用1D SELFIES编码生成高亲和力药物分子，更高效。
SOLD: SELFIES-based Objective-driven Latent Diffusion
- 基于SELFIES字符串的潜在空间扩散模型，避免复杂3D构象生成。
- 生成的分子对目标蛋白亲和力高，效率优于传统方法。
- 适合药物设计初学者与需要快速生成分子的研究者。
近年来，机器学习在全新药物设计中产生了显著影响。然而，当前针对靶向蛋白生成新分子的方法通常直接在三维构象空间中进行，速度慢且过于复杂。本文提出SOLD（基于SELFIES的目标导向潜在扩散模型），一种新型潜在扩散模型，通过1D SELFIES字符串构建的潜在空间生成分子，并以目标蛋白为条件。过程中，我们还训练了一种创新的SELFIES Transformer，并提出了多任务学习模型损失平衡的新方法。该模型以简单高效的方式生成了对目标蛋白具有高亲和力的分子，同时通过增加更多数据仍具备进一步优化潜力。
原文摘要 · Abstract (English)
Recently, machine learning has made a significant impact on de novo drug design. However, current approaches to creating novel molecules conditioned on a target protein typically rely on generating molecules directly in the 3D conformational space, which are often slow and overly complex. In this work, we propose SOLD (SELFIES-based Objective-driven Latent Diffusion), a novel latent diffusion model that generates molecules in a latent space derived from 1D SELFIES strings and conditioned on a target protein. In the process, we also train an innovative SELFIES transformer and propose a new way to balance losses when training multi-task machine learning models.Our model generates high-affinity molecules for the target protein in a simple and efficient way, while also leaving room for future improvements through the addition of more data.
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