Push model using huggingface_hub.

README.md

@@ -3,16 +3,24 @@ base_model: ibm/biomed.sm.mv-te-84m
 library_name: SmallMoleculeMultiView
 license: apache-2.0
 tags:
+- binding-affinity-prediction
+- bio-medical
 - chemistry
+- drug-discovery
+- drug-target-interaction
 - model_hub_mixin
+- molecular-property-prediction
+- moleculenet
 - molecules
-- multiview
-- pytorch
+- multi-view
+- multimodal
 - pytorch_model_hub_mixin
+- small-molecules
+- virtual-screening
 ---
 
 # ibm/biomed.sm.mv-te-84m-MoleculeNet-ligand_scaffold-BBBP-101
-`biomed.sm.mv-te-84m` is a biomedical foundation model for small molecules created using **MMELON** (**M**ulti-view **M**olecular **E**mbedding with **L**ate Fusi**on**), a flexible approach to aggregate multiple views (sequence, image, graph) of molecules in a foundation model setting. While models based on single view representation typically performs well on some downstream tasks and not others, the multi-view model performs robustly across a wide range of property prediction tasks encompassing ligand-protein binding, molecular solubility, metabolism and toxicity. It has been applied to screen compounds against a large (> 100 targets) set of G Protein-Coupled receptors (GPCRs) to identify strong binders for 33 targets related to Alzheimer’s disease, which are validated through structure-based modeling and identification of key binding motifs [Multi-view biomedical foundation models for molecule-target and property prediction](https://arxiv.org/abs/2410.19704).
+`biomed.sm.mv-te-84m` is a multimodal biomedical foundation model for small molecules created using **MMELON** (**M**ulti-view **M**olecular **E**mbedding with **L**ate Fusi**on**), a flexible approach to aggregate multiple views (sequence, image, graph) of molecules in a foundation model setting. While models based on single view representation typically performs well on some downstream tasks and not others, the multi-view model performs robustly across a wide range of property prediction tasks encompassing ligand-protein binding, molecular solubility, metabolism and toxicity. It has been applied to screen compounds against a large (> 100 targets) set of G Protein-Coupled receptors (GPCRs) to identify strong binders for 33 targets related to Alzheimer’s disease, which are validated through structure-based modeling and identification of key binding motifs [Multi-view biomedical foundation models for molecule-target and property prediction](https://arxiv.org/abs/2410.19704).
 
 - **Developers:** IBM Research
 - **GitHub Repository:** [https://github.com/BiomedSciAI/biomed-multi-view](https://github.com/BiomedSciAI/biomed-multi-view)