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

Understand

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.