Uncertainty-Aware Yield Prediction with Multimodal Molecular Features
Authors: Jiayuan Chen, Kehan Guo, Zhen Liu, Olexandr Isayev, Xiangliang Zhang
AAAI 2024 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Extensive experiments on three datasets, including two high throughput experiment (HTE) datasets and one chemist-constructed Amide coupling reaction dataset, demonstrate that UAM outperforms the stateof-the-art methods. |
| Researcher Affiliation | Academia | 1The Ohio State University 2 Department of Computer Science and Engineering, University of Notre Dame 3Department of Chemistry, Carnegie Mellon University |
| Pseudocode | No | The paper describes the model architecture and its components with text and figures, but does not provide pseudocode or a clearly labeled algorithm block. |
| Open Source Code | Yes | The code and used datasets are available at https://github.com/jychen229/Multimodal-reaction-yieldprediction. |
| Open Datasets | Yes | We used Buchwald Hartwig dataset (Ahneman et al. 2018) and Suzuki Miyaura dataset (Perera et al. 2018)... Amide coupling reaction (ACR) dataset1. This is a recently launched large literature dataset, containing 41,239 amide coupling reactions extracted from Reaxys (Reaxys 2020). It is considerably more complex than the two HTE datasets. ... Available at https://github.com/isayevlab/amide reaction data |
| Dataset Splits | Yes | We adopted a train/valid/test split of 6/2/2 and employed early-stopping for avoid overfitting. |
| Hardware Specification | Yes | All experiments are executed on a single NVIDIA RTX3090 GPU. |
| Software Dependencies | No | Our model is implemented by Pytorch and optimized with Adam optimizer and cosine learning rate scheduler with warming up. |
| Experiment Setup | Yes | Our model is implemented by Pytorch and optimized with Adam optimizer and cosine learning rate scheduler with warming up. ... The expert assignment in Mo E is configured with t=1 and k=6. ... In the experiments on the ACR dataset, the late fusion module is designed with feature concatenation, and the Mo E is structured with two stacked layers. |