Early detection of malicious behavior in JavaScript code. - Kristof Schütt; Marius Kloft; Alexander Bikadorov; Konrad Rieck (AISec, 2012)

Investigating the influence of noise and distractors on the interpretation of neural networks. - Pieter-Jan Kindermans; Kristof Schütt; Klaus-Robert Müller; Sven Dähne (CoRR, 2016)

An Empirical Study on The Properties of Random Bases for Kernel Methods. - Maximilian Alber; Pieter-Jan Kindermans; Kristof Schütt; Klaus-Robert Müller; Fei Sha (NIPS, 2017)

SchNet: A continuous-filter convolutional neural network for modeling quantum interactions. - Kristof Schütt; Pieter-Jan Kindermans; Huziel Enoc Sauceda Felix; Stefan Chmiela; Alexandre Tkatchenko; Klaus-Robert Müller (NIPS, 2017)

PatternNet and PatternLRP - Improving the interpretability of neural networks. - Pieter-Jan Kindermans; Kristof T. Schütt; Maximilian Alber; Klaus-Robert Müller; Sven Dähne (CoRR, 2017)

The (Un)reliability of saliency methods. - Pieter-Jan Kindermans; Sara Hooker; Julius Adebayo; Maximilian Alber; Kristof T. Schütt; Sven Dähne; Dumitru Erhan; Been Kim (CoRR, 2017)

Learning how to explain neural networks: PatternNet and PatternAttribution. - Pieter-Jan Kindermans; Kristof T. Schütt; Maximilian Alber; Klaus-Robert Müller; Dumitru Erhan; Been Kim; Sven Dähne (ICLR (Poster), 2018)

Quantum-chemical insights from interpretable atomistic neural networks. - Kristof T. Schütt; Michael Gastegger; Alexandre Tkatchenko; Klaus-Robert Müller (CoRR, 2018)

Generating equilibrium molecules with deep neural networks. - Niklas W. A. Gebauer; Michael Gastegger; Kristof T. Schütt (CoRR, 2018)

Learning representations of molecules and materials with atomistic neural networks. - Kristof T. Schütt; Alexandre Tkatchenko; Klaus-Robert Müller (CoRR, 2018)

Learning representations of atomistic systems with deep neural networks (Lernen von Repräsentationen für atomistische Systeme mit tiefen neuronalen Netzen) - Kristof Schütt (2018)

Learning Representations of Atomistic Systems with Deep Neural Networks. - Kristof Schütt (Ausgezeichnete Informatikdissertationen, 2018)

Symmetry-adapted generation of 3d point sets for the targeted discovery of molecules. - Niklas W. A. Gebauer; Michael Gastegger; Kristof Schütt (NeurIPS, 2019)

iNNvestigate Neural Networks! - Maximilian Alber; Sebastian Lapuschkin; Philipp Seegerer; Miriam Hägele; Kristof T. Schütt; Grégoire Montavon; Wojciech Samek; Klaus-Robert Müller; Sven Dähne; Pieter-Jan Kindermans (J. Mach. Learn. Res., 2019)

The (Un)reliability of Saliency Methods. - Pieter-Jan Kindermans; Sara Hooker; Julius Adebayo; Maximilian Alber; Kristof T. Schütt; Sven Dähne; Dumitru Erhan; Been Kim (Explainable AI, 2019)

Quantum-Chemical Insights from Interpretable Atomistic Neural Networks. - Kristof T. Schütt; Michael Gastegger; Alexandre Tkatchenko; Klaus-Robert Müller (Explainable AI, 2019)

Autonomous robotic nanofabrication with reinforcement learning. - Philipp Leinen; Malte Esders; Kristof T. Schütt; Christian Wagner 007; Klaus-Robert Müller; F. Stefan Tautz (CoRR, 2020)

XAI for Graphs: Explaining Graph Neural Network Predictions by Identifying Relevant Walks. - Thomas Schnake; Oliver Eberle; Jonas Lederer; Shinichi Nakajima; Kristof T. Schütt; Klaus-Robert Müller; Grégoire Montavon (CoRR, 2020)

Equivariant message passing for the prediction of tensorial properties and molecular spectra. - Kristof Schütt; Oliver T. Unke; Michael Gastegger (ICML, 2021)

SpookyNet: Learning Force Fields with Electronic Degrees of Freedom and Nonlocal Effects. - Oliver T. Unke; Stefan Chmiela; Michael Gastegger; Kristof T. Schütt; Huziel E. Sauceda; Klaus-Robert Müller (CoRR, 2021)

Inverse design of 3d molecular structures with conditional generative neural networks. - Niklas W. A. Gebauer; Michael Gastegger; Stefaan Simon Pierre Hessmann; Klaus-Robert Müller; Kristof T. Schütt (CoRR, 2021)

Automatic Identification of Chemical Moieties. - Jonas Lederer; Michael Gastegger; Kristof T. Schütt; Michael Kampffmeyer; Klaus-Robert Müller; Oliver T. Unke (CoRR, 2022)

Higher-Order Explanations of Graph Neural Networks via Relevant Walks. - Thomas Schnake; Oliver Eberle; Jonas Lederer; Shinichi Nakajima; Kristof T. Schütt; Klaus-Robert Müller; Grégoire Montavon (IEEE Trans. Pattern Anal. Mach. Intell., 2022)

Latent-Conditioned Equivariant Diffusion for Structure-Based De Novo Ligand Generation. - Julian Cremer; Tuan Le; Djork-Arné Clevert; Kristof T. Schütt (AIDD@ICANN, 2024)

Navigating the Design Space of Equivariant Diffusion-Based Generative Models for De Novo 3D Molecule Generation. - Tuan Le; Julian Cremer; Frank Noé; Djork-Arné Clevert; Kristof T. Schütt (ICLR, 2024)

PILOT: Equivariant diffusion for pocket conditioned de novo ligand generation with multi-objective guidance via importance sampling. - Julian Cremer; Tuan Le; Frank Noé; Djork-Arné Clevert; Kristof T. Schütt (CoRR, 2024)

Equivariant diffusion for structure-based de novo ligand generation with latent-conditioning. - Tuan Le; Julian Cremer; Djork-Arné Clevert; Kristof T. Schütt (J. Cheminformatics, 2025)