Notice: The reproducibility variables underlying each score are classified using an automated LLM-based pipeline, validated against a manually labeled dataset. LLM-based classification introduces uncertainty and potential bias; scores should be interpreted as estimates. Full accuracy metrics and methodology are described in [1].
ShEPhERD: Diffusing shape, electrostatics, and pharmacophores for bioisosteric drug design
Authors: Keir Adams, Kento Abeywardane, Jenna Fromer, Connor Coley
ICLR 2025 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | We train and evaluate Sh EPh ERD using two new datasets. Our first dataset (Sh EPh ERD-GDB17) contains 2.8M molecules sampled from medicinally-relevant subsets of GDB17... Our second dataset (Sh EPh ERD-MOSES-aq) contains 1.6M drug-like molecules from MOSES... We demonstrate Sh EPh ERD s potential for impact via exemplary drug design tasks including natural product ligand hopping, protein-blind bioactive hit diversification, and bioisosteric fragment merging. |
| Researcher Affiliation | Academia | Keir Adams , Kento Abeywardane , Jenna Fromer, & Connor W. Coley Massachusetts Institute of Technology, Cambridge, MA 02139, USA EMAIL |
| Pseudocode | Yes | Algorithm 1 Denoising Module for x1 Algorithm 2 Forward Pass of Sh EPh ERD s Denoising Network Algorithm 3 Training Algorithm Algorithm 4 Sampling Algorithm for Unconditional Generation Algorithm 5 Sampling Algorithm for Conditional Generation with Inpainting |
| Open Source Code | Yes | To ensure reproducibility, we make our datasets and all training, inference, and evaluation code available on Github at https: //github.com/coleygroup/shepherd and https://github.com/coleygroup/ shepherd-score. |
| Open Datasets | Yes | To ensure reproducibility, we make our datasets and all training, inference, and evaluation code available on Github at https: //github.com/coleygroup/shepherd and https://github.com/coleygroup/ shepherd-score. ... Our first dataset (Sh EPh ERD-GDB17) contains 2.8M molecules sampled from medicinally-relevant subsets of GDB17 (Ruddigkeit et al., 2012; Awale et al., 2019; B uhlmann & Reymond, 2020). Our second dataset (Sh EPh ERDMOSES-aq) contains 1.6M drug-like molecules from MOSES (Polykovskiy et al., 2020) |
| Dataset Splits | No | The paper mentions evaluating on '100 random target molecules (held out from training)' for specific experiments, but it does not provide specific proportions or methodologies for the train/test/validation splits of its main datasets (Sh EPh ERD-GDB17 and Sh EPh ERD-MOSES-aq) in the main text or appendix. |
| Hardware Specification | Yes | Training. We train all models with V100 GPUs (32 GB memory). ...Inference. For each of the P(x1, x2), P(x1, x3), and P(x1, x3, x4) models, generating a batch of 10 independent samples (either unconditionally or via inpainting) takes approximately 3-4 minutes on a V100 GPU |
| Software Dependencies | Yes | To evaluate their likelihood of retaining bioactivity, we use Autodock Vina (Trott & Olson, 2010; Eberhardt et al., 2021) to dock the generated ligands...dock the molecule with Autodock Vina v1.2.5... Auto Dock Vina (v1.1.2) |
| Experiment Setup | Yes | We train Sh EPh ERD with the Adam optimizer using a constant learning rate of 3e-4 and an effective batch size ranging from 40 to 48. We clip gradients that have norm exceeding 5.0. ... We use T = 400 for all Sh EPh ERD models. Table 7 lists hyperparameters relevant to training Sh EPh ERD. |