Energy-Inspired Molecular Conformation Optimization
Authors: Jiaqi Guan, Wesley Wei Qian, qiang liu, Wei-Ying Ma, Jianzhu Ma, Jian Peng
ICLR 2022 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | In our experiments, these new variants show superior performance in molecular conformation optimization comparing to existing SE(3)-equivariant neural networks. Moreover, our energy-inspired formulation is also suitable for molecular conformation generation, where we can generate more diverse and accurate conformers comparing to existing baselines. |
| Researcher Affiliation | Collaboration | Jiaqi Guan University of Illinois at Urbana-Champaign jiaqi@illinois.edu Wesley Wei Qian University of Illinois at Urbana-Champaign weiqian3@illinois.edu Qiang Liu University of Texas at Austin lqiang@cs.utexas.edu Wei-Ying Ma AIR, Tsinghua University maweiying@air.tsinghua.edu.cn Jianzhu Ma Peking University Beijing Institute for General Artiļ¬cial Intelligence majianzhu@pku.edu.cn Jian Peng University of Illinois at Urbana-Champaign AIR, Tsinghua University Heli Xon Limited jianpeng@illinois.edu |
| Pseudocode | No | The paper describes the model's architecture and updates using equations and diagrams, but it does not include formal pseudocode or algorithm blocks. |
| Open Source Code | Yes | The model implementation, experimental data and model checkpoints can be found here: https://github.com/guanjq/confopt_official |
| Open Datasets | Yes | We test molecular conformer optimization on the QM9 dataset with small molecules (up to 9 heavy atoms) (Ramakrishnan et al., 2014) as well as the GEOM-Drugs dataset with larger molecules (up to 91 heavy atoms) (Axelrod & Gomez-Bombarelli, 2020). |
| Dataset Splits | Yes | We randomly split the QM9 dataset into 123k, 5k, 5k, and the GEOM-Drugs dataset into 270K, 10K, and 10K for respective training, validation, and testing. |
| Hardware Specification | Yes | We train all conformer optimization models for the QM9 dataset with one NVIDIA Ge Force GTX 1080 GPU and use one Ge Force GTX 3090 GPU for the GEOM-Drugs dataset. |
| Software Dependencies | No | The paper mentions optimizers like Adam and libraries like RDKit and Psi4, but does not provide specific version numbers for software dependencies needed to reproduce the experiments. |
| Experiment Setup | Yes | The hyper-parameter settings for the optimizer are init_learning_rate=0.001, betas=(0.95, 0.999), and clip_gradient_norm=8. We also schedule to decay the learning rate exponentially with a factor of 0.5 and a minimum learning rate of 0.00001. The learning rate is decayed if there is no improvement for the validation loss in 8 consecutive evaluations, and the training will be completely stopped if no improvement is found for 20 consecutive evaluations. The evaluation is done for every 2000 training steps/batches. |