A graph-based genetic algorithm and generative model/Monte Carlo tree search for the exploration of chemical space 论文
2019Chemical Science引用 347顶会
Computational Drug Discovery MethodsMachine Learning in Materials ScienceAnalytical Chemistry and Chromatography
摘要
, 972-976) using a recurrent neural network (RNN) generative model, and the GB-GM-based method is several orders of magnitude faster. The MCTS results seem more dependent on the composition of the training set than the GA approach for this particular property. Our results suggest that the performance of new ML-based generative models should be compared to that of more traditional, and often simpler, approaches such a GA.