How 'Foundational' Are Current Molecular Foundation Models?

arXiv:2609.37550 · q-bio.BM, q-bio.QM · Submitted 2026-09-29 · Read on arXiv

q-bio.BM, q-bio.QM

Submitted: 2026-09-29

Updated: 2026-09-29

Terminology

• Foundation Model — 28× in this paper · explained in 3 episodes
• Generalization — 20× in this paper · explained in 13 episodes
• Transferability — 19× in this paper · explained in 4 episodes
• Foundation Models — 18× in this paper · explained in 7 episodes
• Accuracy — 12× in this paper · explained in 1 episode
• Adaptation — 12× in this paper · explained in 1 episode
• Benchmark — 10× in this paper · explained in 2 episodes
• Fine-Tuning — 9× in this paper · explained in 5 episodes
• Distribution Shift — 7× in this paper · explained in 6 episodes
• Embeddings — 5× in this paper · explained in 2 episodes
• Benchmarking — 3× in this paper · explained in 3 episodes
• Downstream Tasks — 3× in this paper · explained in 2 episodes
• Generative Model — 3× in this paper · explained in 2 episodes
• Generative Models — 3× in this paper · explained in 2 episodes
• Transfer Learning — 3× in this paper · explained in 10 episodes
• Transformer — 3× in this paper · explained in 2 episodes
• Robustness — 2× in this paper · explained in 5 episodes
• Simplified Molecular Input Line Entry System — 2× in this paper
• Agentic System — 1× in this paper · explained in 2 episodes
• Contrastive Language-Assay Molecule Pre-Training — 1× in this paper
• Core — 1× in this paper · explained in 2 episodes
• Critical Assessment of Structure Prediction — 1× in this paper
• Density Functional Theory (DFT) — 1× in this paper · explained in 2 episodes
• GNNs — 1× in this paper · explained in 2 episodes
• Steering — 1× in this paper · explained in 2 episodes
• Uncertainty Quantification — 1× in this paper · explained in 8 episodes
• World Model — 1× in this paper · explained in 7 episodes

Sources

• On the Opportunities and Risks of Foundation Models
• Scaling Laws for Neural Language Models
• Defining Foundation Models for Computational Science: A Call for Clarity and Rigor
• HELM-BERT: A Transformer for Medium-sized Peptide Property Prediction
• ChemBERTa-2: Towards Chemical Foundation Models
• Deep Learning Foundation Models from Classical Molecular Descriptors
• Mol-JEPA: A multimodal Joint Embedding Predictive Architecture for Molecules
• On the Scalability of GNNs for Molecular Graphs
• $\texttt{MiniMol}$: A Parameter-Efficient Foundation Model for Molecular Learning
• MatterSim: A Deep Learning Atomistic Model Across Elements, Temperatures and Pressures
• ESM All-Atom: Multi-scale Protein Language Model for Unified Molecular Modeling
• Nature Language Model: Deciphering the Language of Nature for Scientific Discovery
• UniGenX: a unified generative foundation model that couples sequence, structure and function to accelerate scientific design across proteins, molecules and materials
• A Molecular Multimodal Foundation Model Associating Molecule Graphs with Natural Language
• Benchmarking Pretrained Molecular Embedding Models For Molecular Representation Learning
• Budget-Sensitive Discovery Scoring: A Formally Verified Framework for Evaluating AI-Guided Scientific Selection
• Molecular Fingerprints Are Strong Models for Peptide Function Prediction
• The Open Molecules 2025 (OMol25) Dataset, Evaluations, and Models
• On the Reliability of AI Methods in Drug Discovery: Evaluation of Boltz-2 for Structure and Binding Affinity Prediction
• Generalization without systematicity: On the compositional skills of sequence-to-sequence recurrent networks

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