metadata
license: mit
language:
- en
tags:
- pharmacore
- sparse
- drug-discovery
- apple-silicon
- chemberta
- molecular-language-model
- cheminformatics
- smiles
- pruning
- efficient-inference
library_name: transformers
pipeline_tag: feature-extraction
base_model: seyonec/ChemBERTa-zinc-base-v1
model-index:
- name: chemberta-zinc-sparse50
results:
- task:
type: feature-extraction
name: Molecular Embedding
metrics:
- type: cosine_similarity
value: 0.973
name: Quality Retention vs Dense
ChemBERTa-zinc Sparse 50% — PharmaCore
A 50% magnitude-pruned version of seyonec/ChemBERTa-zinc-base-v1 optimized for efficient molecular encoding on Apple Silicon.
Why This Model?
| Metric | Dense (Original) | Sparse (This) | Improvement |
|---|---|---|---|
| Parameters (active) | 44.1M | 22M | 50% reduction |
| Inference (M4 MPS) | 5.1ms | 4.9ms | 4% faster |
| Quality Retention | 100% | 97.3% | Minimal loss |
Use Case
Molecular encoder in the PharmaCore drug discovery pipeline:
- Encode SMILES strings into dense embeddings for drug-target scoring
- Molecular similarity computation for drug repurposing
- Drug-likeness assessment and ADMET property prediction
- Runs entirely on consumer Apple Silicon hardware (M1/M2/M3/M4)
Usage
from transformers import AutoModel, AutoTokenizer
import torch
model = AutoModel.from_pretrained("stephenjun8192/chemberta-zinc-sparse50")
tokenizer = AutoTokenizer.from_pretrained("seyonec/ChemBERTa-zinc-base-v1")
# Encode a drug molecule (Erlotinib — EGFR inhibitor)
smiles = "COCCOc1cc2ncnc(Nc3cccc(C#C)c3)c2cc1OCCOC"
inputs = tokenizer(smiles, return_tensors="pt", padding=True, truncation=True)
with torch.no_grad():
outputs = model(**inputs)
embedding = outputs.last_hidden_state.mean(dim=1) # [1, 768]
print(f"Embedding shape: {embedding.shape}")
Sparsification Method
- Technique: Global magnitude pruning (unstructured)
- Sparsity: 50% of all weight parameters set to zero
- Layers pruned: All linear layers (attention Q/K/V/O, FFN)
- Validation: Cosine similarity of embeddings vs dense model ≥ 0.973
- Training data: Pre-trained on 100K ZINC molecules (SMILES)
Benchmarks (Apple M4 Mac mini, 16GB)
| Task | Time |
|---|---|
| Single molecule embedding | 4.9ms |
| Batch of 12 molecules | ~45ms |
| Molecular fingerprint + embedding | ~6ms |
| Drug repurposing (full screen) | ~18s |
Part of PharmaCore
PharmaCore — the first AI drug discovery platform that runs entirely on a MacBook. No cloud GPUs, no API keys, no data leaves your machine.
Citation
@software{pharmacore2026,
title={PharmaCore: Apple Silicon-Native AI Drug Discovery},
author={Stephen Wu},
year={2026},
url={https://github.com/reacherwu/PharmaCore}
}