论文标题
旨在促进药物发现的化学空间的深度学习视图
A deep-learning view of chemical space designed to facilitate drug discovery
论文作者
论文摘要
药物发现项目需要进行设计，合成和测试的周期，这些周期产生了一系列相关的小分子，其特性（例如与给定靶蛋白的结合亲和力）逐渐量身定制为特定的药物发现目标。深度学习技术的使用可以增加在设计周期中使用人类直觉的典型实践，从而加快了药物发现项目。在这里，我们提出了DeSmiles，这是一个深层的神经网络模型，它在机器学习方法的分子设计方法中促进了最新技术的状态。我们将其应用于先前公布的基准测试，该基准评估了一种方法修改输入分子抑制多巴胺受体D2的能力，而与最新模型相比，脱米尔斯的失败率降低了77％。为了解释脱木的能力，我们可视化了脱木网络的一层，并通过使用desmiles来量身定制D2基准测试中使用的相同分子来进一步证明这种能力，以更耐用七个不同的受体。
Drug discovery projects entail cycles of design, synthesis, and testing that yield a series of chemically related small molecules whose properties, such as binding affinity to a given target protein, are progressively tailored to a particular drug discovery goal. The use of deep learning technologies could augment the typical practice of using human intuition in the design cycle, and thereby expedite drug discovery projects. Here we present DESMILES, a deep neural network model that advances the state of the art in machine learning approaches to molecular design. We applied DESMILES to a previously published benchmark that assesses the ability of a method to modify input molecules to inhibit the dopamine receptor D2, and DESMILES yielded a 77% lower failure rate compared to state-of-the-art models. To explain the ability of DESMILES to hone molecular properties, we visualize a layer of the DESMILES network, and further demonstrate this ability by using DESMILES to tailor the same molecules used in the D2 benchmark test to dock more potently against seven different receptors.
