An open-source hub for all your molecular featurizers

Discover an unparalleled diversity of molecular featurizers and deploy them directly in your machine learning workflows.

Get startedBrowse the Hub
import datamol as dm
from molfeat.calc import RDKitDescriptors2D


data = dm.data.freesolv().sample(500).smiles.values
mol2d = data[83]


calc = RDKitDescriptors2D()
calc(mol2d)

What is molfeat?

molfeat is an open-source hub that makes it easy for ML scientists to evaluate and implement a wide range of molecular featurizers. Find the right featurizer for your workflow today.

Roberta-Zinc480M-102M

This is a Roberta style masked language model trained on ~480m SMILES strings from the ZINC database. The model has ~102m parameters and was trained for 150000 iterations with a batch size of 4096 to a validation loss of ~0.122.

Updated on

GPT2-Zinc480M-87M

This is a GPT2 style autoregressive language model trained on ~480m SMILES strings from the ZINC database available. The model has ~87m parameters and was trained for 175000 iterations with a batch size of 3072 to a validation loss of ~.615.

Updated on

ChemGPT-1.2B

ChemGPT (1.2B params) is a transformer model for generative molecular modeling, which was pretrained on the PubChem10M dataset.

Updated on

ChemGPT-19M

ChemGPT (19M params) is a transformers model for generative molecular modeling, which was pretrained on the PubChem10M dataset.

Updated on

ecfp-count

The ECFP-Count (Extended Connectivity Fingerprints-Coun is essentially the same as the ECFP. However, instead of being hashed into a binary vector, there is no hashing process and simply a count vector is returned

Updated on

pcqm4mv2_graphormer_base

Pretrained Graph Transformer on PCQM4Mv2 Homo-Lumo energy gap prediction using 2D molecular graphs.

Updated on

See all featurizers...