ESE-GB-DNN – A dense Neural Network for evaluation of solvation free energies based on Generalized-Born terms
ESE-GB-DNN is a method for evaluation of solvation free energies of molecules and ions both in aqueous and non-aqueous solutions [2]. It requires the solute molecular geometry and the total charge only. To obtain the solvation free energy, first electronegativity-equalization atomic charges are calculated. Subsequently, the Born-type terms, atomic surfaces and volumes are evaluated and, along with three solvent features, are fed into a Dense Neural Network that eventually yields the solvation free energy.
The present ESE-GB-DNN scheme is more efficient than ESE-EE-DNN [3], since no explicit molecular surface needs to be constructed. It is inspired by the ESE-EE scheme [4], which is in turn based on the uESE [6] and xESE [7] that we developed jointly with Alexander Voityuk.
The supported elements are H, C, N, O, F, Si, P, S, Cl, Br, I.
The ESE-GB-DNN solvation free energy can be calculated by the program ESE-GB-DNN, which can be downloaded here free of charge:
ESE-GB-DNN.exe – Windows version
ESE-GB-DNN.x – Linux version.The ESE-GB-DNN program can be run from the command line as follows:
ESE-GB-DNN.exe xyz-file -charge charge -solvent solvent
If your solvent is not in this list, you can use the following call format:
ESE-GB-DNN.exe xyz-file -charge charge -Eps dielectric_constant -BP boiling_point_°C -Nheavy number_of_non_hydrogen_atoms_in_solvent
Warning: The xyz-file should contain atomic symbols (or numbers) and Cartesian coordinates (in Å) and an empty line at the end. It should not contain any header.
Once you use results calculated by the ESE-GB-DNN program, you should include at least the following citations:
1. S. F. Vyboishchikov, ESE-GB-DNN program, Girona, 2023
2. S. F. Vyboishchikov, J. Chem. Theory Comput., 2023, 19, 8340–8350. DOI: 10.1021/acs.jctc.3c00858
and preferably also cite our previous related work:
3. S. F. Vyboishchikov, J. Chem. Inf. Model., 2023, 63, 6283–6292. DOI: 10.1021/acs.jcim.3c00922
4. S. F. Vyboishchikov, J. Comput. Chem., 2023, 44, 307–318. DOI: 10.1002/jcc.26894
5. S. F. Vyboishchikov, A. A. Voityuk, Chemical Reactivity, vol. 2: Approaches and applications, S. Kaya, L. von Szentpály, G. Serdaroğlu, K. Guo (Eds.), Elsevier, Amsterdam, 2023, 399–427. DOI: 10.1016/B978-0-32-390259-5.00021-4
6. S. F. Vyboishchikov, A. A. Voityuk, J. Chem. Inf. Model., 2021, 61, 4544–4553. DOI: 10.1021/acs.jcim.1c00885
7. S. F. Vyboishchikov, A. A. Voityuk, J. Comput. Chem., 2021, 42, 1184–1194. DOI: 10.1002/jcc.26531
8. A. A. Voityuk, S. F. Vyboishchikov, Phys. Chem. Chem. Phys. 2020, 22, 14591–14598. DOI: 10.1039/d0cp02667k
9. A. A. Voityuk, S. F. Vyboishchikov, Phys. Chem. Chem. Phys., 2019, 21, 875–874. DOI: 10.1039/c9cp03010g
Questions related to the ESE-GB-DNN method and program should be addressed to Sergei Vyboishchikov.