QizhiPei/SSM-DTA
SSM-DTA: Breaking the Barriers of Data Scarcity in Drug-Target Affinity Prediction (Briefings in Bioinformatics 2023)
This project helps drug researchers and biochemists predict how strongly a drug compound will bind to a specific protein target. By taking chemical structures of drug molecules (SMILES strings) and protein sequences as input, it provides a predicted binding affinity score. This allows scientists to quickly screen potential drug candidates and understand their interactions with biological targets, especially when experimental data is scarce.
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Use this if you are a drug discovery scientist or biochemist who needs to predict drug-target binding affinities, particularly when you have limited experimental data for training.
Not ideal if you are looking for a general-purpose machine learning framework or if your primary interest is in predicting other biological interactions beyond drug-target affinity.
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55
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Language
Python
License
MIT
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Last pushed
May 28, 2024
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