Publications associées à Structure and Reactivity of N-Heterocyclic Alkynyl Hypervalent Iodine Reagents

Thermal transport of glasses via machine learning driven simulations

Accessing the thermal transport properties of glasses is a major issue for the design of production strategies of glass industry, as well as for the plethora of applications and devices where glasses are employed. From the computational standpoint, the che ...

Frontiers Media Sa2024

Methods for high-throughput synthesis and screening of peptide libraries

Zsolt Bognár

Macrocycles provide an attractive modality for drug development but the identification of ligands to targets of interest is hindered by the lack of large macrocyclic compound libraries for high-throughput screening. A strategy to efficiently synthesize lar ...

EPFL2024

Oxidative Defect Detection Within Free and Packed DNA Systems: A Quantum Mechanical/Molecular Mechanics (QM/ MM) Approach

Ursula Röthlisberger

Base excision repair enzymes (BERs) detect and repair oxidative DNA damage with efficacy despite the small size of the defects and their often only minor structural impact. A charge transfer (CT) model for rapid scanning of DNA stretches has been evoked to ...

Swiss Chemical Soc2024

New Applications of 1,3,2-Diazaphospholenes in Catalysis

Johannes Klett

The constant urge to construct new molecules in an economical and sustainable fashion led to the development of numerous metal-catalyzed transformations. Organocatalysts consisting of abundant and more sustainable elements offer an elegant solution to over ...

EPFL2024

Seebeck Coefficient of Ionic Conductors from Bayesian Regression Analysis

We propose a novel approach to evaluating the ionic Seebeck coefficient in electrolytes from relatively short equilibrium molecular dynamics simulations, based on the Green-Kubo theory of linear response and Bayesian regression analysis. By exploiting the ...

Amer Chemical Soc2024

Encoding quantum-chemical knowledge into machine-learning models of complex molecular properties

Ksenia Briling

Statistical (machine-learning, ML) models are more and more often used in computational chemistry as a substitute to more expensive ab initio and parametrizable methods. While the ML algorithms are capable of learning physical laws implicitly from data, ad ...

EPFL2024

Hypervalent Iodine: New Reagents and Functionalization of Peptides

Eliott Hugo Joran Le Du

Alkynes are found in a multitude of natural or synthetic bioactive compounds. In addition to the capacity of these chemical motifs to impact the physicochemical properties of a molecule of interest, the well-established reactivity of alkynes makes them ide ...

EPFL2023

Predicting Ion Diffusion from the Shape of Potential Energy Landscapes

Berend Smit, Senja Dominique Barthel, Amber Kashan Mace

We present an efficient method to compute diffusion coefficients of multiparticle systems with strong interactions directly from the geometry and topology of the potential energy field of the migrating particles. The approach is tested on Li-ion diffusion ...

Amer Chemical Soc2023

Development of methods for the synthesis of large combinatorial libraries of macrocyclic compounds

Mischa Schüttel

Macrocycles have raised much interest in the pharmaceutical industry due to their ability to bind challenging targets while often still being able to cross membranes to reach intracellular proteins. However, the development of macrocyclic ligands to new di ...

EPFL2023

Atomic-level structure determination of amorphous molecular solids by NMR

David Lyndon Emsley, Arthur César Pinon, Pinelopi Moutzouri, Manuel Cordova

Structure determination of amorphous materials remains challenging, owing to the disorder inherent to these materials. Nuclear magnetic resonance (NMR) powder crystallography is a powerful method to determine the structure of molecular solids, but disorder ...

Nature Portfolio2023