Inverse Design of Substituted BODIPY

We provide python codes to inverse design BODIPY molecules, as discussed in Ref-1.

DesignBodipy_Bayes.py can be used to design molecules using Bayesian optimization based on Gaussian process regression.

DesignBodipy_GA.py can be used for genetic algoritm (GA) optimization

Both programs use a trained kernel ridge regression machine learning (KRR-ML) model to evaluate the S₀→S₁ excitation energy.

Download the design code and SLATM descriptors for 77k training BODIPY molecules

git clone https://github.com/moldis-group/bodipy.git
cd bodipy/data
wget http://moldis.tifrh.res.in/bodipy/desc.npy
cd ..

Example run: Bayesian Optimization

$ python3 DesignBodipy_Bayes.py 2.7

Additional parameters can be obtained using python3 DesignBodipy_Bayes.py --help

Screenshot of output

Searching for 2D BODIPY near 2.700000 eV
Reading ML model from ./data
Iterations 200; Initial evaluations 5
Bayesian opt. parameters:
 Exploration/Exploitation param: 0.010000; Eval. per EI: 5
=================================================================
ITER    POS            GROUPS             S0S1(eV)        Target
=================================================================
0       1 6             28 27           3.337201        2.700000
1       3 5             29 30           3.184931        2.700000
2       5 6             27 22           3.183506        2.700000
13      4 5             30 25           2.999981        2.700000
18      5 7             23 19           2.952890        2.700000
38      2 5             15 25           2.866237        2.700000
83      5 4             34 6            2.709659        2.700000
=================================================================

Example run: GA Optimization

$ python3 DesignBodipy_GA.py 2.7

Additional parameters can be obtained using python3 DesignBodipy_GA.py --help

Screenshot of output

Searching for 7D BODIPY near 2.700000 eV
Reading ML model from ./data
Generations 200; Parent population 20
Starting Genetic optization
Starting population estimation
Calculating parent: 20   
Current Gen 0, Median: 2.985662  Best: 2.767993
Best Groups [19.0, 18.0, 20.0, 34.0, 45.0, 7.0, 30.0] ; Pos [0, 1, 2, 3, 4, 5, 6]
Evaluating: 10 of 10   
Current Gen 1, Median: 2.926014  Best: 2.767993
Best Groups [19.0, 18.0, 20.0, 34.0, 45.0, 7.0, 30.0] ; Pos [0, 1, 2, 3, 4, 5, 6]
Evaluating: 10 of 10   
Current Gen 2, Median: 2.885984  Best: 2.708469
Best Groups [19.0, 40.0, 14.0, 43.0, 23.0, 7.0, 11.0] ; Pos [0, 1, 2, 3, 4, 5, 6]
Evaluating: 10 of 10   
Current Gen 3, Median: 2.859153  Best: 2.708469
Best Groups [19.0, 40.0, 14.0, 43.0, 23.0, 7.0, 11.0] ; Pos [0, 1, 2, 3, 4, 5, 6]
...

Requirements:

1. Python3.6 [tested]
2. Numpy
3. Scipy (scipy.optimize.minimize for iter minimization)
4. Scikit-learn (for Gaussian Process)
5. MOPAC for calculating minimum energy geometry at the PM7 level
6. OBabel for file conversion
7. QML for calculating the SLATM descriptor using the PM7 geometry

Relevant resources

A publicly accessible web interface hosting a trained machine learning (ML) model to predict S₀ → S₁ excitation energy of BODIPYs is available at https://moldis.tifrh.res.in/db/bodipy.

Reference:

[1] Data-Driven Modeling of S0 -> S1 Transition in the Chemical Space of BODIPYs: High-Throughput Computation, Machine Learning Modeling and Inverse Design
Amit Gupta, Sabyasachi Chakraborty, Debashree Ghosh, Raghunathan Ramakrishnan
The Journal of Chemical Physics, 155 (2021) 244102.
DOI: https://doi.org/10.1063/5.0076787
Dataset: https://moldis-group.github.io/BODIPYs/
Dataset DOI: 10.6084/m9.figshare.16529214.v1