File size: 1,525 Bytes
63bdadc 5b5ee28 63bdadc 1bcb06b 3edbc93 |
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 |
import os
from huggingface_hub import HfApi
ASSAY_LIST = ["AC-SINS_pH7.4", "PR_CHO", "HIC", "Tm2", "Titer"]
ASSAY_RENAME = {
"AC-SINS_pH7.4": "Self-association",
"PR_CHO": "Polyreactivity",
"HIC": "Hydrophobicity",
"Tm2": "Thermostability",
"Titer": "Titer",
}
ASSAY_EMOJIS = {
"AC-SINS_pH7.4": "🧲",
"PR_CHO": "🎯",
"HIC": "💧",
"Tm2": "🌡️",
"Titer": "🧪",
}
TOKEN = os.environ.get("HF_TOKEN")
CACHE_PATH=os.getenv("HF_HOME", ".")
API = HfApi(token=TOKEN)
organization="ginkgo-datapoints"
submissions_repo = f'{organization}/abdev-bench-submissions'
results_repo = f'{organization}/abdev-bench-results'
ABOUT_TEXT = """
## About this challenge
We're inviting the ML/bio community to predict developability properties for 244 antibodies from the [GDPa1 dataset](https://huggingface.co/datasets/ginkgo-datapoints/GDPa1).
**What is antibody developability?**
Antibodies have to be manufacturable, stable in high concentrations, and have low off-target effects.
Properties such as these can often hinder the progression of an antibody to the clinic, and are collectively referred to as 'developability'.
Here we show 5 of these properties and invite the community to submit and develop better predictors, which will be tested out on a heldout private set to assess model generalization.
**How to submit?**
TODO
**How to evaluate?**
TODO
FAQs: A list of frequently asked questions.
"""
FAQS = {
"Example FAQ with dropdown": """Full answer to this question""",
} |