[1] I. J. Goodfellow, Y. Bengio, and A. Courville, Deep Learning. Cambridge: MIT Press,
2016.
[2] A. Vaswani, N. Shazeer, N. Parmar, J. Uszkoreit, L. Jones, A. N. Gomez, L. Kaiser, and
I. Polosukhin, “Attention Is All You Need,” ArXiv, vol. preprint, p. ArXiv ID:1706.03762v5,
2017.
[3] W. Chen and A. L. Ferguson, “Molecular enhanced sampling with autoencoders: On-the-fly
collective variable discovery and accelerated free energy landscape exploration,” Journal of
Computational Chemistry, vol. 39, no. 25, pp. 2079–2102, sep 2018.
[4] O. Russakovsky, J. Deng, H. Su, J. Krause, S. Satheesh, S. Ma, Z. Huang, A. Karpathy,
A. Khosla, M. Bernstein, A. C. Berg, and L. Fei-Fei, “ImageNet Large Scale Visual Recog-
nition Challenge,” International Journal of Computer Vision, vol. 115, no. 3, pp. 211–252,
2015.
[5] Y. LeCun, B. Boser, J. S. Denker, D. Henderson, R. E. Howard, W. Hubbard, and L. D.
Jackel, “Backpropagation Applied to Handwritten Zip Code Recognition,” Neural Computa-
tion, vol. 1, no. 4, pp. 541–551, dec 1989.
[6] J. Schmidhuber, “Learning Complex, Extended Sequences Using the Principle of History
Compression,” Neural Computation, vol. 4, no. 2, pp. 234–242, mar 1992.
[7] K. Cho, B. van Merri ̈enboer, D. Bahdanau, and Y. Bengio, “On the properties of neural ma-
chine translation: Encoder–decoder approaches,” Proceedings of SSST 2014 - 8th Workshop
on Syntax, Semantics and Structure in Statistical Translation, pp. 103–111, 2014.
[8] D. P. Kingma and M. Welling, “Auto-Encoding Variational Bayes,” in 2nd International Con-
ference on Learning Representations, ICLR 2014 - Conference Track Proceedings, no. Ml,
dec 2013, p. ArXiv ID: 1312.6114.
[9] A. Van Den Oord, O. Vinyals, and K. Kavukcuoglu, “Neural discrete representation learning,”
arXiv, vol. preprint, p. arXiv ID:1711.00937, 2017.
[10] A. Vahdat and J. Kautz, “NVAE: A deep hierarchical variational autoencoder,” arXiv, vol.
preprint, p. arXiv ID:2007.03898, 2021.
[11] I. J. Goodfellow, J. Pouget-Abadie, M. Mirza, B. Xu, D. Warde-Farley, S. Ozair,
A. Courville, and Y. Bengio, “Generative Adversarial Networks,” arXiv, vol. preprint, p.
arXiv ID:1406.2661, jun 2014.
[12] A. Makhzani, J. Shlens, N. Jaitly, I. Goodfellow, and B. Frey, “Adversarial Autoencoders,”
arXiv, vol. preprint, p. ArXiv ID:1511.05644, 2015.
[13] D. Bahdanau, K. Cho, and Y. Bengio, “Neural Machine Translation by Jointly Learning to
Align and Translate,” in 3rd International Conference on Learning Representations, ICLR
2015 - Conference Track Proceedings, sep 2014, pp. 1–15.
[14] Y. Liu, M. Ott, N. Goyal, J. Du, M. Joshi, D. Chen, O. Levy, M. Lewis, L. Zettlemoyer, and
V. Stoyanov, “RoBERTa: A Robustly Optimized BERT Pretraining Approach,” arXiv, vol.
preprint, p. arXiv ID:1907.11692, 2019.
[15] J. Devlin, M. W. Chang, K. Lee, and K. Toutanova, “BERT: Pre-training of deep bidirectional
transformers for language understanding,” arXiv, vol. preprint, p. arXiv ID:1810.04805,
2019.
[16] T. B. Brown, B. Mann, N. Ryder, M. Subbiah, J. Kaplan, P. Dhariwal, A. Neelakantan,
P. Shyam, G. Sastry, A. Askell, S. Agarwal, A. Herbert-Voss, G. Krueger, T. Henighan,
R. Child, A. Ramesh, D. M. Ziegler, J. Wu, C. Winter, C. Hesse, M. Chen, E. Sigler,
M. Litwin, S. Gray, B. Chess, J. Clark, C. Berner, S. McCandlish, A. Radford, I. Sutskever,
and D. Amodei, “Language Models are Few-Shot Learners,” in Advances in Neural Informa-
tion Processing Systems, may 2020.
[17] Centers for Disease Control and Prevention, “Antibiotic resistance threats in the United
States,” National Center for Emerging Zoonotic and Infectious Diseases (U.S.), Atlanta,
Georgia, Tech. Rep., nov 2019.
[18] C. L. Ventola, “The antibiotic resistance crisis: part 1: causes and threats.” P and T : a
peer-reviewed journal for formulary management, vol. 40, no. 4, pp. 277–83, apr 2015.
[19] K. Bush, P. Courvalin, G. Dantas, J. Davies, B. Eisenstein, P. Huovinen, G. A. Jacoby,
R. Kishony, B. N. Kreiswirth, E. Kutter, S. A. Lerner, S. Levy, K. Lewis, O. Lomovskaya,
J. H. Miller, S. Mobashery, L. J. V. Piddock, S. Projan, C. M. Thomas, A. Tomasz, P. M.
Tulkens, T. R. Walsh, J. D. Watson, J. Witkowski, W. Witte, G. Wright, P. Yeh, and H. I.
Zgurskaya, “Tackling antibiotic resistance,” Nature Reviews Microbiology, vol. 9, no. 12, pp.
894–896, dec 2011.
[20] J. Li, J. J. Koh, S. Liu, R. Lakshminarayanan, C. S. Verma, and R. W. Beuerman, “Mem-
brane active antimicrobial peptides: Translating mechanistic insights to design,” Frontiers in
Neuroscience, vol. 11, p. Article 73, 2017.
[21] M. Magana, M. Pushpanathan, A. L. Santos, L. Leanse, M. Fernandez, A. Ioannidis, M. A.
Giulianotti, Y. Apidianakis, S. Bradfute, A. L. Ferguson, A. Cherkasov, M. N. Seleem,
C. Pinilla, C. de la Fuente-Nunez, T. Lazaridis, T. Dai, R. A. Houghten, R. E. Hancock,
and G. P. Tegos, “The value of antimicrobial peptides in the age of resistance,” The Lancet
Infectious Diseases, vol. 20, no. 9, pp. e216–e230, 2020.
[22] J. J. Schneider, A. Unholzer, M. Schaller, M. Sch ̈afer-Korting, and H. C. Korting, “Human
defensins,” Journal of Molecular Medicine, vol. 83, no. 8, pp. 587–595, aug 2005.
[23] J. Koehbach and D. J. Craik, “The Vast Structural Diversity of Antimicrobial Peptides,”
Trends in Pharmacological Sciences, vol. 40, no. 7, pp. 517–528, jul 2019.
[24] J. Lei, L. Sun, S. Huang, C. Zhu, P. Li, J. He, V. Mackey, D. H. Coy, and Q. He, “The
antimicrobial peptides and their potential clinical applications,” Am J Transl Res, vol. 11,
no. 7, pp. 3919–3931, 2019.
[25] F. Guilhelmelli, N. Vilela, P. Albuquerque, L. d. S. Derengowski, I. Silva-Pereira, and C. M.
Kyaw, “Antibiotic development challenges: The various mechanisms of action of antimicro-
bial peptides and of bacterial resistance,” Frontiers in Microbiology, vol. 4, no. DEC, pp.
1–12, 2013.
[26] A. Bin Hafeez, X. Jiang, P. J. Bergen, and Y. Zhu, “Antimicrobial Peptides: An Update on
Classifications and Databases,” International Journal of Molecular Sciences, vol. 22, no. 21,
p. 11691, oct 2021.
[27] G. Wang, “The antimicrobial peptide database provides a platform for decoding the design
principles of naturally occurring antimicrobial peptides,” Protein Science, vol. 29, no. 1, pp.
8–18, jan 2020.
[28] S. P. Piotto, L. Sessa, S. Concilio, and P. Iannelli, “YADAMP: yet another database of antimi-
crobial peptides,” International Journal of Antimicrobial Agents, vol. 39, no. 4, pp. 346–351,
apr 2012.
[29] F. H. Waghu and S. Idicula-Thomas, “Collection of antimicrobial peptides database and its
derivatives: Applications and beyond,” Protein Science, vol. 29, no. 1, pp. 36–42, jan 2020.
[30] P. Cramer, “AlphaFold2 and the future of structural biology,” Nature Structural and Molecu-
lar Biology, vol. 28, no. 9, pp. 704–705, sep 2021.
[31] D. P. Kingma and M. Welling, “An Introduction to Variational Autoencoders,” Foundations
and Trends in Machine Learning, vol. 12, no. 4, pp. 307–392, jun 2019.
[32] R. G ́omez-Bombarelli, J. N. Wei, D. Duvenaud, J. M. Hern ́andez-Lobato, B. S ́anchez-
Lengeling, D. Sheberla, J. Aguilera-Iparraguirre, T. D. Hirzel, R. P. Adams, and A. Aspuru-
Guzik, “Automatic Chemical Design Using a Data-Driven Continuous Representation of
Molecules,” ACS Central Science, vol. 4, no. 2, pp. 268–276, feb 2018.
[33] D. Polykovskiy, A. Zhebrak, B. Sanchez-Lengeling, S. Golovanov, O. Tatanov, S. Belyaev,
R. Kurbanov, A. Artamonov, V. Aladinskiy, M. Veselov, A. Kadurin, S. Johansson, H. Chen,
S. Nikolenko, A. Aspuru-Guzik, and A. Zhavoronkov, “Molecular Sets (MOSES): A Bench-
marking Platform for Molecular Generation Models,” Frontiers in Pharmacology, vol. 11,
pp. 1–19, 2020.
[34] A. Tucs, D. P. Tran, A. Yumoto, Y. Ito, T. Uzawa, and K. Tsuda, “Generating Ampicillin-
Level Antimicrobial Peptides with Activity-Aware Generative Adversarial Networks,” ACS
Omega, vol. 5, no. 36, pp. 22 847–22 851, sep 2020.
[35] H. Chen, O. Engkvist, Y. Wang, M. Olivecrona, and T. Blaschke, “The rise of deep learning
in drug discovery,” Drug Discovery Today, vol. 23, no. 6, pp. 1241–1250, 2018.
[36] M. H. Segler, T. Kogej, C. Tyrchan, and M. P. Waller, “Generating focused molecule libraries
for drug discovery with recurrent neural networks,” ACS Central Science, vol. 4, no. 1, pp.
120–131, 2018.
[37] P. Das, T. Sercu, K. Wadhawan, I. Padhi, S. Gehrmann, F. Cipcigan, V. Chenthamarakshan,
H. Strobelt, C. dos Santos, P.-Y. Chen, Y. Y. Yang, J. P. K. Tan, J. Hedrick, J. Crain, and
A. Mojsilovic, “Accelerated antimicrobial discovery via deep generative models and molec-
ular dynamics simulations,” Nature Biomedical Engineering, vol. 5, no. 6, pp. 613–623, jun
2021.
[38] T. Sercu, S. Gehrmann, H. Strobelt, P. Das, I. Padhi, C. D. Santos, K. Wadhawan, and
V. Chenthamarakshan, “Interactive Visual Exploration of Latent Space (IVELS) for pep-
tide auto-encoder model selection,” in Deep Generative Models for Highly Structured Data,
DGS@ICLR 2019 Workshop. International Conference on Learning Representations, ICLR,
2019.
[39] C. M. Van Oort, J. B. Ferrell, J. M. Remington, S. Wshah, and J. Li, “AMPGAN v2: Machine
Learning-Guided Design of Antimicrobial Peptides,” Journal of Chemical Information and
Modeling, vol. 61, no. 5, pp. 2198–2207, 2021.
[40] D. Nagarajan, T. Nagarajan, N. Roy, O. Kulkarni, S. Ravichandran, M. Mishra, D. Chakra-
vortty, and N. Chandra, “Computational antimicrobial peptide design and evaluation against
multidrug-resistant clinical isolates of bacteria,” Journal of Biological Chemistry, vol. 293,
no. 10, pp. 3492–3509, 2018.
[41] A. Capecchi, X. Cai, H. Personne, T. K ̈ohler, C. van Delden, and J. L. Reymond, “Machine
learning designs non-hemolytic antimicrobial peptides,” Chemical Science, vol. 12, no. 26,
pp. 9221–9232, 2021.
[42] O. Dollar, N. Joshi, D. A. C. Beck, and J. Pfaendtner, “Attention-based generative models for
de novo molecular design,” Chemical Science, vol. 12, no. 24, pp. 8362–8372, 2021.
[43] O. Prykhodko, S. V. Johansson, P.-C. Kotsias, J. Ar ́us-Pous, E. J. Bjerrum, O. Engkvist,
and H. Chen, “A de novo molecular generation method using latent vector based generative
adversarial network,” Journal of Cheminformatics, vol. 11, no. 1, p. 74, dec 2019.
[44] M. J. Kusner, B. Paige, and J. M. Hern ́andez-Lobato, “Grammar Variational Autoencoder,”
in 34th International Conference on Machine Learning, ICML 2017, mar 2017, p. ArXiv ID:
1703.01925.
[45] F. Grisoni, M. Moret, R. Lingwood, and G. Schneider, “Bidirectional Molecule Generation
with Recurrent Neural Networks,” Journal of Chemical Information and Modeling, vol. 60,
no. 3, pp. 1175–1183, mar 2020.
[46] H. Dai, Y. Tian, B. Dai, S. Skiena, and L. Song, “Syntax-directed variational autoencoder for
structured data,” in 6th International Conference on Learning Representations, ICLR 2018 -
Conference Track Proceedings, 2018, p. ArXiv ID: 1802.08786.
[47] B. Dai, Z. Wang, and D. Wipf, “The usual suspects? Reassessing blame for VAE posterior
collapse,” arXiv, p. ArXiv ID: 1912.10702, 2019.
[48] R. Chowdhury, N. Bouatta, S. Biswas, C. Floristean, A. Kharkar, K. Roy, C. Rochereau,
G. Ahdritz, J. Zhang, G. M. Church, P. K. Sorger, and M. AlQuraishi, “Single-sequence pro-
tein structure prediction using a language model and deep learning,” Nature Biotechnology,
vol. 40, no. 11, pp. 1617–1623, nov 2022.
[49] E. C. Alley, G. Khimulya, S. Biswas, M. AlQuraishi, and G. M. Church, “Unified ratio-
nal protein engineering with sequence-based deep representation learning,” Nature Methods,
vol. 16, no. 12, pp. 1315–1322, 2019.
[50] H. J. Kim, S. E. Hong, and K. J. Cha, “seq2vec: Analyzing sequential data using multi-rank
embedding vectors,” Electronic Commerce Research and Applications, vol. 43, no. August
2019, p. 101003, 2020.
[51] A. Bateman, “UniProt: A worldwide hub of protein knowledge,” Nucleic Acids Research,
vol. 47, no. D1, pp. D506–D515, jan 2019.
[52] S. A. Pinacho-Castellanos, C. R. Garc ́ıa-Jacas, M. K. Gilson, and C. A. Brizuela, “Alignment-
Free Antimicrobial Peptide Predictors: Improving Performance by a Thorough Analysis of
the Largest Available Data Set,” Journal of Chemical Information and Modeling, vol. 61,
no. 6, pp. 3141–3157, 2021.
[53] S. Ramazi, N. Mohammadi, A. Allahverdi, E. Khalili, and P. Abdolmaleki, “A review on an-
timicrobial peptides databases and the computational tools,” Database, vol. 2022, no. Febru-
ary, pp. 1–17, mar 2022.
[54] D. P. Kingma, T. Salimans, and M. Welling, “Variational Dropout and the Local Reparame-
terization Trick,” arXiv, vol. preprint, p. ArXiv ID:1506.02557, jun 2015.
[55] D. P. Kingma, T. Salimans, R. Jozefowicz, X. Chen, I. Sutskever, and M. Welling, “Improving
Variational Inference with Inverse Autoregressive Flow,” in Advances in Neural Information
Processing Systems, no. Nips, jun 2016, p. ArXiv ID: 1606.04934.
[56] I. Tolstikhin, O. Bousquet, S. Gelly, and B. Sch ̈olkopf, “Wasserstein auto-encoders,” in 6th
International Conference on Learning Representations, ICLR 2018 - Conference Track Pro-
ceedings, 2018, pp. 1–20.
[57] T. Karras, M. Aittala, S. Laine, E. H ̈ark ̈onen, J. Hellsten, J. Lehtinen, and T. Aila, “Alias-Free
Generative Adversarial Networks,” in Proceedings of the IEEE Computer Society Conference
on Computer Vision and Pattern Recognition, no. NeurIPS, jun 2021, pp. 438–448.
[58] T. Karras, S. Laine, M. Aittala, J. Hellsten, J. Lehtinen, and T. Aila, “Analyzing and Im-
proving the Image Quality of StyleGAN,” in Proceedings of the IEEE Computer Society
Conference on Computer Vision and Pattern Recognition, dec 2019, pp. 8107–8116.
[59] A. Gretton, K. M. Borgwardt, M. J. Rasch, B. Sch ̈olkopf, and A. Smola, “A kernel two-
sample test,” Journal of Machine Learning Research, vol. 13, no. 25, pp. 723–773, 2012.
[60] J. Vig, “A multiscale visualization of attention in the transformer model,” in ACL 2019 - 57th
Annual Meeting of the Association for Computational Linguistics, Proceedings of System
Demonstrations, 2019, pp. 37–42.
[61] C. Saharia, W. Chan, S. Saxena, L. Li, J. Whang, E. Denton, S. K. S. Ghasemipour, B. K.
Ayan, S. S. Mahdavi, R. G. Lopes, T. Salimans, J. Ho, D. J. Fleet, and M. Norouzi, “Pho-
torealistic Text-to-Image Diffusion Models with Deep Language Understanding,” arXiv, vol.
preprint, p. arXiv ID:2205.11487, 2022.
[62] P. J. Rousseeuw, “Silhouettes: A graphical aid to the interpretation and validation of cluster
analysis,” Journal of Computational and Applied Mathematics, vol. 20, no. C, pp. 53–65,
1987.
[63] P. J. Cock, T. Antao, J. T. Chang, B. A. Chapman, C. J. Cox, A. Dalke, I. Friedberg, T. Hamel-
ryck, F. Kauff, B. Wilczynski, and M. J. De Hoon, “Biopython: Freely available Python tools
for computational molecular biology and bioinformatics,” Bioinformatics, vol. 25, no. 11, pp.
1422–1423, 2009.
[64] S. Henikoff and J. G. Henikoff, “Amino acid substitution matrices from protein blocks,”
Proceedings of the National Academy of Sciences of the United States of America, vol. 89,
no. 22, pp. 10 915–10 919, 1992.
[65] F. Cipcigan, A. P. Carrieri, E. O. Pyzer-Knapp, R. Krishna, Y.-W. Hsiao, M. Winn, M. G.
Ryadnov, C. Edge, G. Martyna, and J. Crain, “Accelerating molecular discovery through
data and physical sciences: Applications to peptide-membrane interactions,” The Journal of
Chemical Physics, vol. 148, no. 24, p. 241744, jun 2018.
[66] H. Jeon, H. K. Ko, J. Jo, Y. Kim, and J. Seo, “Measuring and Explaining the Inter-Cluster
Reliability of Multidimensional Projections,” IEEE Transactions on Visualization and Com-
puter Graphics, vol. 28, no. 1, pp. 551–561, 2022.
[67] R. A. Mansbach and A. L. Ferguson, “Machine learning of single molecule free energy sur-
faces and the impact of chemistry and environment upon structure and dynamics,” The Jour-
nal of Chemical Physics, vol. 142, no. 10, p. 105101, mar 2015.
[68] M. Larralde, “Peptides,” p. github.com/althonos/peptides.py, 2021.
[69] A. Ikai, “Thermostability and aliphatic index of globular proteins,” Journal of Biochemistry,
vol. 88, no. 6, pp. 1895–1898, 1980.
[70] H. G. Boman, “Antibacterial peptides: Basic facts and emerging concepts,” Journal of Inter-
nal Medicine, vol. 254, no. 3, pp. 197–215, 2003.
[71] Compiled by A. D. McNaught and A. Wilkinson., Compendium of Chemical Terminology,
2nd ed. (the ”Gold Book”). Oxford: Blackwell Scientific Publications, 1997.
[72] J. Kyte and R. F. Doolittle, “A simple method for displaying the hydropathic character of a
protein,” Journal of Molecular Biology, vol. 157, no. 1, pp. 105–132, may 1982.
[73] K. Guruprasad, B. V. Reddy, and M. W. Pandit, “Correlation between stability of a protein
and its dipeptide composition: A novel approach for predicting in vivo stability of a protein
from its primary sequence,” Protein Engineering, Design and Selection, vol. 4, no. 2, pp.
155–161, 1990.
[74] P. J. Mohr and B. N. Taylor, “CODATA recommended values of the fundamental physical
constants: 1998,” Reviews of Modern Physics, vol. 72, no. 2, pp. 351–495, apr 2000.
[75] R. Luo, L. Sun, Y. Xia, T. Qin, S. Zhang, H. Poon, and T. Y. Liu, “BioGPT: generative pre-
trained transformer for biomedical text generation and mining,” arXiv, vol. preprint, p. arXiv
ID:2210.10341, 2022.
[76] M. Arts, V. G. Satorras, C.-W. Huang, D. Zuegner, M. Federici, C. Clementi, F. No ́e,
R. Pinsler, and R. van den Berg, “Two for One: Diffusion Models and Force Fields for
Coarse-Grained Molecular Dynamics,” arXiv, vol. preprint, p. arXiv ID:2302.00600, 2023.
[77] G. Corso, H. St ̈ark, B. Jing, R. Barzilay, and T. Jaakkola, “DiffDock: Diffusion Steps, Twists,
and Turns for Molecular Docking,” arXiv, vol. preprint, p. arXiv ID:2210.01776, 2022.
[78] F. Scarselli, M. Gori, Ah Chung Tsoi, M. Hagenbuchner, and G. Monfardini, “The Graph
Neural Network Model,” IEEE Transactions on Neural Networks, vol. 20, no. 1, pp. 61–80,
jan 2009.