GxVAEs: Two Joint VAEs Generate Hit Molecules from Gene Expression Profiles
Authors: Chen Li, Yoshihiro Yamanishi
AAAI 2024 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Experiments and case studies on the generation of therapeutic molecules show that Gx VAEs outperforms current state-of-the-art baselines and yield hit-like molecules with potential bioactivity and drug-like properties. |
| Researcher Affiliation | Academia | Chen Li and Yoshihiro Yamanishi Graduate School of Informatics, Nagoya University, Chikusa, Nagoya, 464-8601, Japan li.chen.z2@a.mail.nagoya-u.ac.jp, yamanishi@i.nagoya-u.ac.jp |
| Pseudocode | Yes | Algorithm 1 in Appendix C shows the procedures of Gx VAEs. |
| Open Source Code | Yes | Code is available at: https://github.com/naruto7283/GxVEAs |
| Open Datasets | Yes | Chemically induced profiles were collected from the LINCS L1000 database (Duan et al. 2014)... Target perturbation profiles were obtained from the LINCS database... Disease-specific profiles were collected from the crowd extracted expression of differential signatures (CREEDS) database (Wang et al. 2016). |
| Dataset Splits | Yes | Figure F.2 in Appendix F shows the violin plots for the QED scores from the validation set (red) and the molecules generated by Mol VAE (green). |
| Hardware Specification | No | All the experiments were run on GPUs with CUDA. This statement is too general and does not provide specific hardware models (e.g., NVIDIA A100, RTX 2080 Ti) or detailed specifications. |
| Software Dependencies | No | The paper mentions software components such as 'RDKit tool' but does not specify version numbers for any software dependencies. |
| Experiment Setup | Yes | In Profile VAE, the encoder and decoder contained three feedforward layers of size of 512, 256, and 128. The learning rate and dropout probability were set to 1e 4 and 0.2, respectively. Mol VAE had an embedding size of 128 and three hidden layers of size 256. The learning rate, dropout probability, and temperature β were set to 5e 4, 0.1, and 1.0. The maximum length of generated SMILES strings was 100. The dimensionality and batch size of the latent vectors for the two VAEs were 64. Profile VAE and Mol VAE were trained for 2000 and 200 epochs, respectively. |