Notice: The reproducibility variables underlying each score are classified using an automated LLM-based pipeline, validated against a manually labeled dataset. LLM-based classification introduces uncertainty and potential bias; scores should be interpreted as estimates. Full accuracy metrics and methodology are described in Coakley et alK. L. Coakley, T. Snelleman, H. Hoos, and O. E. Gundersen, "The embrace of open science: An analysis of a decade of AI research and 56 800 conference papers," Under Review, 2026..
Multi-Objective Molecule Generation using Interpretable Substructures
Authors: Wengong Jin, Dr.Regina Barzilay, Tommi Jaakkola
ICML 2020 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | We evaluate our model on various drug design tasks and demonstrate significant improvements over state-of-the-art baselines in terms of accuracy, diversity, and novelty of generated compounds. |
| Researcher Affiliation | Academia | Wengong Jin 1 Regina Barzilay 1 Tommi Jaakkola 1 1MIT CSAIL. Correspondence to: Wengong Jin <EMAIL>. |
| Pseudocode | Yes | Algorithm 1 Training method with n property constraints. |
| Open Source Code | Yes | 1https://github.com/wengong-jin/multiobj-rationale |
| Open Datasets | Yes | We pre-train all the models on the same ChEMBL dataset, which contains 1.02 million training examples. On the four-property generation task, our model is fine-tuned for L = 50 iterations, with each rationale being expanded for K = 200 times. Following Li et al. (2018), the property prediction model is a random forest using Morgan fingerprint features (Rogers & Hahn, 2010). |
| Dataset Splits | Yes | For each property, we split its property dataset into 80%, 10% and 10% for training, validation and testing. |
| Hardware Specification | No | The paper does not provide specific details about the hardware used for running the experiments. |
| Software Dependencies | No | The paper mentions RDKit but does not provide specific version numbers for any software dependencies. |
| Experiment Setup | Yes | We set the positive threshold δi = 0.5. For each positive molecule, we run 20 iteration of MCTS with cpuct = 10. Our model is fine-tuned for L = 50 iterations, with each rationale being expanded for K = 200 times. |