理論基礎，思想很重要，但論證得并不好：

Cadeddu, A., Wylie, E. K., Jurczak, J., Wampler‐Doty, M., & Grzybowski, B. A. (2014). Organic chemistry as a language and the implications of chemical linguistics for structural and retrosynthetic analyses. Angewandte Chemie International Edition, 53(31), 8108-8112.

綜述類，關聯NLP方法和應用領域的表格挺有價值的：

?ztürk, H., ?zgür, A., Schwaller, P., Laino, T., & Ozkirimli, E. (2020). Exploring chemical space using natural language processing methodologies for drug discovery.?Drug Discovery Today, 25(4), 689-705.

首度提出Protein Vector(Protvec)和Gene Vector(Genevec)的概念：

Asgari, E., & Mofrad, M. R. K. (2015). Continuous distributed representation of biological sequences for deep proteomics and genomics.?PLoS ONE, 10(11), 1–15.

Protein與word embedding的結合：
Bepler, T., & Berger, B. (2019). Learning protein sequence embeddings using information from structure. 7th International Conference on Learning Representations, ICLR 2019, 1–17.

雖然漫畫中將2018年Schwaller發表的Seq2Seq（被期刊接收且效果好，見6）視作這個方法在生物分子領域的第一次成功應用，但做這方面的論文一般都會引用這篇作為一切故事的開端。兩個韓國高中生的作業，能做到這樣真的很厲害了：

Nam, J., & Kim, J. (2016). Linking the neural machine translation and the prediction of organic chemistry reactions. arXiv preprint arXiv:1612.09529.

Seq2Seq最佳：
Schwaller, P., Gaudin, T., Lanyi, D., Bekas, C., & Laino, T. (2018). “Found in Translation”: predicting outcomes of complex organic chemistry reactions using neural sequence-to-sequence models.?Chemical science, 9(28), 6091-6098.

另一篇比較有價值的Seq2Seq：
Karimi, M., Wu, D., Wang, Z., & Shen, Y. (2019). DeepAffinity: Interpretable deep learning of compound-protein affinity through unified recurrent and convolutional neural networks.?Bioinformatics, 35(18), 3329–3338.

漂亮的標題漂亮的intro，但內容不是很驚艷的BERT應用：

Vig, J., Madani, A., Varshney, L. R., Xiong, C., Socher, R., & Rajani, N. F. (2020). Bertology meets biology: Interpreting attention in protein language models. arXiv preprint arXiv:2006.15222.