RGFN: Synthesizable Molecular Generation Using GFlowNets
Authors: Michał Koziarski, Andrei Rekesh, Dmytro Shevchuk, Almer van der Sloot, Piotr Gaiński, Yoshua Bengio, Chenghao Liu, Mike Tyers, Robert Batey
NeurIPS 2024 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | We experimentally evaluate RGFN on a set of diverse screening tasks, including pretrained proxy models and GPU-accelerated docking. |
| Researcher Affiliation | Academia | 1 Mila Québec AI Institute, 2 Université de Montréal, 3 University of Toronto, 4 Jagiellonian University, 5 Mc Gill University, 6 The Hospital for Sick Children Research Institute, 7 Acceleration Consortium |
| Pseudocode | No | The paper describes the generation process in a step-by-step manner (Section 3.2) and illustrates it in Figure 1, but does not present it as formal pseudocode or an algorithm block. |
| Open Source Code | Yes | Source code available at https://github.com/koziarskilab/RGFN. |
| Open Datasets | Yes | This includes proxy models... first, the commonly used s EH proxy as described in [4]. Second, a graph neural network trained on the biological activity classification task of senolytic [74] recognition. Third, the Dopamine Receptor D2 (DRD2) oracle [53] from Therapeutics Data Commons [28]. Pretraining was done in an unsupervised fashion on the ZINC15 dataset [66]. |
| Dataset Splits | No | The paper describes the training of the RGFN model using trajectory balance loss, but does not specify traditional train/validation/test dataset splits as it operates in a reinforcement learning setup against a reward function (oracle models). |
| Hardware Specification | Yes | Evaluating fragment scaling took approximately 800 GPU hours on Ge Force RTX 4090 in total. Remaining experiments took roughly 24 GPU hours per run on Quadro RTX 8000 for s EH, DRD2 and senolytic proxies, and roughly 72 GPU hours per run on an A100 for docking-based proxies. |
| Software Dependencies | Yes | We use the GPU-accelerated Vina-GPU 2.1 [68] implementation of the Quick Vina 2 [2] docking algorithm to calculate docking scores directly in the training loop of RGFN. |
| Experiment Setup | Yes | Both RGFN and FGFN were trained with trajectory balance loss [45] using Adam optimizer with a learning rate of 1 10 3, log Z learning rate of 1 10 1, and batch size of 100. The training lasted 4,000 steps. A random action probability of 0.05 was used, and RGFN used a replay buffer of 20 samples per batch. Both methods use a graph transformer policy with 5 layers, 4 heads, and 64 hidden dimensions. Exponentiated reward R(x) = exp(β score(x)) was used, with β dependent on the task: 8 for s EH proxy, 0.5 for senolytic proxy, 48 for DRD2 proxy, and 4 for all docking runs. |