Conditional Synthesis of 3D Molecules with Time Correction Sampler
Authors: Hojung Jung, Youngrok Park, Laura Schmid, Jaehyeong Jo, Dongkyu Lee, Bongsang Kim, Se-Young Yun, Jinwoo Shin
NeurIPS 2024 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | In this section, we present comprehensive experiments to evaluate the performance of TACS and demonstrate its effectiveness in generating 3D molecular structures with specific properties while maintaining stability and validity. In Section 5.1, we present synthetic experiment with H+ 3 molecules, where the ground state energies are computed using the variational quantum eigensolver (VQE). In Section 5.2, we assess our method using QM9, a standard dataset in quantum chemistry that includes molecular properties and atom coordinates. We compare our approach against several state-of-the-art baselines and provide a detailed analysis of the results. |
| Researcher Affiliation | Collaboration | KAIST AI1 LG Electronics2 |
| Pseudocode | Yes | Algorithm 1 Time-Aware Conditional Synthesis (TACS) |
| Open Source Code | No | Justification: We provide experimental details and will provide code after it is polished. |
| Open Datasets | Yes | Dataset We evaluate our method on QM9 dataset [45], which contains about 134k molecules with up to 9 heavy atoms of (C, N, O, F), each labeled with 12 quantum chemical properties. Following previous works [1, 23], we test on 6 types of quantum chemical properties and split the dataset into 100k/18k/13k molecules for training, validation, and test. |
| Dataset Splits | Yes | Following previous works [1, 23], we test on 6 types of quantum chemical properties and split the dataset into 100k/18k/13k molecules for training, validation, and test. |
| Hardware Specification | Yes | Finally, we train the time predictor within 24 hours with 4 NVIDIA A6000 GPUs. |
| Software Dependencies | No | The paper mentions "Rd Kit [32]" for evaluation metrics but does not provide specific version numbers for it or any other software dependencies like programming languages, machine learning frameworks, or numerical libraries. |
| Experiment Setup | Yes | The diffusion model is trained for 2000 epochs with a batch size of 64, learning rate of 0.0001, Adam optimizer, and an exponential moving average (EMA) with a decay rate of 0.9999. |