Kristof T. Schütt

Pfizer (Germany) — Germany

Materials Science · Physical Sciences

26h-index9.7kcitations63works5.32yr avg
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Research Topics

Machine Learning in Materials Science(44), Computational Drug Discovery Methods(33), Protein Structure and Dynamics(17), Explainable Artificial Intelligence (XAI)(9), Various Chemistry Research Topics(7)

Publications63 total

Coupled Fragment-Based Generative Modeling with Stochastic Interpolants
2025Open Access
Protein–ligand data at scale to support machine learning
Nature Reviews Chemistry·2025· 7 cited
Equivariant diffusion for structure-based de novo ligand generation with latent-conditioning
Journal of Cheminformatics·2025· 1 citedOpen Access
Accelerating crystal structure search through active learning with neural networks for rapid relaxations
npj Computational Materials·2025· 8 citedOpen Access
Latent-Conditioned Equivariant Diffusion for Structure-Based De Novo Ligand Generation
Lecture notes in computer science·2024· 3 citedOpen Access
S02-02 Molecular representation learning for drug discovery
Toxicology Letters·2024
Accelerating crystal structure search through active learning with neural networks for rapid relaxations
arXiv (Cornell University)·2024Open Access
Author response for "PILOT: equivariant diffusion for pocket-conditioned <i>de novo</i> ligand generation with multi-objective guidance <i>via</i> importance sampling"
2024
A data science roadmap for open science organizations engaged in early-stage drug discovery
Nature Communications·2024· 20 citedOpen Access
PILOT: Equivariant diffusion for pocket conditioned de novo ligand generation with multi-objective guidance via importance sampling
arXiv (Cornell University)·2024Open Access
PILOT: equivariant diffusion for pocket-conditioned <i>de novo</i> ligand generation with multi-objective guidance <i>via</i> importance sampling
Chemical Science·2024· 19 citedOpen Access
Navigating the Design Space of Equivariant Diffusion-Based Generative Models for De Novo 3D Molecule Generation
arXiv (Cornell University)·2023· 6 citedOpen Access
SchNetPack 2.0: A neural network toolbox for atomistic machine learning
The Journal of Chemical Physics·2023· 75 cited
Automatic identification of chemical moieties
Physical Chemistry Chemical Physics·2023· 10 citedOpen Access
SchNetPack 2.0: A neural network toolbox for atomistic machine learning
arXiv (Cornell University)·2022· 1 citedOpen Access
Automatic Identification of Chemical Moieties
arXiv (Cornell University)·2022· 3 citedOpen Access
Inverse design of 3d molecular structures with conditional generative neural networks
Nature Communications·2022· 182 citedOpen Access
Roadmap on Machine learning in electronic structure
Electronic Structure·2022· 175 citedOpen Access
SpookyNet: Learning force fields with electronic degrees of freedom and nonlocal effects
Nature Communications·2021· 293 citedOpen Access
Higher-Order Explanations of Graph Neural Networks via Relevant Walks
IEEE Transactions on Pattern Analysis and Machine Intelligence·2021· 238 citedOpen Access
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Frequent Co-authors

Klaus‐Robert Müller(23), Michael Gastegger(21), Alexandre Tkatchenko(18), Pieter-Jan Kindermans(9), Stefan Chmiela(9), Huziel E. Sauceda(8), K. Müller(7), Maximilian Alber(7), Sven Dähne(7), Oliver T. Unke(6), Niklas W. A. Gebauer(6), Jonas Lederer(5), Djork-Arné Clevert(5), Reinhard J. Maurer(4), Dumitru Erhan(4), Been Kim(4), Grégoire Montavon(3), Stefaan S. P. Hessmann(3), Frank Noé(3), Julian Cremer(3)