De novo drug design requires simultaneously generating novel molecules outside of training data and predicting their target properties, making it a hard task for generative models. To address this, we propose Joint Transformer that combines a Transformer decoder, Transformer encoder, and a predictor in a joint generative model with shared weights. We formulate a probabilistic black-box optimization algorithm that employs Joint Transformer to generate novel molecules with improved target properties and outperforms other SMILES-based optimization methods in de novo drug design.

翻译：从头药物设计需要同时生成训练数据之外的新颖分子并预测其靶点性质，这使得生成模型难以胜任。为此，我们提出联合Transformer（Joint Transformer），该模型将Transformer解码器、Transformer编码器与预测器整合为具有共享权重的联合生成模型。我们设计了一种概率性黑盒优化算法，利用联合Transformer生成具有更优靶点性质的新颖分子，在从头药物设计中优于其他基于SMILES的优化方法。