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20242026
most citedMolecularIQ: Characterizing Chemical Reasoning Capabilities Through Symbolic Verification on Molecular Graphs

2 citations · 2 across the 6 of their papers we have counts for

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cs.LG2026

TiRex-2: Generalizing TiRex to Multivariate Data and Streaming

Patrick Podest, Marco Pichler, Elias Bürger +7

We introduce TiRex-2, a recurrent xLSTM-based time series foundation model that generalizes the univariate TiRex to multivariate forecasting with both past and future covariates. R…

cs.LG2026

Contrastive Geometric Learning Unlocks Unified Structure- and Ligand-Based Drug Design

Lisa Schneckenreiter, Sohvi Luukkonen, Lukas Friedrich +2

Structure-based and ligand-based computational drug design have traditionally relied on disjoint data sources and modeling assumptions, limiting their joint use at scale. In this w…

cs.LG20262 cited

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…

cs.LG2026

LaM-SLidE: Latent Space Modeling of Spatial Dynamical Systems via Linked Entities

Florian Sestak, Artur Toshev, Andreas Fürst +3

Generative models are spearheading recent progress in deep learning, showcasing strong promise for trajectory sampling in dynamical systems as well. However, whereas latent space m…

cs.LG2025

Measuring AI Progress in Drug Discovery: A Reproducible Leaderboard for the Tox21 Challenge

Antonia Ebner, Christoph Bartmann, Sonja Topf +3

Deep learning's rise since the early 2010s has transformed fields like computer vision and natural language processing and strongly influenced biomedical research. For drug discove…

cs.LG2025

TiRex: Zero-Shot Forecasting Across Long and Short Horizons with Enhanced In-Context Learning

Andreas Auer, Patrick Podest, Daniel Klotz +3

In-context learning, the ability of large language models to perform tasks using only examples provided in the prompt, has recently been adapted for time series forecasting. This p…