“用深度神经网络增强遗传算法探索化学空间”的可重复性研究
A reproducibility study of "Augmenting Genetic Algorithms with Deep Neural Networks for Exploring the Chemical Space"
摘要：
Nigam等人报道了一种利用SELFIES表示的遗传算法（GA），并且还提出了一种自适应的、基于神经网络的惩罚，该惩罚被认为可以提高所生成分子的多样性。该论文的主要主张是，这种GA优于其他生成技术（由惩罚logP测量），并且基于神经网络的自适应惩罚增加了生成分子的多样性。在这项工作中，我们调查了他们的主张的可重复性。总的来说，我们能够使用基于SELFIES的GA再现可比较的结果，但主要是通过利用（容易优化的）适应度函数的缺陷（即，产生长的含硫链）。此外，我们重现的结果表明，该方法可用于将分子的生成偏向于与参考集相似的分子。最后，我们尝试量化多样性的演变，了解一些超参数的影响，并提出改进的自适应惩罚。
英文摘要：
Nigam et al. reported a genetic algorithm (GA) utilizing the SELFIES representation and also propose an adaptive, neural network-based penalty that is supposed to improve the diversity of the generated molecules. The main claims of the paper are that this GA outperforms other generative techniques (as measured by the penalized logP) and that a neural network-based adaptive penalty increases the diversity of the generated molecules. In this work, we investigated the reproducibility of their claims. Overall, we were able to reproduce comparable results using the SELFIES-based GA, but mostly by exploiting deficiencies of the (easily optimizable) fitness function (i.e., generating long, sulfur containing chains). In addition, we reproduce results showing that the discriminator can be used to bias the generation of molecules to ones that are similar to the reference set. Lastly, we attempted to quantify the evolution of the diversity, understand the influence of some hyperparameters, and propose improvements to the adaptive penalty.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
