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.

After the initial wave of antibiotic discovery, few novel classes of antibiotics have emerged, with the latest dating back to the 1980’s. Furthermore, the pace of antibiotic drug discovery is unabl...

Introduction: Drug design and discovery of new antivirals will always be extremely important in medicinal chemistry, taking into account known and new viral diseases that are yet to come. Although machine learning (ML) have shown to improve predictions on the biological potential of chemicals and accelerate the discovery of drugs over the past decade, new methods and their combinations have improved their performance and established promising perspectives regarding ML in the search for new antivirals. Areas covered: The authors consider some interesting areas that deal with different ML techniques applied to antivirals. Recent innovative studies on ML and antivirals were selected and analyzed in detail. Also, the authors provide a brief look at the past to the present to detect advances and bottlenecks in the area. Expert opinion: From classical ML techniques, it was possible to boost the searches for antivirals. However, from the emergence of new algorithms and the improvement in old approaches, promising results will be achieved every day, as we have observed in the case of SARS-CoV-2. Recent experience has shown that it is possible to use ML to discover new antiviral candidates from virtual screening and drug repurposing.

Dengue is a tropical disease caused by the dengue virus (DENV), with an estimate of 300 million new cases every year. Due to the limited vaccine efficiency and absence of effective antiviral treatment, new drug candidates are urgently needed. DENV NS3‐NS2B protease complex is essential for viral post‐translational processing and maturation, and this enzyme has been extensively studied as a relevant drug target. Crystal structures often underestimate NS3‐NS2B flexibility, whereas they can adopt different conformational states depending on the bound substrate. We conducted molecular dynamics simulations (∼30 μs) with a non‐ and covalently bound inhibitor to understand the conformational changes in the DENV‐3 NS3‐NS2B complex. Our results show that the open‐closing movement of the protease exposes multiple druggable subpockets that can be investigated in later drug discovery efforts.

Synthetic 1,3‐bis(aryloxy)propan‐2‐amines have been shown in previous studies to possess several biological activities, such as antifungal and antiprotozoal. In the present study, we describe the antibacterial activity of new synthetic 1,3‐bis(aryloxy)propan‐2‐amines against Gram‐positive pathogens (Streptococcus pyogenes, Enterococcus faecalis and Staphylococcus aureus) including Methicillin–resistant S. aureus strains. Our compounds showed minimal inhibitory concentrations (MIC) in the range of 2.5–10 μg/ml (5.99–28.58 μM), against different bacterial strains. The minimal bactericidal concentrations found were similar to MIC, suggesting a bactericidal mechanism of action of these compounds. Furthermore, possible molecular targets were suggested by chemical similarity search followed by docking approaches. Our compounds are similar to known ligands targeting the cell division protein FtsZ, Quinolone resistance protein norA and the Enoyl‐[acyl‐carrier‐protein] reductase FabI. Taken together, our data show that synthetic 1,3‐bis(aryloxy)propan‐2‐amines are active against Gram‐positive bacteria, including multidrug–resistant strains and can be a promising lead in the development of new antibacterial compounds for the treatment of these infections.