[論文レビュー] MolecularRNN: Generating realistic molecular graphs with optimized properties

Mariya Popova, Mykhailo Shvets+2 more|arXiv (Cornell University)|May 31, 2019

Computational Drug Discovery Methods参考文献 22被引用数 55

ひとこと要約

MolecularRNNは、結合法制約を満たしながら原子ごとに分子グラフを構築するグラフ再帰生成モデルで、100%の有効性を達成し、方策勾配強化学習を通じて性質最適化を可能にします。

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.

研究の動機と目的

• Motivate de novo molecular design through realistic graph-based generation.
• Develop a graph-based generator that respects chemical valency during inference.
• Enable property optimization of generated molecules via reinforcement learning.
• Provide large-scale analysis of generated molecules and compare to state-of-the-art methods.

提案手法

• Extend GraphRNN to molecular graphs with node types (atoms) and edge types (bond orders).
• Use BFS ordering to limit adjacency predictions and set M=12 for edge predictions.
• Apply valency-based rejection sampling to guarantee 100% chemical validity at inference.
• Introduce a structural penalty during training to leverage invalid intermediate structures as learning signal.
• Use policy gradient with a critic to optimize target properties (e.g., logP pen, QED, melting temperature).
• Pretrain unsupervised on large molecule datasets (ChEMBL, ZINC 250k, MOSES) to learn realistic distributions.

実験結果

リサーチクエスチョン

• RQ1 Can a graph-based generator produce diverse, valid, and novel molecular graphs that resemble real chemical space?
• RQ2 Does valency-based rejection sampling yield 100% valid molecules during inference without sacrificing diversity?
• RQ3 Can reinforcement learning shift the distribution of generated molecules toward predefined property ranges (logP pen, QED, melting temperature)?
• RQ4 Does incorporating a structural penalty from valency violations improve training signals and final validity?
• RQ5 How does MolecularRNN compare to state-of-the-art methods on large-scale generation and property optimization tasks?

主な発見

• Unsupervised likelihood training on large datasets yields high validity and diversity, with 65% validity before valency enforcement and up to 90% after structural penalty, reaching 100% with valency-based rejection sampling.
• On 1 million generated molecules, MolecularRNN achieves high validity (100%), near-maximum uniqueness and novelty across datasets, and competitive internal diversity and drug-likeness metrics.
• Compared to JT-VAE and GCPN, MolecularRNN attains comparable validity and novelty with lower synthetic accessibility scores and strong internal diversity.
• Policy-gradient optimization shifts distributions toward higher penalized logP and QED, with MolecularRNN outperforming baselines in top-molecule scores and in distributional shifts (QED).
• Melting temperature optimization demonstrates the model can learn to increase Tm by promoting aromatic fusion and polar groups, using a graph-convolution predictor as the reward.]
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