Carbon dioxide (CO2) is a key chemical species that is found in a wide range of planetary atmospheres. In the context of exoplanets, CO2 is an indicator of the metal enrichment (that is, elements heavier than helium, also called ‘metallicity’)1–3, and thus the formation processes of the primary atmospheres of hot gas giants4–6. It is also one of the most promising species to detect in the secondary atmospheres of terrestrial exoplanets7–9. Previous photometric measurements of transiting planets with the Spitzer Space Telescope have given hints of the presence of CO2, but have not yielded definitive detections owing to the lack of unambiguous spectroscopic identification10–12. Here we present the detection of CO2 in the atmosphere of the gas giant exoplanet WASP-39b from transmission spectroscopy observations obtained with JWST as part of the Early Release Science programme13,14. The data used in this study span 3.0–5.5 micrometres in wavelength and show a prominent CO2 absorption feature at 4.3 micrometres (26-sigma significance). The overall spectrum is well matched by one-dimensional, ten-times solar metallicity models that assume radiative–convective–thermochemical equilibrium and have moderate cloud opacity. These models predict that the atmosphere should have water, carbon monoxide and hydrogen sulfide in addition to CO2, but little methane. Furthermore, we also tentatively detect a small absorption feature near 4.0 micrometres that is not reproduced by these models.

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A multitargeted therapeutic approach with hybrid drugs is a promising strategy to enhance anticancer efficiency and overcome drug resistance in nonsmall cell lung cancer (NSCLC) treatment. Estimating affinities of small molecules against targets of interest typically proceeds as a preliminary action for recent drug discovery in the pharmaceutical industry. In this investigation, we employed machine learning models to provide a computationally affordable means for computer-aided screening to accelerate the discovery of potential drug compounds. In particular, we introduced a quantitative structure–activity-relationship (QSAR)-based multitask learning model to facilitate an in silico screening system of multitargeted drug development. Our method combines a recently developed graph-based neural network architecture, principal neighborhood aggregation (PNA), with a descriptor-based deep neural network supporting synergistic utilization of molecular graph and fingerprint features. The model was generated by more than ten-thousands affinity-reported ligands of seven crucial receptor tyrosine kinases in NSCLC from two public data sources. As a result, our multitask model demonstrated better performance than all other benchmark models, as well as achieving satisfying predictive ability regarding applicable QSAR criteria for most tasks within the model’s applicability. Since our model could potentially be a screening tool for practical use, we have provided a model implementation platform with a tutorial that is freely accessible hence, advising the first move in a long journey of cancer drug development.

We present the discovery of KELT J072709+072007 (HD 58730), a very low mass ratio (q ≡ M2/M1 ≈ 0.07) eclipsing binary (EB) identified by the Kilodegree Extremely Little Telescope (KELT) survey. We present the discovery light curve and perform a global analysis of four high-precision ground-based light curves, the Transiting Exoplanets Survey Satellite (TESS) light curve, radial velocity (RV) measurements, Doppler Tomography (DT) measurements, and the broad-band spectral energy distribution (SED). Results from the global analysis are consistent with a fully convective (M2 = 0.22 ± 0.02M⊙) M star transiting a late-B primary ($M_1 = 3.34^{+0.07}_{-0.09} {M_\odot }; T_{\rm eff,1} = 11960^{+430}_{-520}\ {\rm K}$). We infer that the primary star is $183_{-30}^{+33}$ Myr old and that the companion star’s radius is inflated by 26 ± 8% relative to the predicted value from a low-mass isochrone of similar age. We separately and analytically fit for the variability in the out-of-eclipse TESS phase curve, finding good agreement between the resulting stellar parameters and those from the global fit. Such systems are valuable for testing theories of binary star formation and understanding how the environment of a star in a close-but-detached binary affects its physical properties. In particular, we examine how a star’s properties in such a binary might differ from the properties it would have in isolation.

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