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..
Uncertainty-Aware Yield Prediction with Multimodal Molecular Features
Authors: Jiayuan Chen, Kehan Guo, Zhen Liu, Olexandr Isayev, Xiangliang Zhang
AAAI 2024 | Venue PDF | 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. |