37 citations · 92 across the 10 of their papers we have counts for
10 papers
MolecularIQ: Characterizing Chemical Reasoning Capabilities Through Symbolic Verification on Molecular Graphs
Christoph Bartmann, Johannes Schimunek, Mykyta Ielanskyi +3
A molecule's properties are fundamentally determined by its composition and structure encoded in its molecular graph. Thus, reasoning about molecular properties requires the abilit…
Federated Few-Shot Learning for Epileptic Seizure Detection Under Privacy Constraints
Ekaterina Sysoykova, Bernhard Anzengruber-Tanase, Michael Haslgrubler +2
Many deep learning approaches have been developed for EEG-based seizure detection; however, most rely on access to large centralized annotated datasets. In clinical practice, EEG d…
Bio-xLSTM: Generative modeling, representation and in-context learning of biological and chemical sequences
Niklas Schmidinger, Lisa Schneckenreiter, Philipp Seidl +7
Language models for biological and chemical sequences enable crucial applications such as drug discovery, protein engineering, and precision medicine. Currently, these language mod…
Re-evaluating Retrosynthesis Algorithms with Syntheseus
Krzysztof Maziarz, Austin Tripp, Guoqing Liu +5
Automated Synthesis Planning has recently re-emerged as a research area at the intersection of chemistry and machine learning. Despite the appearance of steady progress, we argue t…
Context-enriched molecule representations improve few-shot drug discovery
Johannes Schimunek, Philipp Seidl, Lukas Friedrich +4
A central task in computational drug discovery is to construct models from known active molecules to find further promising molecules for subsequent screening. However, typically o…
Enhancing Activity Prediction Models in Drug Discovery with the Ability to Understand Human Language
Philipp Seidl, Andreu Vall, Sepp Hochreiter +1
Activity and property prediction models are the central workhorses in drug discovery and materials sciences, but currently they have to be trained or fine-tuned for new tasks. With…
Supervised machine learning classification for short straddles on the S&P500
Alexander Brunhuemer, Lukas Larcher, Philipp Seidl +3
In this working paper we present our current progress in the training of machine learning models to execute short option strategies on the S&P500. As a first step, this paper is br…
Modern Hopfield Networks for Few- and Zero-Shot Reaction Template Prediction
Philipp Seidl, Philipp Renz, Natalia Dyubankova +6
Finding synthesis routes for molecules of interest is an essential step in the discovery of new drugs and materials. To find such routes, computer-assisted synthesis planning (CASP…
A physical protocol for observers near the boundary to obtain bulk information in quantum gravity
Chandramouli Chowdhury, Olga Papadoulaki, Suvrat Raju
We consider a set of observers who live near the boundary of global AdS, and are allowed to act only with simple low-energy unitaries and make measurements in a small interval of t…
Large-scale ligand-based virtual screening for SARS-CoV-2 inhibitors using deep neural networks
Markus Hofmarcher, Andreas Mayr, Elisabeth Rumetshofer +8
Due to the current severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) pandemic, there is an urgent need for novel therapies and drugs. We conducted a large-scale virtual…