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

A global profile of germline gene expression in C. elegans

As a first step in determining the gene expression patterns that underlie the development of C. elegans, we have used DNA microarrays to identify genes whose expression is increased in the germline. The germline differs from somatic tissue in several ways. The germline is totipotent because it generates every tissue in an individual of the next generation. It is also immortal, since it perpetuates itself indefinitely by producing subsequent generations. In contrast, every cell in the soma differentiates into a particular type and survives only as long as the individual. The complete molecular depiction of germline gene expression generates an overview of all the biological processes in that tissue. In addition, th