Many generative model techniques and architectures applied to de novo molecule generation exist. These models range from purely symbolic approaches such as genetic algorithms to more recent machine learning (ML) approaches such as recurrent neural networks (RNNs) , transformers , variational autoencoders , generative adversarial networks , graph neural networks and hybrid approaches that use ML to guide reinforcement learning (RL) in a heuristic action space . These generative models can produce valid and novel molecules and condition molecule generation towards a particular endpoint (e.g., predicted bioactivity towards a protein target ) via optimization techniques such as, RL , Bayesian optimization , molecular swarm optimization and Monte Carlo tree search . Although generative models still face many challenges for trusted and routine integration into drug discovery pipelines including practical relevance and more comprehensive evaluation .