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Methods for computational de novo design of inorganic molecules have paved the way for automated design of homogeneous catalysts. Such studies have so far relied on correlation-based prediction models as fitness functions (figures of merit), but the soundness of these approaches has yet to be tested by experimental verification of de novo-designed catalysts. Here, a previously developed criterion for the optimization of dative ligands L in ruthenium-based olefin metathesis catalysts RuCl(L)(L')(═CHAr), where Ar is an aryl group and L' is a phosphine ligand dissociating to activate the catalyst, was used in de novo design experiments. These experiments predicted catalysts bearing an N-heterocyclic carbene (L = ) substituted by two N-bound mesityls and two -butyl groups at the imidazolidin-2-ylidene backbone to be promising. Whereas the phosphine-stabilized precursor assumed by the prediction model could not be made, a pyridine-stabilized ruthenium alkylidene complex () bearing carbene was less active than a known leading pyridine-stabilized Grubbs-type catalyst (, L = HIMes). A density functional theory-based analysis showed that the unsubstituted metallacyclobutane (MCB) intermediate generated in the presence of ethylene is the likely resting state of both and . Whereas the design criterion via its correlation between the stability of the MCB and the rate-determining barrier indeed seeks to stabilize the MCB, it relies on RuCl(L)(L')(═CH) adducts as resting states. The change in resting state explains the discrepancy between the prediction and the actual performance of catalyst . To avoid such discrepancies and better address the multifaceted challenges of predicting catalytic performance, future de novo catalyst design studies should explore and test design criteria incorporating information from more than a single relative energy or intermediate.
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http://dx.doi.org/10.1021/acs.jcim.3c01649 | DOI Listing |
BMJ
September 2025
Department of Circulation and Medical Imaging, Norwegian University of Science and Technology, Trondheim, Norway.
Objective: To determine the effect of a prepregnancy lifestyle intervention on glucose tolerance in people at higher risk of gestational diabetes mellitus.
Design: Single centre randomised controlled trial (BEFORE THE BEGINNING).
Setting: University hospital in Trondheim, Norway.
Bioorg Chem
September 2025
School of Cosmetic Science, Mae Fah Luang University, Chiang Rai 57100, Thailand. Electronic address:
Although antimicrobial peptides possess potent antimicrobial activities, the high cost of production, based on amino acid length, has limited their therapeutic and cosmeceutical applications. This study aimed to produce and characterize de novo designed antimicrobial peptides derived from WSKK11 and WSRR11 for efficacy against acne-causing bacteria. Ten designed peptides were evaluated for antimicrobial, hemolytic, and cytotoxic activities, as well as, secondary structures by FTIR and modes of action.
View Article and Find Full Text PDFDiabetologia
September 2025
Walther Straub Institute of Pharmacology and Toxicology, LMU Munich, Munich, Germany.
Aims/hypothesis: Unimolecular peptides targeting the receptors for glucagon-like peptide-1 (GLP-1), glucose-dependent insulinotropic polypeptide (GIP) and glucagon (GCG) have been shown to improve glycaemic management in both mice and humans. Yet the identity of the downstream signalling events mediated by these peptides remain to be elucidated. Here, we aimed to assess the mechanisms by which a validated peptide triagonist for GLP-1/GIP/GCG receptors (IUB447) stimulates insulin secretion in murine pancreatic islets.
View Article and Find Full Text PDFDiabetologia
September 2025
Centre Universitaire de Diabétologie et de ses Complications, AP-HP, Hôpital Lariboisière, Paris, France.
Aims/hypothesis: Severe hypoglycaemia events (SHE) remain frequent in people with type 1 diabetes despite advanced diabetes technologies. We examined whether time below range (TBR) 3.9 mmol/l (70 mg/dl; TBR70) or 3.
View Article and Find Full Text PDFPest Manag Sci
September 2025
National Pesticide Engineering Research Center, State Key Laboratory of Elemento-Organic Chemistry, College of Chemistry, Nankai University, Tianjin, People's Republic of China.
Background: Rapid advances in generative artificial intelligence (AI) are accelerating the process of pesticide development. However, transfer learning-based de novo design focuses on generating molecules that are highly similar to existing inhibitors, which may limit the exploration of novel scaffolds and thereby constrain innovative breakthroughs in pesticide development.
Results: This study proposes a new strategy for fungicide design using antibiotics.