人工智能蛋白质结构设计算法研究进展
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陈志航, 季梦麟, 戚逸飞
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Research progress of artificial intelligence in protein design
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Zhihang CHEN, Menglin JI, Yifei QI
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表2 固定骨架序列设计模型在TS50 &TS500测试集上的序列恢复率和困惑度［77］
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Table 2 The sequence recovery rate and perplexity of fixed-backbone sequence design models on TS50 &TS500 test sets
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模型类别 Group | 模型 Models | TS50 | TS500 |
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恢复率%(↑) Recovery % (↑) | 困惑度(↓) Perplexity (↓) | 恢复率%(↑) Recovery %(↑) | 困惑度(↓) Perplexity (↓) |
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MLP | SPIN | 30.00 | - | - | - | | SPIN2 | 34.00 | - | - | - | | Wang’s model | 33.00 | - | - | - | CNN | SPROF | 39.80 | - | - | - | | ProDCoNN | 46.50 | - | - | - | | DenseCPD | 50.71 | - | 55.53 | -- | GNN | StructGNN | 43.89 | 5.40 | 45.69 | 4.98 | | GraphTrans | 42.20 | 5.60 | 44.66 | 5.16 | | GVP-GNN | 44.14 | 4.71 | 49.14 | 4.20 | | GCA[78] | 47.02 | 5.09 | 47.74 | 4.72 | | ADesign[79] | 48.36 | 5.25 | 49.23 | 4.93 | | ProteinMPNN | 54.43 | 3.93 | 58.08 | 3.53 | | PiFold | 58.72 | 3.86 | 60.42 | 3.44 | | LM-DESIGN（PiFold） | 57.89 | 3.50 | 67.78 | 3.19 |
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