# Reinforced Molecular Optimization with Neighborhood-Controlled Grammars

arXiv: https://arxiv.org/abs/2011.07225 · HTML: https://mlsanity.com/paper/2011.07225/

## Key facts

- Cited by 10 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 14 Nov 2020.

## Built on by (most cited first, 10 of 10)

- [Learning to Extend Molecular Scaffolds with Structural Motifs](https://mlsanity.com/paper/2103.03864/) (2021)
- [DOCKSTRING: easy molecular docking yields better benchmarks for ligand design](https://mlsanity.com/paper/2110.15486/) (2021)
- [Artificial Intelligence in Drug Discovery: Applications and Techniques](https://mlsanity.com/paper/2106.05386/) (2021)
- [Conditional β-VAE for De Novo Molecular Generation](https://mlsanity.com/paper/2205.01592/) (2022)
- [Kernel-Elastic Autoencoder for Molecular Design](https://mlsanity.com/paper/2310.08685/) (2023)
- [An In-depth Summary of Recent Artificial Intelligence Applications in Drug Design](https://mlsanity.com/paper/2110.05478/) (2021)
- [SANGEA: Scalable and Attributed Network Generation](https://mlsanity.com/paper/2309.15648/) (2023)
- [Dynamic Vertex Replacement Grammars](https://mlsanity.com/paper/2303.11553/) (2023)
- [Hierarchical Grammar-Induced Geometry for Data-Efficient Molecular Property Prediction](https://mlsanity.com/paper/2309.01788/) (2023)
- [Constrained Goal-directed Planar Graph Generation with Grammar-based Reinforcement Learning](https://mlsanity.com/paper/2610.06244/) (2026)

## Abstract (arXiv)

A major challenge in the pharmaceutical industry is to design novel molecules with specific desired properties, especially when the property evaluation is costly. Here, we propose MNCE-RL, a graph convolutional policy network for molecular optimization with molecular neighborhood-controlled embedding grammars through reinforcement learning. We extend the original neighborhood-controlled embedding grammars to make them applicable to molecular graph generation and design an efficient algorithm to infer grammatical production rules from given molecules. The use of grammars guarantees the validity of the generated molecular structures. By transforming molecular graphs to parse trees with the inferred grammars, the molecular structure generation task is modeled as a Markov decision process where a policy gradient strategy is utilized. In a series of experiments, we demonstrate that our approach achieves state-of-the-art performance in a diverse range of molecular optimization tasks and exhibits significant superiority in optimizing molecular properties with a limited number of property evaluations.