Biases in Evaluation of Molecular Optimization Methods and Bias Reduction Strategies
Authors: Hiroshi Kajino, Kohei Miyaguchi, Takayuki Osogami
ICML 2023 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Let us empirically quantify the two biases as well as the effectiveness of the bias reduction methods. We employ a reinforcement learning setting as a case study. The code used in our empirical studies will be available in https://github.com/ kanojikajino/biases-in-mol-opt. |
| Researcher Affiliation | Industry | 1IBM Research Tokyo, Tokyo, Japan. Correspondence to: Hiroshi Kajino <kajino@jp.ibm.com>. |
| Pseudocode | No | The paper does not contain any clearly labeled pseudocode or algorithm blocks. |
| Open Source Code | Yes | The code used in our empirical studies will be available in https://github.com/ kanojikajino/biases-in-mol-opt. |
| Open Datasets | Yes | In specific, we used the predictor provided by Gottipati et al. (2020), which was trained on the Ch EMBL database (Gaulton et al., 2017) to predict p IC50 value associated with C-C chemokine receptor type 5 (CCR5). |
| Dataset Splits | No | The paper mentions generating 'train and test sets' and using a 'large sample Dtest of size 10^5' for approximating the true property function, but it does not explicitly specify a separate validation dataset split or its size. |
| Hardware Specification | Yes | We used an IBM Cloud with 16 2.10GHz CPU cores, 128GB memory, and two NVIDIA Tesla P100 GPUs. |
| Software Dependencies | Yes | We implement the whole simulation in Python 3.9.0. All of the chemistry-related operations including the template-based chemical reaction is implemented by RDKit (2021.09.3). |
| Experiment Setup | Yes | For the first 500 steps, we only update the parameters of the critic, fixing those of the actor, and after that, both of them are updated for another 1,500 steps. They are optimized by Ada Grad (Duchi et al., 2011) with initial learning rate 4 10 4 and batch size 64. |