论文标题
RL Boltzmann发电机在数据 - 帕斯斯环境中生成构象的生成器
RL Boltzmann Generators for Conformer Generation in Data-Sparse Environments
论文作者
论文摘要
结构化学家和生物学家的长期兴趣一直是一系列构象体的产生。蛋白质的一部分被称为本质上无序的蛋白质（IDP）未能表现出固定结构，因此也必须根据构象异构体的产生来研究。与小分子设置不同，在IDP设置中，地面真相数据稀疏，破坏了许多依赖此类数据进行培训的现有构象生成方法。 Boltzmann发电机仅对能量功能进行训练，是一种替代方案，但显示出一种模式崩溃，类似地排除了其直接应用到IDP的应用。我们研究了训练RL Boltzmann发电机对密切相关的“ Gibbs得分”的潜力，并证明了构象覆盖范围并不能很好地跟踪此类训练。这表明仅对能量训练的不足与建模方式无关
The generation of conformers has been a long-standing interest to structural chemists and biologists alike. A subset of proteins known as intrinsically disordered proteins (IDPs) fail to exhibit a fixed structure and, therefore, must also be studied in this light of conformer generation. Unlike in the small molecule setting, ground truth data are sparse in the IDP setting, undermining many existing conformer generation methods that rely on such data for training. Boltzmann generators, trained solely on the energy function, serve as an alternative but display a mode collapse that similarly preclude their direct application to IDPs. We investigate the potential of training an RL Boltzmann generator against a closely related "Gibbs score," and demonstrate that conformer coverage does not track well with such training. This suggests that the inadequacy of solely training against the energy is independent of the modeling modality
