In this article, we propose the deep neural network Adversarial Threshold Neural Computer (ATNC). The ATNC model is intended for the de novo design of novel small-molecule organic structures. The model is based on generative adversarial network architecture and reinforcement learning. ATNC uses a Differentiable Neural Computer as a generator and has a new specific block, called adversarial threshold (AT). AT acts as a filter between the agent (generator) and the environment (discriminator + objective reward functions). Furthermore, to generate more diverse molecules we introduce a new objective reward function named Internal Diversity Clustering (IDC). In this work, ATNC is tested and compared with the ORGANIC model. Both models were trained on the SMILES string representation of the molecules, using four objective functions (internal similarity, Muegge druglikeness filter, presence or absence of sp³-rich fragments, and IDC). The SMILES representations of 15K druglike molecules from the ChemDiv collection were used as a training data set. For the different functions, ATNC outperforms ORGANIC. Combined with the IDC, ATNC generates 72% of valid and 77% of unique SMILES strings, while ORGANIC generates only 7% of valid and 86% of unique SMILES strings. For each set of molecules generated by ATNC and ORGANIC, we analyzed distributions of four molecular descriptors (number of atoms, molecular weight, logP, and tpsa) and calculated five chemical statistical features (internal diversity, number of unique heterocycles, number of clusters, number of singletons, and number of compounds that have not been passed through medicinal chemistry filters). Analysis of key molecular descriptors and chemical statistical features demonstrated that the molecules generated by ATNC elicited better druglikeness properties. We also performed in vitro validation of the molecules generated by ATNC; results indicated that ATNC is an effective method for producing hit compounds.

中文翻译：

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用于分子从头设计的对抗阈值神经计算机

在本文中，我们提出了深度神经网络对抗阈值神经计算机（ATNC）。ATNC模型专用于从头开始新型小分子有机结构的设计 该模型基于生成对抗网络架构和强化学习。ATNC使用微分神经计算机作为生成器，并具有一个新的特定块，称为对抗阈值（AT）。AT充当代理（生成器）和环境（区分器+客观奖励函数）之间的过滤器。此外，为了生成更多种不同的分子，我们引入了一个新的客观奖励函数，称为内部多样性聚类（IDC）。在这项工作中，对ATNC进行了测试，并将其与ORGANIC模型进行了比较。使用四个目标函数（内部相似度，Muegge药物相似性过滤器，sp ^3的存在与否）在分子的SMILES字符串表示形式上对两个模型进行了训练。-丰富的片段和IDC）。来自ChemDiv集合的15K类药物分子的SMILES表示用作训练数据集。对于不同的功能，ATNC的性能优于ORGANIC。与IDC结合使用时，ATNC会生成72％的有效SMILES字符串和77％的唯一SMILES字符串，而ORGANIC只会生成7％的有效字符串和86％的唯一SMILES字符串。对于ATNC和ORGANIC生成的每组分子，我们分析了四个分子描述符的分布（原子数，分子量，logP和tpsa），并计算了五个化学统计特征（内部多样性，唯一杂环数，簇数，单身人数，以及尚未通过药物化学过滤器的化合物的数量）。对关键分子描述符和化学统计特征的分析表明，ATNC产生的分子具有更好的药物相似性。我们还表演了ATNC产生的分子的体外验证；结果表明，ATNC是生产命中化合物的有效方法。