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Publications associées (32)

Machine learning-aided generative molecular design

Machine learning has provided a means to accelerate early-stage drug discovery by combining molecule generation and filtering steps in a single architecture that leverages the experience and design preferences of medicinal chemists. However, designing mach ...

Nature Portfolio2024

Molecular hypergraph neural networks

Philippe Schwaller, Junwu Chen

Graph neural networks (GNNs) have demonstrated promising performance across various chemistry-related tasks. However, conventional graphs only model the pairwise connectivity in molecules, failing to adequately represent higher order connections, such as m ...

Aip Publishing2024

Toward data-driven materials design: From atoms to pilot plants

Kevin Maik Jablonka

Discovering new materials is essential but challenging, time-consuming, and expensive.In many cases, simulations can be useful for estimating material properties. For many of the most interesting properties, however, simulations are infeasible because of p ...

EPFL2023

Dissecting the photoacidity of spiropyran/merocyanine molecular switches in water.

Cesare Berton

Photochemistry is a discipline that studies the interaction between light and matter with the scope to induce chemical transformations. The first conjugation between light and chemistry can be dated back to the lifespan of Giacomo Luigi Ciamician, who is c ...

EPFL2023

Photochemical Alkynylations with Hypervalent Iodine Reagents

Stephanie Grace Elizabeth Amos

Over the past twenty years, photochemical transformations have gained in importance in organic chemistry. Indeed, the development of photocatalysts has allowed the use of visible light as an energy source for chemical transformations. More specifically, ph ...

EPFL2022

Local Kernel Regression and Neural Network Approaches to the Conformational Landscapes of Oligopeptides

Michele Ceriotti, Alberto Fabrizio, Benjamin André René Meyer, Edgar Albert Engel, Raimon Fabregat I De Aguilar-Amat, Veronika Juraskova

The application of machine learning to theoretical chemistry has made it possible to combine the accuracy of quantum chemical energetics with the thorough sampling of finite-temperature fluctuations. To reach this goal, a diverse set of methods has been pr ...

2022

Automated identification of local contamination in remote atmospheric composition time series

Jian Wang, Julia Schmale, Andrea Baccarini, Ivo Fabio Beck, Hélène Paule Angot, Lubna Dada

A common challenge of atmospheric measurements in remote environments is to identify pollution from nearby activities that interfere with the purpose of the observations. Pollution, particularly from combustion, typically reveals itself in enhanced particl ...

2022

Empowering Global Chemical Biology at the Dawn of the New Decade

Yimon Aye

On January 22-24, 2020, scientific luminaries across the far-flung corners of chemical biology gathered in Geneva, Switzerland, to deliver their latest and greatest discoveries in the field. Generously supported by the Swiss National Science Foundation (SN ...

AMER CHEMICAL SOC2020

Photoredox-Catalyzed Decarboxylation for the Transfer of Alkynes and Nitriles Using Hypervalent Iodine Reagents

Franck Le Vaillant

Aliphatic alkynes and nitriles are privileged motifs in organic chemistry. Therefore, alkynes and nitriles have played a central role for the exploration and development of novel strategies to forge C-C bonds in efficient manner. They are broadly used as v ...

EPFL2019

Extrapolating the Fragment-Based Approach to Inorganic Drug Discovery

Paul Joseph Dyson, Lucinda Kate Batchelor

The fragment-based approach is a well-established strategy for organic drug discovery. Recent studies have shown that this approach also has considerable potential in medicinal inorganic chemistry, and yet the approach has not been formally described. Here ...

ELSEVIER2019

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