Graph-Based Approaches for Predicting Solvation Energy in Multiple Solvents: Open Datasets and Machine Learning Models
| Autor: | Larry A. Curtiss, Logan Ward, Naveen Dandu, Rajeev S. Assary, Badri Narayanan, Ian Foster, Paul C. Redfern, Ben Blaiszik |
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| Rok vydání: | 2021 |
| Předmět: |
Quantum chemical
010304 chemical physics Artificial neural network Computer science business.industry String (computer science) Graph based Solvation 010402 general chemistry Machine learning computer.software_genre 01 natural sciences Quantum chemistry 0104 chemical sciences chemistry.chemical_compound chemistry 0103 physical sciences Molecule Molecular graph Density functional theory Artificial intelligence Physical and Theoretical Chemistry business computer |
| DOI: | 10.26434/chemrxiv-2021-5tf55 |
| Popis: | The solvation properties of molecules, often estimated using quantum chemical simulations, are important in the synthesis of energy storage materials, drugs, and industrial chemicals. Here, we develop machine learning models of solvation energies to replace expensive quantum chemistry calculations with inexpensive-to-compute message-passing neural network models that require only the molecular graph as inputs. Our models are trained on a new database of solvation energies for 130,258 molecules taken from the QM9 dataset computed in five solvents (acetone, ethanol, acetonitrile, dimethyl sulfoxide, and water) via an implicit solvent model. Our best model achieves a mean absolute error of 0.5 kcal/mol for molecules with nine or fewer non-hydrogen atoms and 1 kcal/mol for molecules with between 10 and 14 non-hydrogen atoms. We make the entire dataset of 651,290 computed entries openly available and provide simple web and programmatic interfaces to enable others to run our solvation energy model on new molecules. This model calculates the solvation energies for molecules using only the SMILES string and also provides an estimate of whether each molecule is within the domain of applicability of our model. We envision that the dataset and models will provide the functionality needed for the rapid screening of large chemical spaces to discover improved molecules for many applications. |
| Databáze: | OpenAIRE |
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