One of the major applications of generative models for drug Discovery targets the lead-optimization phase. During the optimization of a lead series, it is common to have scaffold constraints imposed on the structure of the molecules designed. Without enforcing such constraints, the probability of generating molecules with the required scaffold is extremely low and hinders the practicality of generative models for de-novo drug design. To tackle this issue, we introduce a new algorithm to perform scaffold-constrained in-silico molecular design. We build on the well-known SMILES-based Recurrent Neural Network (RNN) generative model, with a modified sampling procedure to achieve scaffold-constrained generation. We directly benefit from the associated reinforcement Learning methods, allowing to design molecules optimized for different properties while exploring only the relevant chemical space. We showcase the method's ability to perform scaffold-constrained generation on various tasks: designing novel molecules around scaffolds extracted from SureChEMBL chemical series, generating novel active molecules on the Dopamine Receptor D2 (DRD2) target, and, finally, designing predicted actives on the MMP-12 series, an industrial lead-optimization project.

中文翻译：

---

支架约束的分子生成

药物发现生成模型的主要应用之一是针对先导优化阶段。在先导系列的优化过程中，通常会对设计的分子结构施加支架约束。如果不强制执行此类约束，生成具有所需支架的分子的概率极低，并且阻碍了用于从头药物设计的生成模型的实用性。为了解决这个问题，我们引入了一种新算法来执行支架约束的计算机内分子设计。我们建立在著名的基于 SMILES 的循环神经网络 (RNN) 生成模型的基础上，并使用修改后的采样程序来实现支架约束生成。我们直接受益于相关的强化学习方法，允许设计针对不同特性优化的分子，同时仅探索相关的化学空间。我们展示了该方法在各种任务上执行支架约束生成的能力：围绕从 SureChEMBL 化学系列中提取的支架设计新分子，在多巴胺受体 D2 (DRD2) 目标上生成新的活性分子，最后，在 MMP 上设计预测的活性-12系列，工业铅优化项目。