From reading your comments, and the question, you want to generate optimized structures of small bioactive molecules in solvents, but it has to be fast. I would definitely recommend using semi-empirical methods, because that would give you the best of both worlds. The calculation will be faster than DFT, and you can use those methods with relatively accurate implicit solvation models.
Here I have covered three softwares, with several semi-empirical methods, and one force-field based method:
MOPAC has loads of semi-empirical methods, prominent among them being PM3 (old), PM6 (newer), PM6-DH+ (empirical dispersion and hydrogen bonding) etc. MOPAC uses the COSMO model for implicit solvation. In the input file, you have to put the keyword
EPS=78.6 to specify the dielectric constant of the solvent (default 78.6 for water). The MOPAC manual recommends that you use EF algorithm for optimizing with solvent, instead of the default L-BFGS.
As a test, I optimized an esomeprazole molecule (sdf file obtained from PubChem) on my laptop (Intel core i3, 2.2 GHz 4 core) with PM6/water. It took 8.53 seconds for the optimization to finish (multi-threaded run).
GAMESS can use PM3, AM1 and RM1 semi-empirical methods officially. However, PM6 and PM6-D3H+ (D3 dispersion and hydrogen bond corrected) methods are also implemented for elements until fluorine (unofficially). All the semi-empirical methods are interfaced with SMD solvation model (which is more accurate than COSMO as it also includes non-electrostatic contributions to solvation). See this paper for details of the implementation. For example, with PM3/SMD the RMS error for solvation energy is 2.8 kcal/mol (neutral solute), so the method is quite accurate. To use this you have to specify
$BASIS GBASIS=PM3 $END and
$PCM SOLVNT=WATER SMD=.T. $END. For the other solvents, use
I redid the same optimization of esomeprazole on GAMESS and it took 3.8 minutes on 4 cores. This is probably because the implementation of semi-empirical methods in GAMESS is slightly slower itself, also because GAMESS uses different criteria for convergence of optimization and SMD is slightly more demanding.
XTB is uses a semi-empirical tight-binding approach. There are three parameterizations—GFN0-xTB, GFN1-xTB, GFN2-xTB, each newer than the last. XTB is open-source, so you will have to compile it. It has analytical linearized Poisson-Boltzmann(alpb) implicit solvation. According to the official manual, DMSO and water are available, ethanol is not mentioned. However, from my test, the latest version seems to be able to run with ethanol solvation as well.
XTB is run from command line:
xtb.exe --alpb water --opt -P 4 --gfn 2 esomeprazole.sdf > out.log
This uses GFN2 parameters with water solvation and 4 cores. The calculation took 7.346 second on my laptop.
For the other solvents, use
--alpb dmso and
The XTB software also contains a polarizable force field GFN-FF, which can also be run with implicit solvation. I am not sure how accurate it is, but the optimization of esomeprazole takes 0.445 seconds, a significant gain in speed. To use the force field, you have to use
--gfnff instead of
--gfn 2 in the command line.
Running with Python
I have limited knowledge of Python, so I can't help much here. For MOPAC and GAMESS, you can use the python package ASE. It is supposed to be able to interface to the softwares directly. ASE can also write input files, so use that if the interface does not work. As XTB is used from the command-line and can read sdf files, you can directly use
os.subprocess.run(). XTB automatically outputs the optimized geometry in the same format as input (.sdf here).
Not all semi-empirical methods are well-parallelized. MOPAC and XTB uses thread-based parallelization, whereas GAMESS uses an MPI-based one. I assume you are using a cluster for your calculations. It would probably be the most efficient if you ran each calculation on one core, and parallelized the handling of different molecules. You could use something like
mpi4py for this. Also check if the software you are using allows multiple instances to be launched at the same time. On my laptop, XTB can do this, but I have not tested GAMESS or MOPAC.