Exploiting the short-sighted gradients should be balanced with adequate explorations.

Figure 1 (right), is the analog plot of Figure 1 (right), where the "random configuration" shows the statistics

This generates two (possibly invalid) child molecules. If valid molecules exist, the we choose one of them randomly. Details of seven different ways of modifying a molecule are as follows. The atom_addition connects a new atom to a single atom. The atom_insertion puts an atom between two atoms.

Moreover, Evolution Gym is easy to use.

The challenge of discovering new molecules with desired properties is crucial in domains like drug discovery and material design. Recent advances in deep learning-based generative methods have shown promise but face the issue of sample efficiency due to the computational expense of evaluating the reward function. This paper proposes a novel algorithm for sample-efficient molecular optimization by distilling a powerful genetic algorithm into deep generative policy using GFlowNets training, the off-policy method for amortized inference. This approach enables the deep generative policy to learn from domain knowledge, which has been explicitly integrated into the genetic algorithm. Our method achieves state-of-the-art performance in the official molecular optimization benchmark, significantly outperforming previous methods. It also demonstrates effectiveness in designing inhibitors against SARS-CoV-2 with substantially fewer reward calls.