Is there a unified model for generating molecules considering different conditions, such as binding pockets and chemical properties? Although target-aware generative models have made significant advances in drug design, they do not consider chemistry conditions and cannot guarantee the desired chemical properties. Unfortunately, merging the target-aware and chemical-aware models into a unified model to meet customized requirements may lead to the problem of negative transfer. Inspired by the success of multi-task learning in the NLP area, we use prefix embeddings to provide a novel generative model that considers both the targeted pocket's circumstances and a variety of chemical properties. All conditional information is represented as learnable features, which the generative model subsequently employs as a contextual prompt. Experiments show that our model exhibits good controllability in both single and multi-conditional molecular generation. The controllability enables us to outperform previous structure-based drug design methods. More interestingly, we open up the attention mechanism and reveal coupling relationships between conditions, providing guidance for multi-conditional molecule generation.

翻译：是否存在一种统一模型，可在考虑结合口袋、化学性质等不同条件下生成分子？尽管靶标感知生成模型在药物设计中取得显著进展，但这类模型未纳入化学条件约束，无法确保目标化学性质。遗憾的是，将靶标感知与化学性质感知模型融合为统一模型以满足定制化需求时，可能引发负迁移问题。受自然语言处理领域多任务学习成功的启发，我们采用前缀嵌入提出一种新颖生成模型，该模型同时兼顾靶标口袋环境与多种化学性质。所有条件信息均被表征为可学习特征，生成模型随后将其作为上下文提示使用。实验表明，本模型在单条件与多条件分子生成中均展现出良好可控性。这种可控性使我们能够超越既往基于结构的药物设计方法。更有趣的是，我们揭示了注意力机制中条件间的耦合关系，为多条件分子生成提供指导。