QM
Quantum Mechanics
Task Overview
The motion of molecules and protein targets can be described accurately with quantum theory, i.e., Quantum Mechanics (QM). However, ab initio quantum calculation of many-body system suffers from large computational overhead that is impractical for most applications. Various approximations have been applied to solve energy from electronic structure but all of them have a trade-off between accuracy and computational speed. Machine learning models raise a hope to break this bottleneck by leveraging the knowledge of existing chemical data. This task aims to predict the QM results given a drug's structural information.
Impact
A well-trained model can describe the potential energy surface accurately and quickly, so that more accurate and longer simulation of molecular systems are possible. The result of simulation can reveal the biological processes in molecular level and help study the function of protein targets and drug molecules.
Generalization
A machine learning model trained on a set of QM calculations require to extrapolate to unseen or structurally diverse set of compounds.
Product
Small-molecule.
Pipeline Stage
Activity - lead development.
Usage Example
You can access these datasets using the PyTDC library:
from tdc_ml.single_pred import QM
# Load a dataset
data = QM(name='QM7b')
# Access the data
df = data.get_data()
print(df.head())