Navigating Chemical Space with Latent Flows
Authors: Guanghao Wei, Yining Huang, Chenru Duan, Yue Song, Yuanqi Du
NeurIPS 2024 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | We validate the efficacy of Chem Flow on molecule manipulation and singleand multi-objective molecule optimization tasks under both supervised and unsupervised molecular discovery settings. Codes and demos are publicly available on Git Hub at https://github.com/garywei944/Chem Flow. |
| Researcher Affiliation | Collaboration | Guanghao Wei* Cornell University gw338@cornell.edu Yining Huang* Harvard University yininghuang@hms.harvard.edu Chenru Duan Deep Principle, Inc. duanchenru@gmail.com Yue Song California Institute of Technology yuesong@caltech.edu Yuanqi Du Cornell University yd392@cornell.edu |
| Pseudocode | Yes | Algorithm 1 Chem Flow Training |
| Open Source Code | Yes | Codes and demos are publicly available on Git Hub at https://github.com/garywei944/Chem Flow. |
| Open Datasets | Yes | We extract 4,253,577 molecules from the three commonly used datasets for drug discovery including MOSES [46], ZINC250K [27], and Ch EMBL [68]. |
| Dataset Splits | Yes | The VAE is trained for 150 epochs with 4 restarts on 90% of the training data and validated with the rest 10% data. |
| Hardware Specification | Yes | All the experiments including baselines are conducted on one RTX 3090 GPU and one Nvidia A100 GPU. All docking scores are computed on one RTX 2080Ti GPU. |
| Software Dependencies | No | The paper mentions software components like Adam W optimizer and Mish activation function but does not provide specific version numbers for any key software dependencies or libraries needed for reproducibility. |
| Experiment Setup | Yes | Pre-trained VAE ... a 128 dimension embedding layer, 1024 latent space size, 3-hidden-layer encoder, and 3-hidden-layer decoder both with 1D batch normalization and non-linear activation functions. The hidden layer sizes are {4096, 2048, 1024} for the encoder and reversely for the decoder. The VAE is trained using an Adam W [42] optimizer, 0.001 initial learning rate, and 1,024 training batch size. |