Implementing a variety of complementary metrics for molecular complexity and synthetic accessibility.

A collaboration with the Sarpong group to understand complexity of molecules

Requirements

• numpy, pandas
• rdkit
• openbabel
• mordred
• SYBA (conda install -c lich syba)

Set up conda environment directly using the yml file:

To install the required packages through Conda, use the env.yml file as follows and the activate the environment:

1. conda env create -f env.yml
2. conda activate mc This will set up the environment with molcomplex installed.

For installation by cloning the GitHub folder, perform the follwoing steps:

1. Download the zipped folder or clone using: git clone https://github.com/patonlab/molcomplex.git
2. Navigate to the installed folder and run: python setup.py install. This will install molcomplex in the environment you are present in.
3. Install necessary dependencies using the following: conda install -c lich syba, conda install -c conda-forge rdkit, and conda install -c conda-forge openbabel

Recommended installation and update guide

In a nutshell, molcomplex and its dependencies are installed/updated as follows:

pip install molcomplex

conda install -c conda-forge openbabel rdkit Mordred

conda install lich::syba

conda install numpy pandas

Usage

To display the options type:

python -m molcomplex -h

The molcomplex package can be utilised as follows to obtain a csv with complexity scores.

python -m molcomplex -f examples/test.txt

To write to CSV add in the following:

python -m molcomplex -f examples/test.txt --csv

To perform a retro analysis by breaking down bonds to get complexity scores for precursors of the input SMILES add the following option:

python -m molcomplex -f examples/test.txt --csv --retro

Usage APP

To run the web app perform the following steps:

1. Navigate to the webapp folder: cd mcwebapp
2. Run the app as follows: python molcomplexapp.py
3. copy paste the http://127.0.0.1:8050/ or similar into web browser to utilise as an app.

Metrics implemented

• Bertz Complexity (CT) Score (JACS 1981, 103, 3241-3243)
• Balaban J Score (Chem. Phys. Lett. 1982, 89, 399-404)
• Coley SCScore (J. Chem. Inf. Model. 2018, 58, 2, 252)
• IPC: Bonchev & Trinajstic's information content of the coefficients of the characteristic polynomial of the adjacency matrix of a hydrogen-suppressed graph of a molecule (J. Chem. Phys. 1977, 67, 4517-4533)
• Ertl SA_Score (J. Cheminform. 2009, 1, 8)
• Boettcher Score (J. Chem. Inf. Model. 2016, 56, 3, 462–470)
• Rücker's total walk count (twc) index: Rücker, G.; Rücker, C. Counts of All Walks as Atomic and Molecular Descriptors. (J. Chem. Inf. Comput. Sci. 1993, 33, 683-695)
• Proudfoot's Cm index based on atom environments: Proudfoot, J. R. A path based approach to assessing molecular complexity. Bioorganic Med. Chem. Lett. 27, 2014–2017 (2017)
• Kappa Shape Indices 1, 2 & 3 (Quant. Struct. Act. Relat. 1986, 5, 1-7)
• McGowan Volume (Chromatographia, 1987, 23, 243-246)
• Labute Approximate Surface Area (Methods Mol Biol 2004, 275, 261-78)
• Van der Waals Volume Atomic and Bond Contributions (J. Org. Chem. 2003, 68, 7368-7373).
• Zagreb Index
• MOE Type Desciptors (Labute ASA, PEOE VSA, SMR VSA, SLogP VSA)
• SYBA Score (J. Cheminformatics 2020, 12, 35)
• Multiple additional 2D metrics.

Metrics to implement:

• Bertz’s Ns and Nt index: Bertz, S. H. & Sommer, T. J. Rigorous mathematical approaches to strategic bonds and synthetic analysis based on conceptually simple new complexity indices. Chem. Commun. 16, 2409–2410 (1997).

• Randić's zeta index: Randić, M. & Plavšić, D. Characterization of molecular complexity. Int. J. Quantum Chem. 91, 20–31 (2002).

• https://www.nature.com/articles/s41598-018-37253-8

Two noteworthy substructure-based methods are:

• Barone, R. & Chanon, M. A new and simple approach to chemical complexity. Application to the synthesis of natural products. J. Chem. Inf. Comput. Sci. 41, 269–272 (2001).

• Whitlock, H. W. On the structure of total synthesis of complex natural products. J. Org. Chem. 63, 7982–7989 (1998).

Citation:

Molecular Complexity-Inspired Synthetic Strategies Toward the Calyciphylline A-type Daphniphyllum Alkaloids Himalensine A and Daphenylline. Wright, B. A.; Okada, T.; Regni, A.; Luchini, G.; Sowndarya, S. V. S.; Chaisan, N.; Kölbl, S.; Kim, S. F.; Paton, R. S.; Sarpong, R. S. submmitted 2024

Acknowledgment:

This material is based upon work supported by the U.S. National Science Foundation under the NSF Center for Computer Assisted Synthesis (C-CAS), grant number CHE–2202693.