A collaborative constrained graph diffusion model for the generation of realistic synthetic molecules - Nature Machine Intelligence

The collaborative constrained graph diffusion model CoCoGraph generates novel molecules that are guaranteed to be valid and more realistic than state-of-the-art outputs, while achieving faster perform...

nature.com

Chemistry-aware AI can generate millions of plausible new molecules

Finding and developing new molecules is one of the great research endeavors of modern chemistry. From the development of new drugs to the creation of more sustainable materials, everything depends on finding new combinations of atoms with useful properties.

phys.org

A collaborative constrained graph diffusion model for the generation of realistic synthetic molecules - Nature Machine Intelligence

The collaborative constrained graph diffusion model CoCoGraph generates novel molecules that are guaranteed to be valid and more realistic than state-of-the-art outputs, while achieving faster performance with up to an order of magnitude fewer parameters.

dx.doi.org

A collaborative constrained graph diffusion model for the generation of realistic synthetic molecules - Nature Machine Intelligence

The collaborative constrained graph diffusion model CoCoGraph generates novel molecules that are guaranteed to be valid and more realistic than state-of-the-art outputs, while achieving faster performance with up to an order of magnitude fewer parameters.

dx.doi.org

ChemEmbed: a deep learning framework for metabolite identification using enhanced MS/MS data and multidimensional molecular embeddings

Abstract. Machine learning offers a promising path to annotating the large number of unidentified MS/MS spectra in metabolomics, addressing the limited cov

academic.oup.com

SingleFrag: a deep learning tool for MS/MS fragment and spectral prediction and metabolite annotation

Abstract. Metabolite and small molecule identification via tandem mass spectrometry (MS/MS) involves matching experimental spectra with prerecorded spectra

academic.oup.com

SingleFrag: a deep learning tool for MS/MS fragment and spectral prediction and metabolite annotation

Abstract. Metabolite and small molecule identification via tandem mass spectrometry (MS/MS) involves matching experimental spectra with prerecorded spectra

academic.oup.com

SingleFrag: a deep learning tool for MS/MS fragment and spectral prediction and metabolite annotation

Abstract. Metabolite and small molecule identification via tandem mass spectrometry (MS/MS) involves matching experimental spectra with prerecorded spectra

academic.oup.com

A collaborative constrained graph diffusion model for the generation of realistic synthetic molecules

Developing new molecular compounds is crucial to address pressing challenges, from health to environmental sustainability. However, exploring the molecular space to discover new molecules is difficult...

arxiv.org

Applied physics and mathematics

The highlights include but are not limited to the research areas of electronics, optoelectronics, computing technologies and theories, soft matter physics, ...

nature.com

Probabilistic alignment of multiple networks - Nature Communications

Network alignment is a fundamental problem in several domains that aims at mapping nodes across networks. Here, the authors develop a probabilistic approach that assumes that observed networks are err...

nature.com

‘Data manipulations’ alleged in study that paved the way for Microsoft’s quantum chip

Internal emails from 2021 reveal tensions among researchers hunting for elusive Majorana particle

science.org

Multiscale Field Theory for Network Flows

A new theoretical framework reveals universal principles governing network flows, predicting a threshold where flow becomes unsustainable and uncovering how dissipation can enhance performance in cert...

journals.aps.org

Assistant/Associate Professor in Statistical Learning

Assistant/Associate Professor in Statistical Learning

vacancies.maastrichtuniversity.nl

How probabilistic modeling transforms our approach to aligning complex networks

Networks are everywhere—from social connections among individuals, neural connections in brains, interactions among proteins in biological cells, to communication channels within large organizations.

open.substack.com

Probabilistic alignment of multiple networks

Consider K network observations, with N nodes each and adjacency matrices {Ak; , k = 1, …, K }. We consider networks that are directed and with binary edges (that is, we just consider the presence or absence of connection...

nature.com

Probabilistic alignment of multiple networks - Nature Communications

Network alignment is a fundamental problem in several domains that aims at mapping nodes across networks. Here, the authors develop a probabilistic approach that assumes that observed networks are err...

nature.com

Probabilistic alignment of multiple networks - Nature Communications

Network alignment is a fundamental problem in several domains that aims at mapping nodes across networks. Here, the authors develop a probabilistic approach that assumes that observed networks are err...

nature.com

Probabilistic alignment of multiple networks - Nature Communications

Network alignment is a fundamental problem in several domains that aims at mapping nodes across networks. Here, the authors develop a probabilistic approach that assumes that observed networks are err...

nature.com