between the regulator and the target (instrumental approach). Both in the United States and in the European Union, whistleblowers are protected on the basis of the value of the disclosed information for the advancement of regulatory objectives. The second way in which whistleblowers become regulatory intermediaries is by contributing to the development of “communities of compliance” and by enhancing the internal self-regulatory capacities of regulatory targets (reflexive approach). Creating internal channels of reporting and monitoring is perceived as a way to change the organizational culture of targets. Through the instrumentalism – reflexivity dipole, competing rationales and normative visions of regulatory intermediation become apparent: It could, on the one hand, facilitate state intervention and legal sanctions or, on the other hand, signal the aspiration to embed public and social values in private actors

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