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AI-powered toxicity prediction to accelerate safer drug discovery.
Our XGBoost pipeline predicts pIC50 by fusing 2048-bit Morgan Fingerprints with ProtTrans embeddings. Protein-agnostic and robust on "cold" splits, it turns libraries into actionable leads.
Challenge 3: Detecting Alzheimer's disease from EEG, a step to early detection to Alzheimer's!
Pocket Profit: Our Solution to Challenge two, using a two tower neural network architecture.
ToxiQ predicts the acute oral toxicity (log LD50) of any chemical compound from its SMILES notation. It returns the predicted value, estimated mg/kg dose, GHS hazard classification (Cat 1–6)....
Predict compound-target binding affinity with machine learning
Challenge 1
We built an EEG-based machine learning system that detects Alzheimer’s disease using clinically grounded brain signal features. Our model leverages biomarkers to achieve strong balanced accuracy
Detect Alzheimer's early!
Drug discovery is notoriously slow and resource-heavy. We leverage machine learning and Python to predict binding affinity, bypassing traditional biochemical barriers to radically accelerate screening
Using deep learning to detect Alzheimer’s from EEG brain signals.
Predicting LD50 based on features from SMILES nomenclature
Accurately classifies non-invasive brain recordings into diseased and healthy states.
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