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

The stereochemistry of fatty acid hydroxylation by cytochrome P450(BM3)

The stereochemical preference for the cytochrome P450(BM3)-catalysed hydroxylation of tetradecanoic and pentadecanoic acids has been determined via comparison with authentic non-racemic standards utilising enantioselective HPLC. The sub-terminal hydroxylation of these fatty acids by P450(BM3) is highly selective for the formation of the R-alcohols. This is the same enantioselectivity as is seen for hexadecanoic acid oxidation but contrasts with a previous report of S-hydroxylation of pentadecanoic acid by P450(BM3). (c) 2006 Elsevier Ltd. All rights reserved