Structure-based drug design by denoising voxel grids
Authors: Pedro O. Pinheiro, Arian Rokkum Jamasb, Omar Mahmood, Vishnu Sresht, Saeed Saremi
ICML 2024 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Compared to the current state of the art, our model is simpler to train, significantly faster to sample from, and achieves better results on extensive in silico benchmarks the generated molecules are more diverse, exhibit fewer steric clashes, and bind with higher affinity to protein pockets. |
| Researcher Affiliation | Industry | 1Prescient Design, Genentech. Correspondence to: Pedro O. Pinheiro <pedro@opinheiro.com>, Saeed Saremi <saremis@gene.com>. |
| Pseudocode | Yes | Algorithm 1 c WJS, using the BAOAB scheme (Sachs et al., 2017, Algorithm 1) adapted for the conditional ULD (9). |
| Open Source Code | No | The paper mentions that the code for the Pose Check tool is available, but does not provide any concrete access to the source code for the Vox Bind method described in the paper. |
| Open Datasets | Yes | We benchmark our model on Cross Docked2020 (Francoeur et al., 2020), a popular dataset for pocket-conditional molecule generation. |
| Dataset Splits | Yes | 100,000 and 100 ligand-pocket pairs are partitioned into the training and test sets, respectively. We take 100 samples from the training set as our hold-out validation set. |
| Hardware Specification | Yes | Both models are trained for 340 epochs on four NVIDIA A100 GPUs (a total of ten days). |
| Software Dependencies | No | The paper mentions several software tools like Open Babel, RDKit, Auto Dock Vina, MMseqs2, and PyUUL along with their citations, but it does not explicitly provide specific version numbers for these software dependencies in the text. |
| Experiment Setup | Yes | The models are trained with batch size of 64, learning rate 10 5 and weight decay 10 2. The weights are updated with Adam W optimizer (Loshchilov & Hutter, 2019) (β1 = .9, β2 = .999) and exponential moving average with decay .999. Both models are trained for 340 epochs... We use δ = σ/2 and fix γ = 1/δ, i.e., the effective friction (Saremi et al., 2023) is set to 1. |