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

Uniformity of Peptide Release Is Maintained by Methylation of Release Factors

Termination of protein synthesis on the ribosome is catalyzed by release factors (RFs), which share a conserved glycine-glycine-glutamine (GGQ) motif. The glutamine residue is methylated in vivo, but a mechanistic understanding of its contribution to hydrolysis is lacking. Here, we show that the modification, apart from increasing the overall rate of termination on all dipeptides, substantially increases the rate of peptide release on a subset of amino acids. In the presence of unmethylated RFs, we measure rates of hydrolysis that are exceptionally slow on proline and glycine residues and approximately two orders of magnitude faster in the presence of the methylated factors. Structures of 70S ribosomes bound to methylated RF1 and RF2 reveal that the glutamine side-chain methylation packs against 23S rRNA nucleotide 2451, stabilizing the GGQ motif and placing the side-chain amide of the glutamine toward tRNA. These data provide a framework for understanding how release factor modifications impact termination