DeepFMPO v3D

Code accompanying the paper "On the value of using 3D-shape and electrostatic similarities in deep generative methods". The paper can be found at https://doi.org/10.33774/chemrxiv-2021-sqvv9

Instructions

To run the code on the default dataset use the command:

python Main.py

To use other sets of molecules (one of molecules that will be optimized and one of molecules that will be fragmented), use the command:

python Main.py fragment.smi lead.smi

In both cases several parameters can be set in the global_parameters.py file. The most important parameter is the type of charges to be used: gasteiger, mmff or psi4. If psi4 charges are used, methodPsi4 and basisPsi4 must also be set. Please note that using psi4 charges the calculation requires a lot of time, so this option should be used only with small datasets.

To visualize the generated molecules use the script Show_Epoch_new.py, with the command:

python Show_Epoch_new.py

To save the generated fragments in an sdf file use the script decoding_to_sdf.py, with the command:

python decoding_to_sdf.py

The fragments are reported in the same way in which they are present in the similarity tree.

Requirements

The program is written in Python 3.7 using the following Python libraries:

• rdkit
• scipy
• resp
• psi4
• dask
• ESP-Sim
• numpy
• sklearn
• keras
• pandas
• bisect
• Levenshtein
• A backend to keras, such as theano, tensorflow or CNTK

Please note that the ESP-sim can be obtained from the "Comparison of electrostatic potential and shape" GitHub repo: https://github.com/hesther/espsim