Abyssal hydrothermal springs-Cryptic incubators for brooding octopus.

Does warmth from hydrothermal springs play a vital role in the biology and ecology of abyssal animals? Deep off central California, thousands of octopus (Muusoctopus robustus) migrate through cold dark waters to hydrothermal springs near an extinct volcano to mate, nest, and die, forming the largest known aggregation of octopus on Earth. Warmth from the springs plays a key role by raising metabolic rates, speeding embryonic development, and presumably increasing reproductive success; we show that brood times for females are ~1.8 years, far faster than expected for abyssal octopods. Using a high-resolution subsea mapping system, we created landscape-scale maps and image mosaics that reveal 6000 octopus in a 2.5-ha area. Because octopuses die after reproducing, hydrothermal springs indirectly provide a food supplement to the local energy budget. Although localized deep-sea heat sources may be essential to octopuses and other warm-tolerant species, most of these unique and often cryptic habitats remain undiscovered and unexplored.

Biased Diversity for Effective Virtual Screening.

Virtual screening is no longer merely a matter of identifying the subset of compounds from a large collection likely to be active against a particular endpoint. This viewpoint shares some distinctive practices at Novartis, where virtual screening combines multiple computational tools that marry the competing goals of biasing the selection of compounds toward multiple desired properties, while diversifying the selection to sample the available chemistry space, identifying quality compounds that inform drug discovery. Topics include the various considerations needed for a successful virtual screening practice: triaging, compound quality, accuracy and test sets, activity prediction including multitask modeling, virtual profiling, automation, multiproperty bias, diversity and property spaces, and biased-diversity designs.

Profile-QSAR 2.0: Kinase Virtual Screening Accuracy Comparable to Four-Concentration IC50s for Realistically Novel Compounds.

While conventional random forest regression (RFR) virtual screening models appear to have excellent accuracy on random held-out test sets, they prove lacking in actual practice. Analysis of 18 historical virtual screens showed that random test sets are far more similar to their training sets than are the compounds project teams actually order. A new, cluster-based "realistic" training/test set split, which mirrors the chemical novelty of real-life virtual screens, recapitulates the poor predictive power of RFR models in real projects. The original Profile-QSAR (pQSAR) method greatly broadened the domain of applicability over conventional models by using as independent variables a profile of activity predictions from all historical assays in a large protein family. However, the accuracy still fell short of experiment on realistic test sets. The improved "pQSAR 2.0" method replaces probabilities of activity from naïve Bayes categorical models at several thresholds with predicted IC50s from RFR models. Unexpectedly, the high accuracy also requires removing the RFR model for the actual assay of interest from the independent variable profile. With these improvements, pQSAR 2.0 activity predictions are now statistically comparable to medium-throughput four-concentration IC50 measurements even on the realistic test set. Beyond the yes/no activity predictions from a typical high-throughput screen (HTS) or conventional virtual screen, these semiquantitative IC50 predictions allow for predicted potency, ligand efficiency, lipophilic efficiency, and selectivity against antitargets, greatly facilitating hitlist triaging and enabling virtual screening panels such as toxicity panels and overall promiscuity predictions.