arXiv AI
Aug 12
Proteo-R1: Reasoning Foundation Models for De Novo Protein Design
arXiv:2605. 02937v2 Announce Type: replace-cross Abstract: Deep learning in de novo protein design has achieved atomic-level fidelity.
Speak to a Protein: An Interactive Multimodal Co-Scientist
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arXiv AI
Sep 23
ChemVTS-Bench: Evaluating Visual-Textual-Symbolic Reasoning of Multimodal Large Language Models in Chemistry
ChemVTS-Bench is a domain-authentic benchmark that evaluates Visual‑Textual‑Symbolic reasoning in multimodal large language models for chemistry. It presents diverse chemical problems—organic molecules, inorganic materials, and 3D crystal structures—in three input modes: visual-only, visual‑text hybrid, and SMILES-based symbolic. The benchmark includes an automated agent workflow for inference, answer verification, and failure diagnosis, and shows that visual-only inputs and structural chemistry remain challenging for current models.
arXiv AI
Aug 6
AutoProteinEngine: A Large Language Model Driven Agent Framework for Multimodal AutoML in Protein Engineering
arXiv:2411. 04440v1 Announce Type: cross Abstract: Protein engineering is important for biomedical applications, but conventional approaches are often inefficient and resource-intensive.
arXiv AI
Jul 24
Monkey King Bang: A Unified Scientific Multimodal Foundation Model
arXiv:2607. 20557v1 Announce Type: cross Abstract: Scientific discovery is increasingly shifting from isolated disciplines to multi-domain reasoning, and AI for science faces a similar transition.
arXiv AI
Aug 26
MolEmb: Multimodal Large Language Models Can Be Strong Molecular Embedding Models
MolEmb is a lightweight framework that adapts multimodal large language models (MLLMs) to serve as general molecular embedding models. By aligning molecular profiles with textual descriptions in a shared embedding space using a bidirectional contrastive objective, MolEmb produces embeddings conditioned on both a molecular profile and a natural‑language semantic context. The model performs competitively on molecular property prediction and enables cross‑modal molecule‑text retrieval, while the newly introduced MolCAR benchmark demonstrates that context‑aware molecular embedding is largely a data property of the supervision.
arXiv Machine Learning
Jul 15
PRIME: Protein Representation via Physics-Informed Multiscale Equivariant Hierarchies
arXiv:2605. 01625v3 Announce Type: replace Abstract: Proteins are inherently multiscale physical systems whose functional properties emerge from coordinated structural organization across multiple spatial resolutions, ranging from atomic interactions to global fold topology.
arXiv Machine Learning
Sep 29
ChemMLLM: Chemical Multimodal Large Language Model
ChemMLLM is a unified chemical multimodal large language model designed for molecule understanding and generation across text, SMILES strings, and images. The authors curated five multimodal tasks and benchmarked ChemMLLM against leading general MLLMs, chemical LLMs, and specialized models, finding it outperforms general-purpose MLLMs and matches specialized models on all tasks. The study demonstrates that a single foundation model can handle diverse cross‑modal chemical tasks, including image generation, enabling more intuitive visual human‑AI interaction.
arXiv Machine Learning
Sep 14
LatentVerse: A Framework for Understanding Shared and Modality-Specific Information in Multimodal Latent Representations
LatentVerse is a new framework that provides a web-based visual analytics platform and a command-line interface for analyzing multimodal latent representations. It unifies diagnostics for representation quality metrics and extends analysis to multimodal settings by decomposing embeddings into shared and modality-specific components. The authors evaluate the tool through simulations, real biomedical data analyses, and a user study, demonstrating its utility for interpretable evaluation of foundation model representations.
arXiv AI
Jul 9
Accurate, Interdisciplinary and Transparent Structure-property Understanding with Deep Native Structural Reasoning
arXiv:2607. 07708v1 Announce Type: cross Abstract: Structure-property relationships are foundational to biology, chemistry and materials science, where function, reactivity and physical response emerge from spatial, chemical and periodic organization.
arXiv AI
Jun 3
CP-Agent: Context-Aware Multimodal Reasoning for Cellular Morphological Profiling under Chemical Perturbations
arXiv:2606. 03435v1 Announce Type: new Abstract: Cell Painting combines multiplexed fluorescent staining, high-content imaging, and quantitative analysis to generate high-dimensional phenotypic readouts to support diverse downstream tasks such as mechanism-of-action (MoA) inference, toxicity prediction, and construction of drug-disease atlases.
Hugging Face Trending Papers
Jul 8
Accurate, Interdisciplinary and Transparent Structure-property Understanding with Deep Native Structural Reasoning
Structure-property relationships are foundational to biology, chemistry and materials science, where function, reactivity and physical response emerge from spatial, chemical and periodic organization. Mechanistically explaining these relationships requires interpreting structural evidence through scientific principles and physical constraints, from stereochemistry and bonding to symmetry, energetics and periodic order.
arXiv AI
6d ago
Does Learning Protein Folding Generalize to Broader Reasoning?
arXiv:2609.38879v1 Announce Type: cross Abstract: Large language models rely heavily on human text, which often conveys surface answers rather than the spatial and structural logic behind them. Prote...