MoFormer：基于条件Transformer联合多模态融合描述符的多目标抗菌肽生成
MoFormer: Multi-objective Antimicrobial Peptide Generation Based on Conditional Transformer Joint Multi-modal Fusion Descriptor
AI总结：
针对抗菌肽多目标优化的理想与现实权衡挑战，提出多目标AMP合成流程MoFormer，在条件约束与多模态融合描述符引导下于结构化潜在空间优化序列，结合Pareto排序与大模型微调代理分层筛选候选物，有效提升了抗菌活性并降低溶血性。
摘要：
深度学习在优化现有肽以赋予其更理想性质方面具有广阔前景，这是加速新药发现的关键一步。尽管近期涌现了多种优化的抗菌肽（AMP）生成方法，但由于理想与现实的权衡，多目标优化仍极具挑战。在此，我们建立了一个多目标AMP合成流程（MoFormer），用于同时优化AMP的多种属性。MoFormer在条件约束和细粒度多描述符的引导下，于高度结构化的潜在空间中改善AMP序列的期望属性。我们表明，MoFormer在增强抗菌活性和最小溶血性的生成任务中优于现有方法。我们还利用基于Pareto的非支配排序算法和基于大模型微调的代理，对候选物进行分层排序。我们从两个视角证明了使用MoFormer带来的实质性属性提升：(1)采用分子模拟和氨基酸间评分相互作用来解析AMP的结构与功能；(2)可视化潜在空间以检验质量与分布特征，验证了一种在设计约束下促进AMP多目标优化的有效手段。
英文摘要：
Deep learning holds a big promise for optimizing existing peptides with more desirable properties, a critical step towards accelerating new drug discovery. Despite the recent emergence of several optimized Antimicrobial peptides(AMP) generation methods, multi-objective optimizations remain still quite challenging for the idealism-realism tradeoff. Here, we establish a multi-objective AMP synthesis pipeline (MoFormer) for the simultaneous optimization of multi-attributes of AMPs. MoFormer improves the desired attributes of AMP sequences in a highly structured latent space, guided by conditional constraints and fine-grained multi-descriptor.We show that MoFormer outperforms existing methods in the generation task of enhanced antimicrobial activity and minimal hemolysis. We also utilize a Pareto-based non-dominated sorting algorithm and proxies based on large model fine-tuning to hierarchically rank the candidates. We demonstrate substantial property improvement using MoFormer from two perspectives: (1) employing molecular simulations and scoring interactions among amino acids to decipher the structure and functionality of AMPs; (2) visualizing latent space to examine the qualities and distribution features, verifying an effective means to facilitate multi-objective optimization AMPs with design constraints
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