BioMatrix unifies sequences, structures, and language for molecules and proteins inside one decoder-only foundation model via shared discrete tokens and achieves SOTA or competitive results on 77 of 80 downstream tasks.
A molecular multimodal foundation model associating molecule graphs with natural language
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MoleCode is a training-free, LLM-native representation that makes molecular graphs with explicit atoms, bonds, and topology directly readable and editable in language models, improving structural tasks over implicit string encodings.
S^2-Bench is a new one-to-many benchmark for natural language-driven molecule generation with three tasks, and OpenMolIns is an instruction dataset enabling Llama3.1-8B to outperform GPT-4o and Claude-3.5 on it.
A multimodal alignment framework derives morphology-aware image embeddings as continuous traits for Bayesian phylogenetic reconstruction, improving topological agreement on the Rove-Tree-11 dataset.
BadGraph poisons training data with textual triggers to implant backdoors in latent diffusion models for text-guided graph generation, achieving 50% attack success rate at under 10% poisoning and over 80% at 24% poisoning with negligible clean performance loss.
MolReFlect introduces a teacher-student framework that automatically creates fine-grained molecule-text alignments to achieve SOTA results on molecule-caption translation.
MolE-RAG is a training-free RAG framework that augments LLMs with literature, molecular context, and structural analogs to improve performance on nine molecular property prediction tasks.
Bolek injects Morgan fingerprint embeddings into an instruction-tuned text model, then fine-tunes on molecular alignment and synthetic chain-of-thought tasks to improve performance and grounding on 15 TDC binary classification endpoints while generalizing to unseen tasks.
citing papers explorer
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BioMatrix: Towards a Comprehensive Biological Foundation Model Spanning the Modality Matrix of Sequences, Structures, and Language
BioMatrix unifies sequences, structures, and language for molecules and proteins inside one decoder-only foundation model via shared discrete tokens and achieves SOTA or competitive results on 77 of 80 downstream tasks.
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MoleCode unlocks structural intelligence in large language models
MoleCode is a training-free, LLM-native representation that makes molecular graphs with explicit atoms, bonds, and topology directly readable and editable in language models, improving structural tasks over implicit string encodings.
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Speak-to-Structure: Evaluating LLMs in Open-domain Natural Language-Driven Molecule Generation
S^2-Bench is a new one-to-many benchmark for natural language-driven molecule generation with three tasks, and OpenMolIns is an instruction dataset enabling Llama3.1-8B to outperform GPT-4o and Claude-3.5 on it.
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Morphology-Aware Multimodal Representation Learning for Insect Phylogenetic Reconstruction
A multimodal alignment framework derives morphology-aware image embeddings as continuous traits for Bayesian phylogenetic reconstruction, improving topological agreement on the Rove-Tree-11 dataset.
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BadGraph: A Backdoor Attack Against Latent Diffusion Model for Text-Guided Graph Generation
BadGraph poisons training data with textual triggers to implant backdoors in latent diffusion models for text-guided graph generation, achieving 50% attack success rate at under 10% poisoning and over 80% at 24% poisoning with negligible clean performance loss.
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MolReFlect: Towards In-Context Fine-grained Alignments between Molecules and Texts
MolReFlect introduces a teacher-student framework that automatically creates fine-grained molecule-text alignments to achieve SOTA results on molecule-caption translation.
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MolE-RAG: Molecular Structure-Enhanced Retrieval-Augmented Generation for Chemistry
MolE-RAG is a training-free RAG framework that augments LLMs with literature, molecular context, and structural analogs to improve performance on nine molecular property prediction tasks.
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Bolek: A Multimodal Language Model for Molecular Reasoning
Bolek injects Morgan fingerprint embeddings into an instruction-tuned text model, then fine-tunes on molecular alignment and synthetic chain-of-thought tasks to improve performance and grounding on 15 TDC binary classification endpoints while generalizing to unseen tasks.