Molecule Design by Latent Prompt Transformer
Authors: Deqian Kong, Yuhao Huang, Jianwen Xie, Edouardo Honig, Ming Xu, Shuanghong Xue, Pei Lin, Sanping Zhou, Sheng Zhong, Nanning Zheng, Ying Nian Wu
NeurIPS 2024 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Experiments demonstrate that LPT not only effectively discovers useful molecules across single-objective, multi-objective, and structure-constrained optimization tasks, but also exhibits strong sample efficiency. |
| Researcher Affiliation | Collaboration | 1Department of Statistics and Data Science, UCLA 2Institute of Artificial Intelligence and Robotics, Xi an Jiaotong University 3Bio Map Research 4Akool Research 5Institute of Engineering in Medicine, UCSD 6Shu Chien-Gene Lay Department of Bioengineering, UCSD |
| Pseudocode | Yes | Algorithm 1 MLE learning of Latent Prompt Transformer (LPT); Algorithm 2 Online learning of LPT |
| Open Source Code | Yes | Project page: https://sites.google.com/view/latent-prompt-transformer. (Footnote 1). In the NeurIPS Paper Checklist, it states: "Code is released in our project page." |
| Open Datasets | Yes | For molecule design tasks, we use SELFIES representations of the ZINC (Irwin et al., 2012) dataset, which comprises 250K drug-like molecules as our offline dataset D. [...] The TF Bind 8 task focuses on identifying DNA sequences that are 8 bases long... This task contains a training set of 32,898 samples... The GFP task involves generating protein sequences of 237 amino acids... we use a subset of 5,000 samples as the training set by following the methodology outlined in Trabucco et al. (2022). |
| Dataset Splits | No | The paper mentions training sets and evaluating on certain numbers of samples, but does not explicitly provide percentages or counts for validation splits, nor does it specify a cross-validation setup for all experiments. |
| Hardware Specification | Yes | Training was conducted on an NVIDIA A6000 GPU, requiring 20 hours for pre-training, 10 hours for fine-tuning, and 12 hours for online learning. Additional details can be found in App. A.3. |
| Software Dependencies | No | The paper mentions RDKit and Auto Dock GPU as tools used for property computation and the Adam W optimizer for training, but does not specify their version numbers or other crucial software dependencies with specific versions (e.g., Python, PyTorch, CUDA versions). |
| Experiment Setup | Yes | The prior model, pα(z), of LPT is a one-dimensional UNet... The sequence generation model, pβ(x|z), is implemented as a 3-layer causal Transformer, while a 3-layer MLP serves as the predictor model, pγ(y|z). As described in Sec. 3.2, we pre-train LPT on molecules for 30 epochs and then fine-tune it with target properties for an additional 10 epochs... We use the Adam W optimizer... with a weight decay of 0.1. |