Artificial intelligence (AI) is increasingly used in every stage of drug
development. Continuing breakthroughs in AI-based methods for drug discovery
require the creation, improvement, and refinement of drug discovery data. We
posit a new data challenge that slows the advancement of drug discovery AI:
datasets are often collected independently from each other, often with little
overlap, creating data sparsity. Data sparsity makes data curation difficult
for researchers looking to answer key research questions requiring values posed
across multiple datasets. We propose a novel diffusion GNN model Syngand
capable of generating ligand and pharmacokinetic data end-to-end. We show and
provide a methodology for sampling pharmacokinetic data for existing ligands
using our Syngand model. We show the initial promising results on the efficacy
of the Syngand-generated synthetic target property data on downstream
regression tasks with AqSolDB, LD50, and hERG central. Using our proposed model
and methodology, researchers can easily generate synthetic ligand data to help
them explore research questions that require data spanning multiple datasets.

人工智能在药物研发的各个阶段中得到了越来越多的应用。我们提出了一个新的数据挑战，即数据集常常相互独立收集，缺乏重叠部分，导致数据稀疏。为解决这一挑战，我们提出了一种新颖的扩散图神经网络模型 Syngand，可以全面生成配体和药物动力学数据。利用我们的 Syngand 模型，我们展示了一种用于现有配体采样药物动力学数据的方法学。我们在 AqSolDB、LD50 和 hERG central 等下游回归任务上展示了 Syngand 生成的合成靶标属性数据的初步有效结果。使用我们提出的模型和方法，研究人员可以轻松生成合成配体数据，以帮助他们探索涉及跨多个数据集的研究问题。