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Title: Augmenting Genetic Algorithms with Deep Neural Networks for Exploring the Chemical Space

Paper content:
Challenges in natural sciences can often be phrased as optimization problems.
Machine learning techniques have recently been applied to solve such problems.
One example in chemistry is the design of tailor-made organic materials and molecules, which requires efficient methods to explore the chemical space.
We present a genetic algorithm (GA) that is enhanced with a neural network (DNN) based discriminator model to improve the diversity of generated molecules and at the same time steer the GA.
We show that our algorithm outperforms other generative models in optimization tasks.
We furthermore present a way to increase interpretability of genetic algorithms, which helped us to derive design principles
The design of optimal structures under constraints is an important problem spanning multiple domains in the physical sciences.
Specifically, in chemistry, the design of tailor-made organic materials and molecules requires efficient methods to explore the chemical space.
Purely experimental approaches are often time consuming and expensive.
Reliable computational tools can accelerate and guide experimental efforts to find new materials faster.
We present a genetic algorithm (GA) (Davis, 1991; Devillers, 1996; Sheridan & Kearsley, 1995; Parrill, 1996) for molecular design that is enhanced with two features:
We presented a hybrid GA and ML-based generative model and demonstrated its application in molecular design.
The model outperforms literature approaches in generating molecules with desired properties.
A detailed analysis of the data generated by the genetic algorithm allowed us to interpret the model and learn rules for the design of high performing molecules.
This human expert design inspired from GA molecules outperformed all molecules created by generative models.
For computationally more expensive property evaluations, we will extend our approach by the introduction of an on-the-fly trained ML property evaluation method, which will open new ways of solving the inverse design challenge in chemistry and materials sciences.
Our approach is independent of domain knowledge, thus applicable to design questions in other scientific disciplines beyond chemistry.
We therefore plan to generalize the GA-D approach to make it a more general concept of generative modelling.
6 SUPPLEMENTARY INFORMATION Figure S1 shows examples of the molecules optimized in Section 4.4.
Figure S1 : Molecular modifications resulting in increased penalized logP scores under similarity constraint sim(m, m ) > 0.4, 0.6.
We show the molecules that resulted in largest score improvement.
Figures S2-S4 show comparisons between the property distributions observed in molecule data sets such as the ZINC and the GuacaMol data set and property distributions of molecules generated using random SELFIES ( Figure S2 ), GA generated molecules with the penalized logP objective ( Figure S3 ) and GA generated molecules with an objective function which includes logP and QED ( Figure S4 ).
While the average logP scores of average SELFIES are low, the tail of the distribution reaches to high values, explaining the surprisingly high penalized logP scores shown in Table 1 .
The QED and weight distributions of molecules optimized using the penalized logP objective significantly differ from the distributions of the ZINC and the GuacaMol data set (see Figure S3 ).
As soon as the QED score is simultaneously optimized, the distributions of GA generated molecules and molecules from the reference data sets become more similar (see Figure S4 ).
Figure 6 shows that the GA can simultaneously optimize logP and QED.
Figure S2 : Distributions of
a) logP,
b) SA,
c) QED and
d) molecular weight for randomly generated SELFIES, molecules from the ZINC data set and molecules from the GuacaMol data set.
Figure S3 : Distributions of
a) logP,
b) SA,
c) QED and
d) molecular weight for GA generated SELFIES (penalized logP objective function), molecules from the ZINC data set and molecules from the GuacaMol data set.
Figure S4 : Distributions of
a) logP,
b) SA,
c) QED and
d) molecular weight for GA generated SELFIES (logP and QED as objective function), molecules from the ZINC data set and molecules from the GuacaMol data set.

assistant: text: Tackling inverse design via genetic algorithms augmented with deep neural networks.