This github repository aims at providing user guide for the use of AQM dataset.
The dataset files can be downloaded from the ZENODO repository.
We have reported initial results demonstrating that a machine learning model trained to learn the difference between the values of properties in implicit water (P-sol) and those in the gas phase (P-gas) can predict solvated-phase properties with greater precision than a model trained directly on P-sol values. More information can be found in the ./DeltaNN/ folder.
The authors are grateful for financial support from the European Union’s Horizon 2020 research and innovation program under the Marie Sklodowska-Curie grant agreement No 956832, “Advanced Machine Learning for Innovative Drug Discovery” (AIDD).
If you use the dataset please cite
@article{aqm1,
title={Dataset for quantum-mechanical exploration of conformers and solvent effects in large drug-like molecules},
author={Medrano Sandonas, Leonardo and Van Rompaey, Dries and Fallani, Alessio and Hilfiker, Mathias and Hahn, David and Perez-Benito, Laura and Verhoeven, Jonas and Tresadern, Gary and Kurt Wegner, Joerg and Ceulemans, Hugo and others},
journal={Sci. Data},
volume={11},
number={1},
pages={742},
year={2024},
publisher={Nature Publishing Group UK London},
doi={10.1038/s41597-024-03521-8}
}
@InProceedings{aqm2,
author="Hilfiker, Mathias and Medrano Sandonas, Leonardo and Kl{\"a}hn, Marco and Engkvist, Ola and Tkatchenko, Alexandre",
editor="Clevert, Djork-Arn{\'e} and Wand, Michael and Malinovsk{\'a}, Krist{\'i}na and Schmidhuber, J{\"u}rgen and Tetko, Igor V.",
title="Leveraging Quantum Mechanical Properties to Predict Solvent Effects on Large Drug-Like Molecules",
booktitle="AI in Drug Discovery",
year="2025",
publisher="Springer Nature Switzerland",
address="Cham",
pages="47--57",
isbn="978-3-031-72381-0"
}