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Johannes Schimunek

3 accepted papers

2026

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

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 0SourceScholar
2025

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

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 6SourcePDFScholar
2023

Context-enriched molecule representations improve few-shot drug discovery

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…