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MoLFormer - Drug Discovery & Chemistry | aibioatlas | aibioatlas

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MoLFormer Scientific Architecture

MoLFormer Scientific Architecture · IBM Research

Drug Discovery & Chemistry · Molecular Foundation Models

MoLFormer

Di IBM Research →

Large-scale chemical language foundation model for molecular property prediction

MoLFormer is a chemical foundation model trained on 1.1 billion molecules with linear attention transformers, achieving state-of-the-art accuracy in quantum chemical properties and ADMET prediction.

Trained on 1.1 billion SMILES strings from PubChem and ZINC databasesLinear attention mechanism enabling efficient scaling to large molecular graphsState-of-the-art benchmarks on MoleculeNet across physical chemistry and biophysics tasksFine-tuning support for custom pharmaceutical assay endpoints

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Panoramica

MoLFormer is a chemical foundation model trained on 1.1 billion molecules with linear attention transformers, achieving state-of-the-art accuracy in quantum chemical properties and ADMET prediction.

Informazioni verificate rispetto a una fonte ufficiale; non un test pratico. Fonte · Ultima revisione: 20/09/2026, 11:09:40 UTC

Caratteristiche principali

Trained on 1.1 billion SMILES strings from PubChem and ZINC databases
Linear attention mechanism enabling efficient scaling to large molecular graphs
State-of-the-art benchmarks on MoleculeNet across physical chemistry and biophysics tasks
Fine-tuning support for custom pharmaceutical assay endpoints

Interactive 3D Illustration

Biological Architecture (Stylized Ribbon)

Default view is a stylized demonstration ribbon. Enter any 4-letter RCSB PDB accession code above to stream real atomic coordinates.

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Illustrative Confidence Gradient:

>90 High / Low B 70-90 Confident 50-70 Low <50 Very low / High B

Academic Context & Research Evidence

Biological & Workflow Fit

Biological Application: Solubility, toxicity (hERG, Ames), BBB permeability, and binding affinity prediction
Research Workflow: SMILES input -> high-dimensional latent molecular embeddings -> downstream property predictions
Compute & Hardware: 1x GPU or CPU for inference; multi-GPU for fine-tuning
Licensing & Academic Use: MIT License
Documented Evidence: View validation publication / source ↗

Cite this Tool

Use this citation format when referencing MoLFormer in scientific publications and benchmark papers.

@software{molformer_2026,
  title = {{MoLFormer}},
  author = {{IBM Research}},
  year = {2026},
  url = {https://github.com/IBM/molformer},
  note = {Indexed on aibioatlas - AI for Biology and Drug Discovery}
}

Peer-Reviewed Literature & Preprints

Live scientific citations queried from Europe PMC and PubMed for MoLFormer.

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Informazioni tecniche / prodotto

Missing values mean the catalog has no recorded information. They do not mean a feature is absent.

Entry typeAI Model

Access modeOpen Source

AI roleChemical Foundation Model

Input dataNot recorded

Output dataNot recorded

Licence conditionsMIT License

Commercial eligibilityOpen source release

Compute requirements1x GPU or CPU for inference; multi-GPU for fine-tuning

ValidationNot recorded

TypeChemical transformer foundation model

Intended useNot recorded

CompatibilityNot recorded

ManufacturerIBM Research

Biological applicationSolubility, toxicity (hERG, Ames), BBB permeability, and binding affinity prediction

Research workflowSMILES input -> high-dimensional latent molecular embeddings -> downstream property predictions

Evidence levelPeer-reviewed research (Nature Machine Intelligence 2022)

Integration evidencehttps://github.com/IBM/molformer

Laboratory handoffFilters high-risk toxic molecules before compound synthesis

AvailabilityAvailable on Hugging Face and GitHub

Price / accessFree Open Source

Research fit & compatibility

No software–hardware integration has been verified for this entry yet. Explore documented research workflows.

Gallery & resources

MoLFormer Scientific Architecture

MoLFormer Scientific Architecture · Credit: IBM Research · Open original ↗

Research and editorial use Source & attribution ↗ License ↗

Resource: IBM MoLFormer Repository ↗ · IBM Research

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MOLECULAR DOCKING • SMILES GRAPH

ADMET-AI

AI Drug Discovery

Research AI Methods

ADMET-AI

Source-scoped research method · Confirm distribution and exact release

ADMET-AI — admet-property-prediction

Research / method authors

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MOLECULAR DOCKING • SMILES GRAPH

ChemFM

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ChemFM

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Chemical foundation model for learned molecular representations, property prediction and conditional mole

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MOLECULAR DOCKING • SMILES GRAPH

DiffPharma

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DiffPharma

Research AI model / method · Research code or publication

Diffusion-based ligand molecule generation constrained by specified pocket and pharmacophore interactions

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MOLECULAR DOCKING • SMILES GRAPH

DiffSBDD

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DiffSBDD

Source-scoped research method · Confirm distribution and exact release

DiffSBDD — structure-conditioned-molecular-generation

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Dove è disponibile questo prodotto?

Available on Hugging Face and GitHub

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