F. Wu, M. Misra, A.K. Mohanty, Challenges and new opportunities on barrier performance of biodegradable polymers for sustainable packaging. Prog. Polym. Sci. 117, 101395 (2021). https://doi.org/10.1016/j.progpolymsci.2021.101395

Article  CAS  Google Scholar 

R. Grace, Closing the circle: reshaping how products are conceived and made. Plast. Eng. 73, 8–11 (2017). https://doi.org/10.1002/j.1941-9635.2017.tb01670.x

Article  Google Scholar 

F. Allen, J. Gasparro, J. Swaney, M. Phelan, J. Gillespie, Directive 2004/38/EC of the European Parliament and of the Council of 29 April 2004, Immigration Law Handbook (2023) 2253-C79P212. https://doi.org/10.1093/oso/9780192896292.003.0079

T. No, 301: Ready biodegradability. OECD (1992). https://doi.org/10.1787/9789264070349-en

Article  Google Scholar 

P.A. Vanrolleghen, K.J. Keesman. Identification of biodegradation models under model and data uncertainty, Water Sci. Technol. (1996). https://doi.org/10.1016/0273-1223(96)00192-8

P.G. Polishchuk, T.I. Madzhidov, A. Varnek, Estimation of the size of drug-like chemical space based on GDB-17 data. J. Comput. Aided Mol. Des. 27, 675–679 (2013). https://doi.org/10.1007/s10822-013-9672-4

Article  CAS  PubMed  Google Scholar 

D. Weininger, SMILES, a chemical language and information system. 1. Introduction to methodology and encoding rules. J. Chem. Inf. Comput. Sci. 28, 31–36 (1988). https://doi.org/10.1021/ci00057a005

Article  CAS  Google Scholar 

C. Bilodeau, W. Jin, T. Jaakkola, R. Barzilay, K.F. Jensen, Generative models for molecular discovery: recent advances and challenges. WIREs Comput. Mol. Sci. (2022). https://doi.org/10.1002/wcms.1608

Article  Google Scholar 

M. Olivecrona, T. Blaschke, O. Engkvist, H. Chen, Molecular de-novo design through deep reinforcement learning. J. Cheminform. 9, 48 (2017). https://doi.org/10.1186/s13321-017-0235-x

Article  PubMed Central  PubMed  Google Scholar 

P.-H. Chiu, Y.-L. Yang, H.-K. Tsao, Y.-J. Sheng, Deep learning for predictions of hydrolysis rates and conditional molecular design of esters. J. Taiwan Inst. Chem. Eng. 126, 1–13 (2021). https://doi.org/10.1016/j.jtice.2021.06.045

Article  CAS  Google Scholar 

M. Wang, C.-Y. Hsieh, J. Wang, D. Wang, G. Weng, C. Shen, X. Yao, Z. Bing, H. Li, D. Cao, T. Hou, RELATION: a deep generative model for structure-based de novo drug design. J. Med. Chem. 65, 9478–9492 (2022). https://doi.org/10.1021/acs.jmedchem.2c00732

Article  CAS  PubMed  Google Scholar 

J. Arús-Pous, A. Patronov, E.J. Bjerrum, C. Tyrchan, J.-L. Reymond, H. Chen, O. Engkvist, SMILES-based deep generative scaffold decorator for de-novo drug design. J. Cheminform. 12, 38 (2020). https://doi.org/10.1186/s13321-020-00441-8

Article  CAS  PubMed Central  PubMed  Google Scholar 

N. De Cao, T. Kipf, MolGAN: An implicit generative model for small molecular graphs, ArXiv abs/1805.1 (2018) null. https://www.semanticscholar.org/paper/def1049b5aae96c8e1eab0ca58d77ac9c2f0e3e9

W. Tang, Y. Li, Y. Yu, Z. Wang, T. Xu, J. Chen, J. Lin, X. Li, Development of models predicting biodegradation rate rating with multiple linear regression and support vector machine algorithms. Chemosphere 253, 126666 (2020). https://doi.org/10.1016/j.chemosphere.2020.126666

Article  CAS  PubMed  Google Scholar 

O. Dollar, N. Joshi, D.A.C. Beck, J. Pfaendtner, Attention-based generative models for de novo molecular design. Chem. Sci. 12, 8362–8372 (2021). https://doi.org/10.1039/d1sc01050f

Article  CAS  PubMed Central  PubMed  Google Scholar 

F. Lunghini, G. Marcou, P. Gantzer, P. Azam, D. Horvath, E. Van Miert, A. Varnek, Modelling of ready biodegradability based on combined public and industrial data sources. SAR QSAR Environ. Res. 31, 171–186 (2019). https://doi.org/10.1080/1062936x.2019.1697360

Article  CAS  PubMed  Google Scholar 

W.F.C. Rocha, D.A. Sheen, Classification of biodegradable materials using QSAR modelling with uncertainty estimation. SAR QSAR Environ. Res. 27, 799–811 (2016). https://doi.org/10.1080/1062936X.2016.1238010

Article  CAS  PubMed  Google Scholar 

K. Acharya, D. Werner, J. Dolfing, M. Barycki, P. Meynet, W. Mrozik, O. Komolafe, T. Puzyn, R.J. Davenport, A quantitative structure-biodegradation relationship (QSBR) approach to predict biodegradation rates of aromatic chemicals. Water Res. 157, 181–190 (2019). https://doi.org/10.1016/j.watres.2019.03.086

Article  CAS  PubMed  Google Scholar 

B. Barros, P. Lacerda, C. Albuquerque, A. Conci, Pulmonary COVID-19: learning spatiotemporal features combining CNN and LSTM networks for lung ultrasound video classification. Sensors (Basel) (2021). https://doi.org/10.3390/S21165486

Article  PubMed  Google Scholar 

H.V. Dang, H. Tran-Ngoc, T.V. Nguyen, T. Bui-Tien, G. De Roeck, H.X. Nguyen, Data-driven structural health monitoring using feature fusion and hybrid deep learning. IEEE Trans. Autom. Sci. Eng. 18, 2087–2103 (2021). https://doi.org/10.1109/TASE.2020.3034401

Article  Google Scholar 

P. Bilokon, Y. Qiu, Transformers versus LSTMs for electronic trading. SSRN Electron. J. (2023). https://doi.org/10.2139/ssrn.4577922

Article  Google Scholar 

Y. Tatsunami, M. Taki, Sequencer: Deep LSTM for Image Classification, Adv Neural Inf Process Syst 35 (2022). https://arxiv.org/abs/2205.01972v4. Accessed 29 Apr 2024

A. Zeyer, P. Bahar, K. Irie, R. Schluter, H. Ney, A Comparison of Transformer and LSTM Encoder Decoder Models for ASR, 2019 IEEE Automatic Speech Recognition and Understanding Workshop, ASRU 2019 - Proceedings (2019) 8–15. https://doi.org/10.1109/ASRU46091.2019.9004025

R.T.B.D.T.R. Mansouri Kamel, V. Consonni, QSAR biodegradation, (2013)

P. Dey, S.K. Chaulya, S. Kumar, Hybrid CNN-LSTM and IoT-based coal mine hazards monitoring and prediction system. Process. Saf. Environ. Prot. 152, 249–263 (2021). https://doi.org/10.1016/J.PSEP.2021.06.005

Article  CAS  Google Scholar 

Y. Zhao, Improvement and application of multi-layer LSTM Algorithm based on spatial-temporal correlation. Ingénierie Des Systèmes d Inf. 25 (2020) null. https://doi.org/10.18280/isi.250107

C. Ding, G. Wang, X. Zhang, Q. Liu, X. Liu, A hybrid CNN-LSTM model for predicting PM2.5 in Beijing based on spatiotemporal correlation. Environ. Ecol. Stat. 28, 503–522 (2021). https://doi.org/10.1007/s10651-021-00501-8

Article  CAS  Google Scholar 

D.Q. Gbadago, J. Moon, M. Kim, S. Hwang, A unified framework for the mathematical modelling, predictive analysis, and optimization of reaction systems using computational fluid dynamics, deep neural network and genetic algorithm: a case of butadiene synthesis. Chem. Eng. J. 409, 128163 (2021). https://doi.org/10.1016/j.cej.2020.128163

Article  CAS  Google Scholar 

J. Moon, D.Q. Gbadago, G. Hwang, D. Lee, S. Hwang, Software platform for high-fidelity-data-based artificial neural network modeling and process optimization in chemical engineering. Comput. Chem. Eng. 158, 107637 (2022). https://doi.org/10.1016/J.COMPCHEMENG.2021.107637

Article  CAS  Google Scholar 

P. Dey, K. Saurabh, C. Kumar, D. Pandit, S.K. Chaulya, S. Ray, G.M. Prasad, S.K. Mandal, t-SNE and variational auto-encoder with a bi-LSTM neural network-based model for prediction of gas concentration in a sealed-off area of underground coal mines. Soft. Comput. 25, 14183–14207 (2021). https://doi.org/10.1007/s00500-021-06261-8

Article  Google Scholar 

W. Wang, A Pre-trained Conditional Transformer for Target-specific De Novo Molecular Generation, (2022). https://www.semanticscholar.org/paper/ed9763062daec0eec7ceb65e822360e340c75605

X. Yang, Z. Zhang, An attention-based domain spatial-temporal meta-learning (ADST-ML) approach for PM2.5 concentration dynamics prediction. Urban Clim. (2023). https://doi.org/10.1016/j.uclim.2022.101363

Article  PubMed Central  PubMed  Google Scholar 

N. Xu, X. Wang, X. Meng, H. Chang, Gas concentration prediction based on IWOA-LSTM-CEEMDAN residual correction model. Sensors (Basel) (2022). https://doi.org/10.3390/s22124412

Article  PubMed Central  PubMed  Google Scholar 

L. Pingyang, N. Chen, M. Shanjun, L. Mei, LSTM based encoder-decoder for short-term predictions of gas concentration using multi-sensor fusion. Process. Saf. Environ. Prot. 137, 93–105 (2020). https://doi.org/10.1016/j.psep.2020.02.021

Article  CAS  Google Scholar 

K. Kumari, P. Dey, C. Kumar, D. Pandit, S. Mishra, V. Kisku, S.K. Chaulya, S. Ray, G.M. Prasad, UMAP and LSTM based fire status and explosibility prediction for sealed-off area in underground coal mine. Process. Saf. Environ. Prot. 146, 837–852 (2021). https://doi.org/10.1016/j.psep.2020.12.019

Article  CAS  Google Scholar 

M. Popova, O. Isayev, A. Tropsha, Deep reinforcement learning for de novo drug design. Sci. Adv. 4, eaap7885 (2018). https://doi.org/10.1126/sciadv.aap7885

Article  CAS  PubMed Central  PubMed