Deep generative diffusion models are a promising avenue for de novo 3D
molecular design in material science and drug discovery. However, their utility
is still constrained by suboptimal performance with large molecular structures
and limited training data. Addressing this gap, we explore the design space of
E(3) equivariant diffusion models, focusing on previously blank spots. Our
extensive comparative analysis evaluates the interplay between continuous and
discrete state spaces. Out of this investigation, we introduce the EQGAT-diff
model, which consistently surpasses the performance of established models on
the QM9 and GEOM-Drugs datasets by a large margin. Distinctively, EQGAT-diff
takes continuous atomic positions while chemical elements and bond types are
categorical and employ a time-dependent loss weighting that significantly
increases training convergence and the quality of generated samples. To further
strengthen the applicability of diffusion models to limited training data, we
examine the transferability of EQGAT-diff trained on the large PubChem3D
dataset with implicit hydrogens to target distributions with explicit
hydrogens. Fine-tuning EQGAT-diff for a couple of iterations further pushes
state-of-the-art performance across datasets. We envision that our findings
will find applications in structure-based drug design, where the accuracy of
generative models for small datasets of complex molecules is critical.

深度生成扩散模型在材料科学和药物发现中是一种有前途的新途径。然而，它们的效用仍受到大分子结构和有限的训练数据的限制。本研究通过对连续和离散状态空间的相互作用进行广泛的比较分析，探索了 E (3) 等变扩散模型的设计空间并提出了 EQGAT-diff 模型。该模型在 QM9 和 GEOM-Drugs 数据集上的表现显著优于现有模型，并通过引入连续原子位置、分类化学元素和键类型，并采用时间依赖的损失加权，显著提高了训练收敛性和生成样本的质量。我们还研究了将在 PubChem3D 数据集上针对隐式氢训练的 EQGAT-diff 模型迁移到具有显式氢的目标分布的可行性，并通过若干迭代的微调进一步推动数据集上的最新性能。本研究的发现有望在基于结构的药物设计中应用，尤其对于复杂分子的小数据集的生成模型的准确性至关重要。