2 research outputs found
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
Aminopyrimidine Derivatives as Multiflavivirus Antiviral Compounds Identified from a Consensus Virtual Screening Approach
Around three billion people are at risk of infection
by the dengue
virus (DENV) and potentially other flaviviruses. Worldwide outbreaks
of DENV, Zika virus (ZIKV), and yellow fever virus (YFV), the lack
of antiviral drugs, and limitations on vaccine usage emphasize the
need for novel antiviral research. Here, we propose a consensus virtual
screening approach to discover potential protease inhibitors (NS3pro) against different flavivirus. We employed an in
silico combination of a hologram quantitative structure–activity
relationship (HQSAR) model and molecular docking on characterized
binding sites followed by molecular dynamics (MD) simulations, which
filtered a data set of 7.6 million compounds to 2,775 hits. Lastly,
docking and MD simulations selected six final potential NS3pro inhibitors with stable interactions along the simulations. Five
compounds had their antiviral activity confirmed against ZIKV, YFV,
DENV-2, and DENV-3 (ranging from 4.21 ± 0.14 to 37.51 ±
0.8 μM), displaying aggregator characteristics for enzymatic
inhibition against ZIKV NS3pro (ranging from 28 ±
7 to 70 ± 7 μM). Taken together, the compounds identified
in this approach may contribute to the design of promising candidates
to treat different flavivirus infections