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Philipp Seidl

5 accepted papers

2026

MolecularIQ: Characterizing Chemical Reasoning Capabilities Through Symbolic Verification on Molecular Graphs

Christoph Bartmann, Johannes Schimunek, Mykyta Ielanskyi, Philipp Seidl, Günter Klambauer, Sohvi Luukkonen
ICLR 2026poster

Large Language Models (LLMs) are increasingly applied to chemistry, tackling tasks such as molecular name conversion, captioning, text-guided generation, and property or reaction prediction. A molecule’s properties are fundamentally determined by its composition and structure, encoded in its molecul…

Cited by 0Source ↗Scholar ↗
2025

Bio-xLSTM: Generative modeling, representation and in-context learning of biological and chemical sequences

Niklas Schmidinger, Lisa Schneckenreiter, Philipp Seidl, Johannes Schimunek, Pieter-Jan Hoedt, Johannes Brandstetter +4
ICLR 2025poster

Language models for biological and chemical sequences enable crucial applications such as drug discovery, protein engineering, and precision medicine. Currently, these language models are predominantly based on Transformer architectures. While Transformers have yielded impressive results, their quad…

Cited by 6Source ↗PDF ↗Scholar ↗
2023

Context-enriched molecule representations improve few-shot drug discovery

Johannes Schimunek, Philipp Seidl, Lukas Friedrich, Daniel Kuhn, Friedrich Rippmann, Sepp Hochreiter +1
ICLR 2023poster

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 only very few active molecules are known. Therefore, few-shot learning methods have the potential to improve the effectiv…

Cited by 45Source ↗PDF ↗code ↗Scholar ↗
2023

Enhancing Activity Prediction Models in Drug Discovery with the Ability to Understand Human Language

Philipp Seidl, Andreu Vall, Sepp Hochreiter, Günter Klambauer
ICML 2023poster

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. Without training or fine-tuning, scientific language models could be used for such low-data tasks through their announced z…

Cited by 63Source ↗PDF ↗code ↗Scholar ↗
2021

Hopfield Networks is All You Need

Hubert Ramsauer, Bernhard Schäfl, Johannes Lehner, Philipp Seidl, Michael Widrich, Lukas Gruber +7
ICLR 2021poster

We introduce a modern Hopfield network with continuous states and a corresponding update rule. The new Hopfield network can store exponentially (with the dimension of the associative space) many patterns, retrieves the pattern with one update, and has exponentially small retrieval errors. It has thr…

Cited by 673Source ↗PDF ↗code ↗Scholar ↗