论文标题
受控分子发生器，以优化多种化学特性
Controlled Molecule Generator for Optimizing Multiple Chemical Properties
论文作者
论文摘要
具有所需化学特性的新型和优化的分子是药物发现过程的重要组成部分。不符合所需特性之一，可能会在临床测试中导致失败，这是昂贵的。此外，优化这些多个属性是一项具有挑战性的任务，因为一个属性的优化容易更改其他属性。在本文中，我们将这个多秘诀优化问题作为序列翻译过程提出，并提出了一个新的优化分子发生器模型，这些模型基于具有两个约束网络的变压器：属性预测和相似性预测。我们通过将这些约束网络中的得分预测纳入修改的光束搜索算法中，进一步改善了模型。该实验表明，我们提出的模型的表现优于最先进的模型，可以同时优化多个属性。
Generating a novel and optimized molecule with desired chemical properties is an essential part of the drug discovery process. Failure to meet one of the required properties can frequently lead to failure in a clinical test which is costly. In addition, optimizing these multiple properties is a challenging task because the optimization of one property is prone to changing other properties. In this paper, we pose this multi-property optimization problem as a sequence translation process and propose a new optimized molecule generator model based on the Transformer with two constraint networks: property prediction and similarity prediction. We further improve the model by incorporating score predictions from these constraint networks in a modified beam search algorithm. The experiments demonstrate that our proposed model outperforms state-of-the-art models by a significant margin for optimizing multiple properties simultaneously.
