Berdigaliyev N, Aljofan M. An overview of drug discovery and development. Fut Med Chem. 2020;12(10):939–47.
Sadybekov AV, Katritch V. Computational approaches streamlining drug discovery. Nature. 2023;616(7958):673–85.
Schwalbe-Koda D, Gómez-Bombarelli R. Generative models for automatic chemical design. In: Machine learning meets quantum physics; 2020. pp. 445–467.
Kingma DP, Welling M. Auto-encoding variational bayes. 2013. arXiv preprint arXiv:1312.6114.
Gómez-Bombarelli R, Wei JN, Duvenaud D, Hernández-Lobato JM, Sánchez-Lengeling B, Sheberla D, Aguilera-Iparraguirre J, Hirzel TD, Adams RP, Aspuru-Guzik A. Automatic chemical design using a data-driven continuous representation of molecules. ACS Central Sci. 2018;4(2):268–76.
Weininger D. Smiles, a chemical language and information system. 1. Introduction to methodology and encoding rules. J Chem Inf Comput Sci. 1988;28(1):31–6.
Kusner MJ, Paige B, Hernández-Lobato JM. Grammar variationalautoencoder. In: International conference on machine learning. PMLR; 2017. pp. 1945–1954.
Dai H, Tian Y, Dai B, Skiena S, Song L. Syntax-directed variationalautoencoder for molecule generation. In: Proceedings of the international conference on learning representations; 2018.
Jin, W., Barzilay, R., Jaakkola, T.: Junction tree variational autoencoder for molecular graph generation. In: International conference on machine learning. PMLR; 2018. pp. 2323–2332.
Shi C, Xu M, Zhu Z, Zhang W, Zhang M, Tang J. Graphaf: a flow-based autoregressive model for molecular graph generation. 2020. arXiv preprint arXiv:2001.09382.
Lee M, Min K. Mgcvae: multi-objective inverse design via molecular graph conditional variational autoencoder. J Chem Inf Model. 2022;62(12):2943–50.
Creswell A, White T, Dumoulin V, Arulkumaran K, Sengupta B, Bharath AA. Generative adversarial networks: an overview. IEEE Signal Process Mag. 2018;35(1):53–65.
Sutton RS, McAllester D, Singh S, Mansour Y. Policy gradient methods for reinforcement learning with function approximation. Adv Neural Inf Process Syst. 1999;12.
Yu L, Zhang W, Wang J, Yu Y. Seqgan: sequence generative adversarial nets with policy gradient. Proceedings of the AAAI Conference on Artificial Intelligence 2017;31(1). https://doi.org/10.1609/aaai.v31i1.10804.
Guimaraes GL, Sanchez-Lengeling B, Outeiral C, Farias PLC, Aspuru-Guzik A. Objective-reinforced generative adversarial networks (organ) for sequence generation models. 2017. arXiv preprint arXiv:1705.10843.
Li C, Yamanaka C, Kaitoh K, Yamanishi Y. Transformer-based objective-reinforced generative adversarial network to generate desired molecules. In: IJCAI; 2022. pp. 3884–3890.
Holland JH. Genetic algorithms. Sci Am. 1992;267(1):66–73.
Jensen JH. A graph-based genetic algorithm and generative model/monte carlo tree search for the exploration of chemical space. Chem Sci. 2019;10(12):3567–72.
Yüksel A, Ulusoy E, Ünlü A, Doğan T. Selformer: molecular representation learning via selfies language models. Mach Learn: Sci Technol. 2023;4(2):025035.
Krenn M, Häse F, Nigam A, Friederich P, Aspuru-Guzik A. Self-referencing embedded strings (selfies): a 100% robust molecular string representation. Mach Learn: Sci Technol. 2020;1(4):045024.
Ramakrishnan R, Dral PO, Rupp M, Von Lilienfeld OA. Quantum chemistry structures and properties of 134 kilo molecules. Sci Data. 2014;1(1):1–7.
Krenn M, Ai Q, Barthel S, Carson N, Frei A, Frey NC, Friederich P, Gaudin T, Gayle AA, Jablonka KM, et al. Selfies and the future of molecular string representations. Patterns. 2022;3(10):045024.
Landrum G, et al. Rdkit: a software suite for cheminformatics, computational chemistry, and predictive modeling. Greg Landrum. 2013;8(31.10):5281.
Covington P, Adams J, Sargin E. Deep neural networks for youtube recommendations. In: Proceedings of the 10th ACM conference on recommender systems; 2016. pp. 191–198.
Ferreira, C.: Gene expression programming: a new adaptive algorithm for solving problems; 2001. arXiv preprint arXiv:cs/0102027.
Nigam, A., Friederich, P., Krenn, M., Aspuru-Guzik, A.: Augmenting genetic algorithms with deep neural networks for exploring the chemical space. 2019. arXiv preprint arXiv:1909.11655.
McDougall J, Stoner EC. The computation of fermi-Dirac functions. Philos Trans R Soc Lond Ser A, Math Phys Sci. 1938;237(773):67–104.
Liang J, Xu Y, Liu R, Zhu X. Qm-sym, a symmetrized quantum chemistry database of 135 kilo molecules. Sci Data. 2019;6(1):213.
Tanimoto TT. Elementary mathematical theory of classification and prediction. 1958.
Cereto-Massagué A, Ojeda MJ, Valls C, Mulero M, Garcia-Vallvé S, Pujadas G. Molecular fingerprint similarity search in virtual screening. Methods. 2015;71:58–63. https://doi.org/10.1016/j.ymeth.2014.08.005
Popova M, Shvets M, Oliva J, et al. MolecularRNN: Generating realistic molecular graphs with optimized properties. 2019. arXiv preprint arXiv:1905.13372.
Wang J, Hsieh C-Y, Wang M, et al. Multi-constraint molecular generation based on conditional transformer, knowledge distillation and reinforcement learning. Nat Mach Intell. 2021;3(10):914–22.
Comments (0)