# MolecularRNN: Generating realistic molecular graphs with optimized properties Source: [MolecularRNN: Generating realistic molecular graphs with optimized properties]() Collection index: [llms.txt]() Data updated: 2025-10-25T19:02:51.915Z Authors: Mariya Popova, Mykhailo Shvets, Junier Oliva, Olexandr Isayev Published: 2019-05-31T01:33:13.000Z ## Abstract Designing new molecules with a set of predefined properties is a core problem in modern drug discovery and development. There is a growing need for de-novo design methods that would address this problem. We present MolecularRNN, the graph recurrent generative model for molecular structures. Our model generates diverse realistic molecular graphs after likelihood pretraining on a big database of molecules. We perform an analysis of our pretrained models on large-scale generated datasets of 1 million samples. Further, the model is tuned with policy gradient algorithm, provided a critic that estimates the reward for the property of interest. We show a significant distribution shift to the desired range for lipophilicity, drug-likeness, and melting point outperforming state-of-the-art works. With the use of rejection sampling based on valency constraints, our model yields 100% validity. Moreover, we show that invalid molecules provide a rich signal to the model through the use of structure penalty in our reinforcement learning pipeline. ## Recorded benchmark results A research result applies to its stated dataset, split, metric and evaluation configuration. Compare like-for-like settings, preserve the metric's unit and direction, and distinguish paper-reported results from independent reproduction. The best result recorded here is not a claim of exhaustive, current global coverage. - [Property Optimization on ZINC250K (test)](): Property optimization; ZINC250k (test); 1st Order Metric: 0.844. ## Original sources and implementation [Original paper]() [PDF]()