最近对人工智能的研究表明,机器学习算法可以自动生成新的类似药物的分子。生成模型彻底改变了从头药物发现,使探索过程更有效。已经提出了几种模型框架和输入格式来增强智能算法在生成分子设计中的性能。在对过去五年的实验文章和评论的系统文献综述中,机器学习模型,与计算分子设计相关的挑战以及提出的解决方案,和分子编码方法进行了讨论。基于查询的PubMed搜索,ScienceDirect,Springer,Wiley在线图书馆,arXiv,MDPI,bioRxiv,IEEEXplore数据库产生了87项研究。通过引文搜索确定了另外12项研究。在实现机器学习的文章中,确定了六种突出的算法:长短期记忆递归神经网络(LSTM-RNN),变分自动编码器(VAE),生成对抗网络(GAN),对抗性自动编码器(AE),进化算法,和门控循环单元(GRU-RNN)。此外,八个中心挑战被指定:生成的分子文库的同质性,缺乏可合成性,有限的化验数据,模型可解释性,无法进行多属性优化,不可比较性,限制分子大小,模型评估的不确定性。分子被编码为字符串,偶尔使用随机化来增强,作为2D图形,或作为3D图形。进行统计分析和可视化以说明在过去五年中,从头药物设计中的机器学习方法如何发展。最后,讨论了未来的机会和保留。
Recent research on artificial intelligence indicates that machine learning algorithms can auto-generate novel drug-like molecules. Generative models have revolutionized de novo drug discovery, rendering the explorative process more efficient. Several model frameworks and input formats have been proposed to enhance the performance of intelligent algorithms in generative molecular design. In this systematic literature
review of experimental articles and reviews over the last five years, machine learning models, challenges associated with computational molecule design along with proposed solutions, and molecular encoding methods are discussed. A query-based search of the PubMed, ScienceDirect, Springer, Wiley Online Library, arXiv, MDPI, bioRxiv, and IEEE Xplore databases yielded 87 studies. Twelve additional studies were identified via citation searching. Of the articles in which machine learning was implemented, six prominent algorithms were identified: long short-term memory recurrent neural networks (LSTM-RNNs), variational autoencoders (VAEs), generative adversarial networks (GANs), adversarial autoencoders (AAEs), evolutionary algorithms, and gated recurrent unit (GRU-RNNs). Furthermore, eight central challenges were designated: homogeneity of generated molecular libraries, deficient synthesizability, limited assay data, model interpretability, incapacity for multi-property optimization, incomparability, restricted molecule size, and uncertainty in model evaluation. Molecules were encoded either as strings, which were occasionally augmented using randomization, as 2D graphs, or as 3D graphs. Statistical analysis and visualization are performed to illustrate how approaches to machine learning in de novo drug design have evolved over the past five years. Finally, future opportunities and reservations are discussed.