Deep Generative Models for 3D Compound Design
Deep Generative Models for 3D Compound Design
复制标题
DOI：
10.1101/830497
复制
发表时间：
2019-11
期刊：
影响因子：
--
通讯作者：
F. Imrie;A. Bradley;M. van der Schaar;C. Deane
中科院分区：
文献类型：
--
作者：
F. Imrie;A. Bradley;M. van der Schaar;C. Deane
合理的化合物设计仍然是一个具有挑战性的问题，计算方法和药物化学家。计算生成方法已经开始显示出有前途的结果的设计问题。然而，他们还没有使用3D结构信息的力量。我们开发了一种新的基于图的深度生成模型，将最先进的机器学习技术与结构知识相结合。我们的方法（“DeLinker”）采用两个片段或部分结构，并设计一个包含两者的分子。生成过程依赖于蛋白质上下文，利用部分结构之间的相对距离和方向。这种三维信息是至关重要的成功的化合物设计，我们证明了其对生成过程的影响和省略这些信息的局限性。在大规模评估中，DeLinker设计的与原始分子具有高3D相似性的分子比数据库基线多60%。当考虑具有至少五个原子的较长连接子的更相关问题时，优于性能增加到200%。我们证明了这种方法在各种设计问题上的有效性和适用性：片段连接，支架跳跃和蛋白水解靶向嵌合体（PROTAC）设计。据我们所知，这是第一个将3D结构信息直接纳入设计过程的分子生成模型。代码可在https://github.com/oxpig/DeLinker上获得。
Rational compound design remains a challenging problem for both computational methods and medicinal chemists. Computational generative methods have begun to show promising results for the design problem. However, they have not yet used the power of 3D structural information. We have developed a novel graph-based deep generative model that combines state-of-the-art machine learning techniques with structural knowledge. Our method (“DeLinker”) takes two fragments or partial structures and designs a molecule incorporating both. The generation process is protein context dependent, utilising the relative distance and orientation between the partial structures. This 3D information is vital to successful compound design, and we demonstrate its impact on the generation process and the limitations of omitting such information. In a large scale evaluation, DeLinker designed 60% more molecules with high 3D similarity to the original molecule than a database baseline. When considering the more relevant problem of longer linkers with at least five atoms, the outperformance increased to 200%. We demonstrate the effectiveness and applicability of this approach on a diverse range of design problems: fragment linking, scaffold hopping, and proteolysis targeting chimera (PROTAC) design. As far as we are aware, this is the first molecular generative model to incorporate 3D structural information directly in the design process. Code is available at https://github.com/oxpig/DeLinker.