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MolecularRNN: Generating realistic molecular graphs with optimized properties
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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.
Forward citations
Cited by 7 Pith papers
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Adopting Reinforcement Learning with Verifiable Rewards for Molecular Generation
LLMol fine-tunes an LLM on simplified SELFIES and uses GRPO with RDKit-derived rewards for targeted molecular generation, but its own benchmark tables contradict the claimed state-of-the-art performance.
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A Metric for the Balance of Information in Graph Learning
NNRD, the log average ratio of performance under feature noise to performance under structure noise, is proposed as a dataset-level indicator of which information source a molecular graph task favors.
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Graph Neural Networks in Modern AI-aided Drug Discovery
A comprehensive model-centric review of graph neural network methods and applications in AI-aided drug discovery, from molecular representation to synthesis planning.
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