Research productivity in the pharmaceutical industry has declined significantly in recent decades, with higher costs, longer timelines, and lower success rates of drug candidates in clinical trials. This has prioritized the scalability and multiobjectivity of drug discovery and design. De novo drug design has emerged as a promising approach; molecules are generated from scratch, thus reducing the reliance on trial and error and premade molecular repositories. However, optimizing for molecular traits remains challenging, impeding the implementation of de novo methods. In this work, we propose a de novo approach capable of optimizing multiple traits collectively. A recurrent neural network was used to generate molecules which were then ranked based on multiple properties by a nondominated sorting algorithm. The best of the molecules generated were selected and used to fine-tune the recurrent neural network through transfer learning, creating a cycle that mimics the traditional design–synthesis–test cycle. We demonstrate the efficacy of this approach through a proof of concept, optimizing for constraints on molecular weight, octanol-water partition coefficient, the number of rotatable bonds, hydrogen bond donors, and hydrogen bond acceptors simultaneously. Analysis of the molecules generated after five iterations of the cycle revealed a 14-fold improvement in the quality of generated molecules, along with improvements to the accuracy of the recurrent neural network and the structural diversity of the molecules generated. This cycle notably does not require large amounts of training data nor any handwritten scoring functions. Altogether, this approach uniquely combines scalable generation with multiobjective optimization of molecules.

中文翻译：

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具有递归神经网络和非支配排序的多目标从头药物设计

近几十年来，制药行业的研究生产率显着下降，其成本更高，时间表更长，候选药物在临床试验中的成功率更低。这优先考虑了药物发现和设计的可扩展性和多目标性。从头设计药物已成为一种有前途的方法。分子是从头开始生成的，因此减少了对反复试验和预制分子存储库的依赖。然而，优化分子特征仍然具有挑战性，阻碍了从头方法的实施。在这项工作中，我们提出了一种从头开始的方法，该方法能够集体优化多个特征。使用循环神经网络生成分子，然后通过非主导排序算法根据多种属性对分子进行排序。选择最佳生成的分子，并通过转移学习将其用于微调循环神经网络，从而创建一个模仿传统设计-合成-测试循环的循环。我们通过概念验证证明了该方法的有效性，同时优化了对分子量，辛醇-水分配系数，可旋转键数，氢键供体和氢键受体的限制。对循环进行五次迭代后生成的分子的分析显示，生成的分子的质量提高了14倍，同时循环神经网络的准确性和生成的分子的结构多样性也得​​到了提高。显然，该周期不需要大量的训练数据，也不需要任何手写评分功能。共，