Unified Guidance for Geometry-Conditioned Molecular Generation
Authors: Sirine Ayadi, Leon Hetzel, Johanna Sommer, Fabian Theis, Stephan Günnemann
NeurIPS 2024 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | We show how applications such as structure-based, fragment-based, and ligand-based drug design are formulated in the Uni Guide framework and demonstrate on-par or superior performance compared to specialised models. |
| Researcher Affiliation | Academia | School of Computation, Information and Technology, Technical University of Munich Munich Data Science Institute, Technical University of Munich Center for Computation Health, Helmholtz Munich {si.ayadi, l.hetzel, jm.sommer, f.theis, s.guennemann}@tum.de |
| Pseudocode | Yes | Algorithm 1: Sampling algorithm to generate a ligand that is conditioned on a reference ligand Mref s surface, using an unconditional model ϵθ(zt, t) modelling the distribution over molecules. |
| Open Source Code | Yes | We made the code available as part of the supplementary material with the submission. We have included the link to Uni Guide s project page, which will reference the public codebase. |
| Open Datasets | Yes | Following Chen et al. [14], we employ the MOSES dataset for the ligand-based drug design task [66]. ... We train two unconditional diffusion models, Shape Mol [U] and EDM, to generate 3D molecules on the MOSES dataset [66], licensed under the MIT License, for which we generate 3D conformers with RDKit [73], available under the BSD 3-Clause License. |
| Dataset Splits | Yes | We use 1, 593, 653 training samples and randomly select 1000 samples for validation. |
| Hardware Specification | Yes | We run multi-GPU trainings on 4 NVIDIA A100 GPUs until convergence, however, a single NVIDIA A100 GPU is sufficient for this training and will only increase the training time. |
| Software Dependencies | No | The paper mentions software such as RDKit and Open Drug Discovery Toolkit (ODDT) and their licenses, but does not provide specific version numbers for these or other software dependencies necessary for replication. |
| Experiment Setup | Yes | The model architecture of Shape Mol[U] is an unconditional version of the Shape Mol model proposed in Chen et al. [14], and it is trained with 1000 diffusion steps. Shape Mol [U] is trained with a batch size of 32 on two NVIDIA A100 GPUs for 500 epochs. |