More Like this

1. Comparison of Ce( iv )/Th( iv )-alkynyl complexes and observation of a trans -influence ligand series for Ce( iv )

   https://doi.org/10.1039/D5SC03222A  

   Yang, Qiaomu; Yu, Xiaojuan; Lapsheva, Ekaterina; Pandey, Pragati; Smith, Patrick W; Gupta, Himanshu; Gau, Michael R; Carroll, Patrick J; Minasian, Stefan G; Autschbach, Jochen; et al (August 2025, Chemical Science)

   Organometallic cerium(iv) and thorium(iv) alkynyl complexes were synthesized, and compared experimentally and theoretically. A cerium(iv)trans-influence ligand series was observed and analyzed. 

   more » « less
2. Mononuclear Fe( iii ) Schiff base antipyrine complexes for catalytic hydrogen generation

   https://doi.org/10.1039/D4DT01876A  

   Cropley, Jessica D; Mitchell, Amanda C; Fritsch, Nicole A; Ho, Marissa; Wells, Timothy D; Reynolds, Todd M; Brennessel, William W; McNamara, William R (September 2024, Dalton Transactions)

   Mononuclear Fe(iii) complexes containing an antipyrine Schiff base ligand were prepared and fully characterized, demonstrating a planar tetradentate coordination geometry. The resulting complexes are active for HER with possible ligand cooperativity. 

   more » « less
3. An Fe( ii ) complex detects hydrogen peroxide with ¹ H and ¹⁹ F magnetic resonance imaging responses

   https://doi.org/10.1039/D5CC04066C  

   Karbalaei, Sana; Franke, Alicja; Zahl, Achim; Pokkuluri, P Raj; Beyers, Ronald J; Ivanović-Burmazović, Ivana; Goldsmith, Christian R (October 2025, Chemical Communications)

   A highly air- and water-stable Fe(ii) complex with a fluorinated ligand has a strong¹⁹F MRI signal but is a poorT₁-weighted¹H MRI contrast agent. Upon oxidation by H₂O₂, the¹⁹F MRI signal decays as the relaxivity for¹H MRI markedly improves. 

   more » « less
4. Protein‐Ligand Structure and Affinity Prediction in CASP16 Using a Geometric Deep Learning Ensemble and Flow Matching

   https://doi.org/10.1002/prot.26827  

   Morehead, Alex; Liu, Jian; Neupane, Pawan; Giri, Nabin; Cheng, Jianlin (April 2025, Proteins: Structure, Function, and Bioinformatics)

   ABSTRACT Predicting the structure of ligands bound to proteins is a foundational problem in modern biotechnology and drug discovery, yet little is known about how to combine the predictions of protein‐ligand structure (poses) produced by the latest deep learning methods to identify the best poses and how to accurately estimate the binding affinity between a protein target and a list of ligand candidates. Further, a blind benchmarking and assessment of protein‐ligand structure and binding affinity prediction is necessary to ensure it generalizes well to new settings. Towards this end, we introduceMULTICOM_ligand, a deep learning‐based protein‐ligand structure and binding affinity prediction ensemble featuring structural consensus ranking for unsupervised pose ranking and a new deep generative flow matching model for joint structure and binding affinity prediction. Notably,MULTICOM_ligand ranked among the top‐5 ligand prediction methods in both protein‐ligand structure prediction and binding affinity prediction in the 16th Critical Assessment of Techniques for Structure Prediction (CASP16), demonstrating its efficacy and utility for real‐world drug discovery efforts. The source code for MULTICOM_ligand is freely available on GitHub. 

   more » « less
5. Toward Robustness across Cosmological Simulation Models I llustris TNG, SIMBA, A strid , and S wift -E agle

   https://doi.org/10.3847/1538-4357/adec78  

   Jo, Yongseok; Genel, Shy; Sengupta, Anirvan; Wandelt, Benjamin; Somerville, Rachel; Villaescusa-Navarro, Francisco (September 2025, The Astrophysical Journal)

   Abstract The rapid advancement of large-scale cosmological simulations has opened new avenues for cosmological and astrophysical research. However, the increasing diversity among cosmological simulation models presents a challenge to therobustness. In this work, we develop the Model-Insensitive ESTimator (Miest), a machine that canrobustlyestimate the cosmological parameters, Ω_mandσ₈, from neural hydrogen maps of simulation models in the Cosmology and Astrophysics with MachinE Learning Simulations project—IllustrisTNG,SIMBA, Astrid, and SWIFT-Eagle. An estimator is consideredrobustif it possesses a consistent predictive power across all simulations, including those used during the training phase. We train our machine using multiple simulation models and ensure that it only extracts common features between the models while disregarding the model-specific features. This allows us to develop a novel model that is capable of accurately estimating parameters across a range of simulation models, without being biased toward any particular model. Upon the investigation of the latent space—a set of summary statistics, we find that the implementation ofrobustnessleads to the blending of latent variables across different models, demonstrating the removal of model-specific features. In comparison to a standard machine lackingrobustness, the average performance of Mieston the unseen simulations during the training phase has been improved by ∼17% for Ω_mand 38% forσ₈. By using a machine learning approach that can extractrobust, yet physical features, we hope to improve our understanding of galaxy formation and evolution in a (subgrid) model-insensitive manner, and ultimately, gain insight into the underlying physical processes responsible forrobustness. 

   more » « less