Kristof T. Schütt
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View article: Equivariant diffusion for structure-based de novo ligand generation with latent-conditioning
Equivariant diffusion for structure-based de novo ligand generation with latent-conditioning Open
We introduce PoLiGenX, a novel generative model for de novo ligand design that employs latent-conditioned, target-aware equivariant diffusion. Our approach leverages the conditioning of the ligand generation process on reference molecules …
View article: Accelerating crystal structure search through active learning with neural networks for rapid relaxations
Accelerating crystal structure search through active learning with neural networks for rapid relaxations Open
Global optimization of crystal compositions is a significant yet computationally intensive method to identify stable structures within chemical space. The specific physical properties linked to a three-dimensional atomic arrangement make t…
View article: Latent-Conditioned Equivariant Diffusion for Structure-Based De Novo Ligand Generation
Latent-Conditioned Equivariant Diffusion for Structure-Based De Novo Ligand Generation Open
We propose PoLiGenX for de novo ligand design using latent-conditioned, target-aware equivariant diffusion. Our model leverages the conditioning of the generation process on reference molecules within a protein pocket to produce shape-simi…
View article: Accelerating crystal structure search through active learning with neural networks for rapid relaxations
Accelerating crystal structure search through active learning with neural networks for rapid relaxations Open
Global optimization of crystal compositions is a significant yet computationally intensive method to identify stable structures within chemical space. The specific physical properties linked to a three-dimensional atomic arrangement make t…
View article: PILOT: Equivariant diffusion for pocket conditioned de novo ligand generation with multi-objective guidance via importance sampling
PILOT: Equivariant diffusion for pocket conditioned de novo ligand generation with multi-objective guidance via importance sampling Open
The generation of ligands that both are tailored to a given protein pocket and exhibit a range of desired chemical properties is a major challenge in structure-based drug design. Here, we propose an in-silico approach for the $\textit{de n…
View article: PILOT: equivariant diffusion for pocket-conditioned <i>de novo</i> ligand generation with multi-objective guidance <i>via</i> importance sampling
PILOT: equivariant diffusion for pocket-conditioned <i>de novo</i> ligand generation with multi-objective guidance <i>via</i> importance sampling Open
Creating ligands that fit specific protein pockets and possess desired chemical properties is a key challenge in SBDD. Guided 3D diffusion models present a promising solution, offering precise ligand generation with tailored properties.
View article: Navigating the Design Space of Equivariant Diffusion-Based Generative Models for De Novo 3D Molecule Generation
Navigating the Design Space of Equivariant Diffusion-Based Generative Models for De Novo 3D Molecule Generation Open
Deep generative diffusion models are a promising avenue for 3D de novo molecular design in materials science and drug discovery. However, their utility is still limited by suboptimal performance on large molecular structures and limited tr…
View article: Automatic identification of chemical moieties
Automatic identification of chemical moieties Open
A versatile, transferable and differentiable method to automatically identify chemical moieties based on message passing neural network feature representations.
View article: SchNetPack 2.0: A neural network toolbox for atomistic machine learning
SchNetPack 2.0: A neural network toolbox for atomistic machine learning Open
SchNetPack is a versatile neural networks toolbox that addresses both the requirements of method development and application of atomistic machine learning. Version 2.0 comes with an improved data pipeline, modules for equivariant neural ne…
View article: Automatic Identification of Chemical Moieties
Automatic Identification of Chemical Moieties Open
In recent years, the prediction of quantum mechanical observables with machine learning methods has become increasingly popular. Message-passing neural networks (MPNNs) solve this task by constructing atomic representations, from which the…
View article: Roadmap on Machine learning in electronic structure
Roadmap on Machine learning in electronic structure Open
In recent years, we have been witnessing a paradigm shift in computational materials science. In fact, traditional methods, mostly developed in the second half of the XXth century, are being complemented, extended, and sometimes even compl…
View article: Higher-Order Explanations of Graph Neural Networks via Relevant Walks
Higher-Order Explanations of Graph Neural Networks via Relevant Walks Open
Graph Neural Networks (GNNs) are a popular approach for predicting graph structured data. As GNNs tightly entangle the input graph into the neural network structure, common explainable AI approaches are not applicable. To a large extent, G…
View article: Datasets: Machine learning of solvent effects on molecular spectra and reactions
Datasets: Machine learning of solvent effects on molecular spectra and reactions Open
Fast and accurate simulation of complex chemical systems in environments such as solutions is a long standing challenge in theoretical chemistry. In recent years, machine learning has extended the boundaries of quantum chemistry by providi…
View article: Machine Learning Force Fields
Machine Learning Force Fields Open
In recent years, the use of Machine Learning (ML) in computational chemistry has enabled numerous advances previously out of reach due to the computational complexity of traditional electronic-structure methods. One of the most promising a…
View article: Deep integration of machine learning into computational chemistry and materials science
Deep integration of machine learning into computational chemistry and materials science Open
Machine learning (ML) methods are being used in almost every conceivable area of electronic structure theory and molecular simulation. In particular, ML has become firmly established in the construction of high-dimensional interatomic pote…
View article: Equivariant message passing for the prediction of tensorial properties and molecular spectra
Equivariant message passing for the prediction of tensorial properties and molecular spectra Open
Message passing neural networks have become a method of choice for learning on graphs, in particular the prediction of chemical properties and the acceleration of molecular dynamics studies. While they readily scale to large training data …
View article: Machine learning of solvent effects on molecular spectra and reactions
Machine learning of solvent effects on molecular spectra and reactions Open
A machine learning approach for modeling the influence of external environments and fields on molecules has been developed, which allows the prediction of various types of molecular spectra in vacuum and under implicit and explicit solvati…
View article: XAI for Graphs: Explaining Graph Neural Network Predictions by Identifying Relevant Walks
XAI for Graphs: Explaining Graph Neural Network Predictions by Identifying Relevant Walks Open
Graph Neural Networks (GNNs) are a popular approach for predicting graph structured data. As GNNs tightly entangle the input graph into the neural network structure, common explainable AI (XAI) approaches are not applicable. To a large ext…
View article: Hamiltonian datasets: "Unifying machine learning and quantum chemistry with a deep neural network for molecular wavefunctions""
Hamiltonian datasets: "Unifying machine learning and quantum chemistry with a deep neural network for molecular wavefunctions"" Open
Machine learning advances chemistry and materials science by enabling large-scale exploration of chemical space based on quantum chemical calculations. While these models supply fast and accurate predictions of atomistic chemical propertie…
View article: Unifying machine learning and quantum chemistry -- a deep neural network for molecular wavefunctions
Unifying machine learning and quantum chemistry -- a deep neural network for molecular wavefunctions Open
Machine learning advances chemistry and materials science by enabling large-scale exploration of chemical space based on quantum chemical calculations. While these models supply fast and accurate predictions of atomistic chemical propertie…
View article: Symmetry-adapted generation of 3d point sets for the targeted discovery of molecules
Symmetry-adapted generation of 3d point sets for the targeted discovery of molecules Open
Deep learning has proven to yield fast and accurate predictions of quantum-chemical properties to accelerate the discovery of novel molecules and materials. As an exhaustive exploration of the vast chemical space is still infeasible, we re…
View article: Symmetry-adapted generation of 3d point sets for the targeted discovery of molecules
Symmetry-adapted generation of 3d point sets for the targeted discovery of molecules Open
Deep learning has proven to yield fast and accurate predictions of quantum-chemical properties to accelerate the discovery of novel molecules and materials. As an exhaustive exploration of the vast chemical space is still infeasible, we re…
View article: Learning representations of molecules and materials with atomistic\n neural networks
Learning representations of molecules and materials with atomistic\n neural networks Open
Deep Learning has been shown to learn efficient representations for\nstructured data such as image, text or audio. In this chapter, we present\nneural network architectures that are able to learn efficient representations\nof molecules and…