Drug Discovery Maps, a Machine Learning Model That Visualizes and Predicts Kinome-Inhibitor Interaction Landscapes

The interpretation of high-dimensional structure-activity data sets in drug discovery to predict ligand-protein interaction landscapes is a challenging task. Here we present Drug Discovery Maps (DDM), a machine learning model that maps the activity profile of compounds across an entire protein family, as illustrated here for the kinase family. DDM is based on the t-distributed stochastic neighbor embedding (t-SNE) algorithm to generate a visualization of molecular and biological similarity. DDM maps chemical and target space and predicts the activities of novel kinase inhibitors across the kinome. The model was validated using independent data sets and in a prospective experimental setting, where DDM predicted new inhibitors for FMS-like tyrosine kinase 3 (FLT3), a therapeutic target for the treatment of acute myeloid leukemia. Compounds were resynthesized, yielding highly potent, cellularly active FLT3 inhibitors. Biochemical assays confirmed most of the predicted off-targets. DDM is further unique in that it is completely open-source and available as a ready-to-use executable to facilitate broad and easy adoption

R144 revealed as a double-lined spectroscopic binary

R144 is a WN6h star in the 30 Doradus region. It is suspected to be a binary because of its high luminosity and its strong X-ray flux, but no periodicity could be established so far. Here, we present new Xshooter multi-epoch spectroscopy of R144 obtained at the ESO Very Large Telescope (VLT). We detect variability in position and/or shape of all the spectral lines. We measure radial velocity variations with an amplitude larger than 250 km/s in NIV and NV lines. Furthermore, the NIII and NV line Doppler shifts are anti-correlated and the NIV lines show a double-peaked profile on six of our seven epochs. We thus conclude that R144 is a double-lined spectroscopic binary. Possible orbital periods range from 2 to 6 months, although a period up to one year is allowed if the orbit is highly eccentric. We estimate the spectral types of the components to be WN5-6h and WN6-7h, respectively. The high luminosity of the system (log Lbol/Lsun ~ 6.8) suggests a present-day total mass content in the range of about 200 to 300 Msun, depending on the evolutionary stage of the components. This makes R144 the most massive binary identified so far, with a total mass content at birth possibly as large as 400 Msun. We briefly discuss the presence of such a massive object 60 pc away from the R136 cluster core in the context of star formation and stellar dynamics.Comment: Accepted for publication in MNRAS Letters, 5 pages, 3 figure

Geographical variation in \u3ci\u3ePlasmodium vivax\u3c/i\u3e relapse

Background: Plasmodium vivax has the widest geographic distribution of the human malaria parasites and nearly 2.5 billion people live at risk of infection. The control of P. vivax in individuals and populations is complicated by its ability to relapse weeks to months after initial infection. Strains of P. vivax from different geographical areas are thought to exhibit varied relapse timings. In tropical regions strains relapse quickly (three to six weeks), whereas those in temperate regions do so more slowly (six to twelve months), but no comprehensive assessment of evidence has been conducted. Here observed patterns of relapse periodicity are used to generate predictions of relapse incidence within geographic regions representative of varying parasite transmission. Methods: A global review of reports of P. vivax relapse in patients not treated with a radical cure was conducted. Records of time to first P. vivax relapse were positioned by geographic origin relative to expert opinion regions of relapse behaviour and epidemiological zones. Mixed-effects meta-analysis was conducted to determine which geographic classification best described the data, such that a description of the pattern of relapse periodicity within each region could be described. Model outputs of incidence and mean time to relapse were mapped to illustrate the global variation in relapse. Results: Differences in relapse periodicity were best described by a historical geographic classification system used to describe malaria transmission zones based on areas sharing zoological and ecological features. Maps of incidence and time to relapse showed high relapse frequency to be predominant in tropical regions and prolonged relapse in temperate areas. Conclusions: The results indicate that relapse periodicity varies systematically by geographic region and are categorized by nine global regions characterized by similar malaria transmission dynamics. This indicates that relapse may be an adaptation evolved to exploit seasonal changes in vector survival and therefore optimize transmission. Geographic patterns in P. vivax relapse are important to clinicians treating individual infections, epidemiologists trying to infer P. vivax burden, and public health officials trying to control and eliminate the disease in human populations