3D generative models have shown significant promise in structure-based drug
design (SBDD), particularly in discovering ligands tailored to specific target
binding sites. Existing algorithms often focus primarily on ligand-target
binding, characterized by binding affinity. Moreover, models trained solely on
target-ligand distribution may fall short in addressing the broader objectives
of drug discovery, such as the development of novel ligands with desired
properties like drug-likeness, and synthesizability, underscoring the
multifaceted nature of the drug design process. To overcome these challenges,
we decouple the problem into molecular generation and property prediction. The
latter synergistically guides the diffusion sampling process, facilitating
guided diffusion and resulting in the creation of meaningful molecules with the
desired properties. We call this guided molecular generation process as TAGMol.
Through experiments on benchmark datasets, TAGMol demonstrates superior
performance compared to state-of-the-art baselines, achieving a 22% improvement
in average Vina Score and yielding favorable outcomes in essential auxiliary
properties. This establishes TAGMol as a comprehensive framework for drug
generation.

3D 生成模型在结构基药物设计中表现出显著的潜力，尤其在发现适应特定靶点结合位点的配体方面。TAGMol 是一个综合的药物生成框架，通过将问题分解为分子生成和特性预测，并通过引导扩散采样过程来实现生成具有所需特性的有意义的分子。在基准数据集上进行的实验证明，TAGMol 相对于现有先进算法具有优越性能，在平均 Vina 分数上提高了 22％，并在重要辅助属性方面取得了良好的结果。