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..
Deep Molecular Representation Learning via Fusing Physical and Chemical Information
Authors: Shuwen Yang, Ziyao Li, Guojie Song, Lingsheng Cai
NeurIPS 2021 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | We evaluated Phys Chem on quantum mechanics (QM7, QM8 and QM9) and physical chemistry (LIPOP, FREESOLV and ESOL) datasets, as well as drug effectiveness datasets of the notorious SARS-CoV-2. [...] We split all datasets into 8:1:1 as training, validation and test sets. |
| Researcher Affiliation | Academia | 1Key Laboratory of Machine Perception and Intelligence (MOE), Peking University, Beijing, China 2Center for Data Science, Peking University, Beijing, China |
| Pseudocode | No | No pseudocode or clearly labeled algorithm block found in the paper. |
| Open Source Code | No | The paper does not provide any explicit statement or link indicating the availability of open-source code for the described methodology. |
| Open Datasets | Yes | The datasets are publicly available at http://moleculenet.ai/datasets-1. The datasets are available at https://opendata.ncats.nih.gov/covid19/ (CC BY 4.0 license) and are continuously extended. |
| Dataset Splits | Yes | We split all datasets into 8:1:1 as training, validation and test sets. |
| Hardware Specification | No | No specific hardware details (like GPU models, CPU types, or memory) used for running experiments are mentioned in the paper. |
| Software Dependencies | Yes | We use the 2020.03.1.0 version of the RDKit package at http://www.rdkit.org/. |
| Experiment Setup | Yes | Unless otherwise specified, we used L = 2 pairs of blocks for Phys Chem. In the initializer, we used a 2-layer GCN and a 2-layer LSTM. In each Phys Net block, we set df = 3, S = 4 and τ = 0.25. In each Chem Net block, we set the dimensions of atom and bond states as 128 and 64, correspondingly. In the representation readout block, we used T = 1 global attentive layers with 256-dimensional meta-atom states. |