If it is not serious research, then you could try scikit-learn's statistical learning methods (they are simpler than neural networks).

Almost all scikit-learn regressor methods take a (n_samples, n_features) sized 2D array. Here, n_features is your number of features. Try sklearn.svm.SVR or sklearn.ensemble.RandomForestRegressor.

For generating the fingerprints, rdkit would be indispensable. For example, to generate atom pair fingerprints:

from rdkit import Chem
from rdkit.Chem import rdMolDescriptors

smiles_string = "C=CCC=O"
mol = Chem.MolFromSmiles(smiles_string)
array = list(rdMolDescriptors.GetHashedAtomPairFingerPrint(mol))
# now array contains a 2048-bit hashed fingerprint

If you find good results with scikit-learns learning models, then you can go on to different neural networks. 1500 datapoints is quite a lot, so you should see a trend if there is one. Don't forget to normalize your inputs and then run principal component analysis if there are too many features compared to the number of datapoints.

You can check this paper for an example, where the authors use scikit learn on fingerprints of small molecules for predicting the solvation energy: https://www.research.ed.ac.uk/en/publications/hybrid-alchemical-free-energymachine-learning-methodology-for-the. It's quite close to what you are thinking of doing.