High Connectivity in the Deepwater Snapper Pristipomoides filamentosus (Lutjanidae) across the Indo-Pacific with Isolation of the Hawaiian Archipelago

In the tropical Indo-Pacific, most phylogeographic studies have focused on the shallow-water taxa that inhabit reefs to approximately 30 m depth. Little is known about the large predatory fishes, primarily snappers (subfamily Etelinae) and groupers (subfamily Epinephelinae) that occur at 100–400 m. These long-lived, slow-growing species support fisheries across the Indo-Pacific, yet no comprehensive genetic surveys within this group have been conducted. Here we contribute the first range-wide survey of a deepwater Indo-Pacific snapper, Pristipomoides filamentosus, with special focus on Hawai'i. We applied mtDNA cytochrome b and 11 microsatellite loci to 26 samples (N = 1,222) collected across 17,000 km from Hawai'i to the western Indian Ocean. Results indicate that P. filamentosus is a highly dispersive species with low but significant population structure (mtDNA ΦST = 0.029, microsatellite FST = 0.029) due entirely to the isolation of Hawai'i. No population structure was detected across 14,000 km of the Indo-Pacific from Tonga in the Central Pacific to the Seychelles in the western Indian Ocean, a pattern rarely observed in reef species. Despite a long pelagic phase (60–180 days), interisland dispersal as adults, and extensive gene flow across the Indo-Pacific, P. filamentosus is unable to maintain population connectivity with Hawai'i. Coalescent analyses indicate that P. filamentosus may have colonized Hawai'i 26 K–52 K y ago against prevailing currents, with dispersal away from Hawai'i dominating migration estimates. P. filamentosus harbors low genetic diversity in Hawai'i, a common pattern in marine fishes, and our data indicate a single archipelago-wide stock. However, like the Hawaiian Grouper, Hyporthodus quernus, this snapper had several significant pairwise comparisons (FST) clustered around the middle of the archipelago (St. Rogatien, Brooks Banks, Gardner) indicating that this region may be isolated or (more likely) receives input from Johnston Atoll to the south

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