Synthetic Biology Journal ›› 2021, Vol. 2 ›› Issue (1): 1-14.DOI: 10.12211/2096-8280.2020-074
• Invited Review • Previous Articles Next Articles
Ye WANG, Haochen WANG, Minghao YAN, Guanhua HU, Xiaowo WANG
Received:
2020-07-09
Revised:
2020-11-15
Online:
2021-03-12
Published:
2021-03-22
Contact:
Xiaowo WANG
王也, 王昊晨, 晏明皓, 胡冠华, 汪小我
通讯作者:
汪小我
作者简介:
王也(1995—),女,博士研究生,主要研究方向为模式识别与机器学习、生物信息学、合成生物学。E-mail:wangy17@mails.tsinghua.edu.cn基金资助:
CLC Number:
Ye WANG, Haochen WANG, Minghao YAN, Guanhua HU, Xiaowo WANG. Design of biomolecular sequences by artificial intelligence[J]. Synthetic Biology Journal, 2021, 2(1): 1-14.
王也, 王昊晨, 晏明皓, 胡冠华, 汪小我. 生物分子序列的人工智能设计[J]. 合成生物学, 2021, 2(1): 1-14.
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URL: https://synbioj.cip.com.cn/EN/10.12211/2096-8280.2020-074
深度生成式模型 | 生物序列 | 数据形式 | 模型名称 | 寻优算法 | 相关文献 |
---|---|---|---|---|---|
生成对抗网络 | 核酸 | 碱基序列独热编码 | WGAN | 性能得分 梯度寻优 | [ |
蛋白质 | 氨基酸距离矩阵 | DCGAN | 基于Rosetta的采样 | [ | |
小分子药物 | 分子图矩阵编码 | MolGAN | 强化学习 | [ | |
变分/对抗自编码器 | 小分子药物 | 原子团的连接树编码 | Junction Tree VAE | 贝叶斯优化 | [ |
SMILES独热编码 | ChemVAE | 性能得分 梯度寻优 | [ | ||
邻接矩阵与属性向量等组成的概率图 | GraphVAE | 条件生成 | [ | ||
循环神经网络 | 小分子药物 | SMILES独热编码 | ChemTS | 蒙特卡洛树搜索 | [ |
LSTM | 迁移学习 | [ | |||
蛋白质 | 氨基酸独热编码 | LSTM | 迁移学习 | [ |
Tab. 1 Applications for deep generative models combined with optimization algorithms
深度生成式模型 | 生物序列 | 数据形式 | 模型名称 | 寻优算法 | 相关文献 |
---|---|---|---|---|---|
生成对抗网络 | 核酸 | 碱基序列独热编码 | WGAN | 性能得分 梯度寻优 | [ |
蛋白质 | 氨基酸距离矩阵 | DCGAN | 基于Rosetta的采样 | [ | |
小分子药物 | 分子图矩阵编码 | MolGAN | 强化学习 | [ | |
变分/对抗自编码器 | 小分子药物 | 原子团的连接树编码 | Junction Tree VAE | 贝叶斯优化 | [ |
SMILES独热编码 | ChemVAE | 性能得分 梯度寻优 | [ | ||
邻接矩阵与属性向量等组成的概率图 | GraphVAE | 条件生成 | [ | ||
循环神经网络 | 小分子药物 | SMILES独热编码 | ChemTS | 蒙特卡洛树搜索 | [ |
LSTM | 迁移学习 | [ | |||
蛋白质 | 氨基酸独热编码 | LSTM | 迁移学习 | [ |
评估指标类型 | 评估指标 | 小分子药物 | 蛋白质序列 | 核酸序列 |
---|---|---|---|---|
分布拟合评估 | 合理性 | SMILES/分子图的合理性 | Rosetta仿真结果 | 连续碱基数目 |
多样性 | 不重复小分子比例 | 不重复的蛋白质比例 | 设计的序列之间的相似性 | |
新颖性 | 新药比例 | 新蛋白比例 | 与天然序列的相似性 | |
分布拟合度 | Frechet ChemNet Distance | 经验性适应度分布得分 | 与天然序列的K-mer相关性 | |
物理化学约束符合度 | 物理化学性质的KL散度 | 统计能量函数 | GC含量 | |
定向优化评估 | 重要结构特征 | 化学结构特征 | 与已知重要功能团的相似性 | 功能性的Motif或重要间隔序列长度 |
测试集重设计比例 | 药物分子重设计比例 | 未报道 | 未报道 | |
自定义优化功能得分 | 药物溶解度等性能得分 | 蛋白靶向位点的功能得分 | 调控强度等功能得分 |
Tab. 2 Evaluation criteria for deep generative model designed biomolecular sequences
评估指标类型 | 评估指标 | 小分子药物 | 蛋白质序列 | 核酸序列 |
---|---|---|---|---|
分布拟合评估 | 合理性 | SMILES/分子图的合理性 | Rosetta仿真结果 | 连续碱基数目 |
多样性 | 不重复小分子比例 | 不重复的蛋白质比例 | 设计的序列之间的相似性 | |
新颖性 | 新药比例 | 新蛋白比例 | 与天然序列的相似性 | |
分布拟合度 | Frechet ChemNet Distance | 经验性适应度分布得分 | 与天然序列的K-mer相关性 | |
物理化学约束符合度 | 物理化学性质的KL散度 | 统计能量函数 | GC含量 | |
定向优化评估 | 重要结构特征 | 化学结构特征 | 与已知重要功能团的相似性 | 功能性的Motif或重要间隔序列长度 |
测试集重设计比例 | 药物分子重设计比例 | 未报道 | 未报道 | |
自定义优化功能得分 | 药物溶解度等性能得分 | 蛋白靶向位点的功能得分 | 调控强度等功能得分 |
生物序列 | 数量级 | 智能算法 | 优势 | 挑战 |
---|---|---|---|---|
小分子药物序列 | 1.5×106 [ | 常用RNN、AAE、VAE、GAN结合强化学习和迁移学习进行药物序列设计 | 数据与数据库积累丰富;评估体系较为成熟 | 合成相对困难,需考虑与筛选易于合成的分子序列 |
1.8×106 [ | ||||
1500[ | ||||
蛋白质 序列 | 约100 000 [ | 常用RNN、GAN、ANN结合蛋白设计的Rosetta软件和迁移学习进行蛋白序列设计[ | 模拟预测软件如Rosetta在领域内标准化程度高;蛋白设计可应用场景广阔 | 三维空间结构、折叠构象的搜索与预测准确性仍有限 |
核酸序列 | 与具体物种基因组大小以及核酸序列对应的功能相关 | 利用GAN结合专家知识、预测器等对核酸序列进行设计 | 核酸序列相对易于合成,设计灵活度高,合成周期较短 | 特定功能的核酸序列数据集规模小;调控元件等序列在基因组缺乏精确定义 |
Tab. 3 Advantages and challenges of intelligent design for drug molecules, proteins and nucleic acid sequences
生物序列 | 数量级 | 智能算法 | 优势 | 挑战 |
---|---|---|---|---|
小分子药物序列 | 1.5×106 [ | 常用RNN、AAE、VAE、GAN结合强化学习和迁移学习进行药物序列设计 | 数据与数据库积累丰富;评估体系较为成熟 | 合成相对困难,需考虑与筛选易于合成的分子序列 |
1.8×106 [ | ||||
1500[ | ||||
蛋白质 序列 | 约100 000 [ | 常用RNN、GAN、ANN结合蛋白设计的Rosetta软件和迁移学习进行蛋白序列设计[ | 模拟预测软件如Rosetta在领域内标准化程度高;蛋白设计可应用场景广阔 | 三维空间结构、折叠构象的搜索与预测准确性仍有限 |
核酸序列 | 与具体物种基因组大小以及核酸序列对应的功能相关 | 利用GAN结合专家知识、预测器等对核酸序列进行设计 | 核酸序列相对易于合成,设计灵活度高,合成周期较短 | 特定功能的核酸序列数据集规模小;调控元件等序列在基因组缺乏精确定义 |
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