论文标题
基于配体的药物设计的模化形状条件生成的3D分子
Equivariant Shape-Conditioned Generation of 3D Molecules for Ligand-Based Drug Design
论文作者
论文摘要
与已知的配体相比，基于形状的虚拟筛选被广泛用于基于配体的药物设计中，以搜索具有相似3D形状但新颖的2D化学结构的分子的化学文库。 3D深生成模型有可能自动化这种形状条件的3D化学空间的探索；但是，没有现有模型可以在采用特定形状（例如已知结合姿势）的构象中可靠地产生有效的药物样分子。我们引入了一种新的多模式3D生成模型，该模型可以通过等效地编码分子形状和变异编码化学身份来实现形状条件的3D分子设计。我们通过使用启发式粘结几何形状使用基于自回旋的片段产生来确保生成分子的局部几何和化学有效性，从而使模型可以优先级的可旋转键的评分，以最好地将增长的构象结构与目标形状保持一致。我们在与药物设计相关的任务中评估了3D生成模型，包括形状条件的化学分子结构的生成以及形状约束的分子特性优化，这表明了其对枚举库虚拟筛选的实用性。
Shape-based virtual screening is widely employed in ligand-based drug design to search chemical libraries for molecules with similar 3D shapes yet novel 2D chemical structures compared to known ligands. 3D deep generative models have the potential to automate this exploration of shape-conditioned 3D chemical space; however, no existing models can reliably generate valid drug-like molecules in conformations that adopt a specific shape such as a known binding pose. We introduce a new multimodal 3D generative model that enables shape-conditioned 3D molecular design by equivariantly encoding molecular shape and variationally encoding chemical identity. We ensure local geometric and chemical validity of generated molecules by using autoregressive fragment-based generation with heuristic bonding geometries, allowing the model to prioritize the scoring of rotatable bonds to best align the growing conformational structure to the target shape. We evaluate our 3D generative model in tasks relevant to drug design including shape-conditioned generation of chemically diverse molecular structures and shape-constrained molecular property optimization, demonstrating its utility over virtual screening of enumerated libraries.
