Hierarchical Generation of Molecular Graphs using Structural Motifs

Authors: Wengong Jin, Dr.Regina Barzilay, Tommi Jaakkola

ICML 2020 | Conference PDF | Archive PDF | Plain Text | LLM Run Details

Reproducibility Variable Result LLM Response
Research Type Experimental We evaluate our model on multiple molecule generation tasks, including polymers, and show that our model significantly outperforms previous state-of-the-art baselines. ... The proposed model is evaluated on various tasks ranging from polymer generative modeling to graph translation for molecule property optimization. ... Our method (Hier VAE) significantly outperforms all previous methods in terms of reconstruction accuracy (79.9% vs 58.5%).
Researcher Affiliation Academia Wengong Jin 1 Regina Barzilay 1 Tommi Jaakkola 1 1MIT CSAIL. Correspondence to: Wengong Jin <wengong@csail.mit.edu>.
Pseudocode No The paper describes the architecture and processes in prose but does not include any pseudocode or algorithm blocks.
Open Source Code Yes 1https://github.com/wengong-jin/hgraph2graph
Open Datasets Yes Our method is evaluated on the polymer dataset from St. John et al. (2019), which contains 86K polymers in total (after removing duplicates). The dataset is divided into 76K, 5K and 5K for training, validation and testing.
Dataset Splits Yes The dataset is divided into 76K, 5K and 5K for training, validation and testing.
Hardware Specification No The paper does not provide specific hardware details (e.g., GPU/CPU models, memory) used for running its experiments.
Software Dependencies No The paper does not list specific software dependencies with version numbers (e.g., Python, PyTorch, CUDA versions).
Experiment Setup No The paper describes the model architecture and training objective (ELBO, teacher forcing) but does not explicitly provide concrete hyperparameter values (e.g., learning rate, batch size, number of epochs, specific optimizer settings) for the experimental setup.