{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,5,14]],"date-time":"2026-05-14T00:10:27Z","timestamp":1778717427250,"version":"3.51.4"},"reference-count":70,"publisher":"Elsevier BV","license":[{"start":{"date-parts":[[2026,8,1]],"date-time":"2026-08-01T00:00:00Z","timestamp":1785542400000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.elsevier.com\/tdm\/userlicense\/1.0\/"},{"start":{"date-parts":[[2026,8,1]],"date-time":"2026-08-01T00:00:00Z","timestamp":1785542400000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.elsevier.com\/legal\/tdmrep-license"},{"start":{"date-parts":[[2026,8,1]],"date-time":"2026-08-01T00:00:00Z","timestamp":1785542400000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-017"},{"start":{"date-parts":[[2026,8,1]],"date-time":"2026-08-01T00:00:00Z","timestamp":1785542400000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-037"},{"start":{"date-parts":[[2026,8,1]],"date-time":"2026-08-01T00:00:00Z","timestamp":1785542400000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-012"},{"start":{"date-parts":[[2026,8,1]],"date-time":"2026-08-01T00:00:00Z","timestamp":1785542400000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-029"},{"start":{"date-parts":[[2026,8,1]],"date-time":"2026-08-01T00:00:00Z","timestamp":1785542400000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-004"}],"funder":[{"DOI":"10.13039\/501100013804","name":"Fundamental Research Funds for the Central Universities","doi-asserted-by":"publisher","id":[{"id":"10.13039\/501100013804","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"publisher","id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":["elsevier.com","sciencedirect.com"],"crossmark-restriction":true},"short-container-title":["Computers &amp; Chemical Engineering"],"published-print":{"date-parts":[[2026,8]]},"DOI":"10.1016\/j.compchemeng.2026.109670","type":"journal-article","created":{"date-parts":[[2026,4,15]],"date-time":"2026-04-15T20:26:40Z","timestamp":1776284800000},"page":"109670","update-policy":"https:\/\/doi.org\/10.1016\/elsevier_cm_policy","source":"Crossref","is-referenced-by-count":0,"special_numbering":"C","title":["TAL-GA: A task-adaptive and lightweight generative architecture for multi-property molecular design in drug discovery"],"prefix":"10.1016","volume":"211","author":[{"given":"Zhengtao","family":"Zhou","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Quanhu","family":"Sun","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Bohao","family":"Li","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-4536-9816","authenticated-orcid":false,"given":"Yan","family":"Chen","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Jingzheng","family":"Ren","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Weifeng","family":"Shen","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"78","reference":[{"key":"10.1016\/j.compchemeng.2026.109670_bib0001","doi-asserted-by":"crossref","unstructured":"Akiba, T., Sano, S., Yanase, T., Ohta, T., & Koyama, M. (2019). Optuna: a next-generation hyperparameter optimization framework.","DOI":"10.1145\/3292500.3330701"},{"key":"10.1016\/j.compchemeng.2026.109670_bib0002","doi-asserted-by":"crossref","first-page":"14047","DOI":"10.1021\/acs.jmedchem.3c01083","article-title":"Prediction of small-molecule developability using large-scale In Silico ADMET models","volume":"66","author":"Beckers","year":"2023","journal-title":"J. Med. Chem."},{"key":"10.1016\/j.compchemeng.2026.109670_bib0003","author":"Benhenda"},{"key":"10.1016\/j.compchemeng.2026.109670_bib0004","doi-asserted-by":"crossref","first-page":"277","DOI":"10.1007\/s40262-022-01194-3","article-title":"Physiologically based pharmacokinetic modelling to identify physiological and drug parameters driving pharmacokinetics in obese individuals","volume":"62","author":"Berton","year":"2023","journal-title":"Clin. Pharmacokinet."},{"key":"10.1016\/j.compchemeng.2026.109670_bib0005","first-page":"202","article-title":"Integrated design of solvent\u2013antisolvent mixtures and crystallization processes powered by machine learning","author":"Bosetti","year":"2025","journal-title":"Comput. Chem. Eng."},{"key":"10.1016\/j.compchemeng.2026.109670_bib0006","doi-asserted-by":"crossref","first-page":"1411","DOI":"10.1016\/j.ins.2010.11.036","article-title":"On convergence of the multi-objective particle swarm optimizers","volume":"181","author":"Chakraborty","year":"2011","journal-title":"Inf. Sci."},{"key":"10.1016\/j.compchemeng.2026.109670_bib0007","doi-asserted-by":"crossref","DOI":"10.1016\/j.compchemeng.2024.108626","article-title":"A virtual screening framework based on the binding site selectivity for small molecule drug discovery","volume":"184","author":"Che","year":"2024","journal-title":"Comput. Chem. Eng."},{"key":"10.1016\/j.compchemeng.2026.109670_bib0008","doi-asserted-by":"crossref","first-page":"541","DOI":"10.1016\/j.ins.2015.07.018","article-title":"A new multi-objective particle swarm optimization algorithm based on decomposition","volume":"325","author":"Dai","year":"2015","journal-title":"Inf. Sci."},{"key":"10.1016\/j.compchemeng.2026.109670_bib0009","doi-asserted-by":"crossref","first-page":"16838","DOI":"10.1021\/acs.jmedchem.1c01683","article-title":"Active learning for drug design: a case study on the plasma exposure of orally administered drugs","volume":"64","author":"Ding","year":"2021","journal-title":"J. Med. Chem."},{"key":"10.1016\/j.compchemeng.2026.109670_bib0010","first-page":"4","article-title":"Deep Learning for molecular design\u2014a review of the State of the art","author":"Elton","year":"2019","journal-title":"Mol. Syst. Des. Eng."},{"key":"10.1016\/j.compchemeng.2026.109670_bib0011","doi-asserted-by":"crossref","first-page":"8","DOI":"10.1186\/1758-2946-1-8","article-title":"Estimation of synthetic accessibility score of drug-like molecules based on molecular complexity and fragment contributions","volume":"1","author":"Ertl","year":"2009","journal-title":"J. Cheminform."},{"key":"10.1016\/j.compchemeng.2026.109670_bib0012","unstructured":"Goldberg, Y., & Levy, O. (2014). word2vec Explained: deriving Mikolov et al.'s negative-sampling word-embedding method. arXiv preprint arXiv:1402.3722."},{"key":"10.1016\/j.compchemeng.2026.109670_bib0013","doi-asserted-by":"crossref","first-page":"268","DOI":"10.1021\/acscentsci.7b00572","article-title":"Automatic chemical design using a data-driven continuous representation of molecules","volume":"4","author":"G\u00f3mez-Bombarelli","year":"2018","journal-title":"ACS. Cent. Sci."},{"key":"10.1016\/j.compchemeng.2026.109670_bib0014","first-page":"2672","article-title":"Generative adversarial nets","volume":"3","author":"Goodfellow","year":"2014","journal-title":"Adv. Neural Inf. Process. Syst."},{"key":"10.1016\/j.compchemeng.2026.109670_bib0015","doi-asserted-by":"crossref","first-page":"577","DOI":"10.1039\/C9SC04026A","article-title":"Constrained bayesian optimization for automatic chemical design using variational autoencoders","volume":"11","author":"Griffiths","year":"2020","journal-title":"Chem. Sci."},{"key":"10.1016\/j.compchemeng.2026.109670_bib0016","doi-asserted-by":"crossref","DOI":"10.1126\/sciadv.abg3338","article-title":"Combining generative artificial intelligence and on-chip synthesis for de novo drug design","volume":"7","author":"Grisoni","year":"2021","journal-title":"Sci. Adv."},{"key":"10.1016\/j.compchemeng.2026.109670_bib0017","doi-asserted-by":"crossref","DOI":"10.1016\/j.compchemeng.2024.108750","article-title":"Graph neural networks for CO2 solubility predictions in Deep eutectic Solvents","volume":"187","author":"Hern\u00e1ndez Morales","year":"2024","journal-title":"Comput. Chem. Eng."},{"key":"10.1016\/j.compchemeng.2026.109670_bib0018","series-title":"Proceedings of the 31st International Conference on Neural Information Processing Systems","first-page":"2604","article-title":"Predicting organic reaction outcomes with Weisfeiler-Lehman network","author":"Jin","year":"2017"},{"key":"10.1016\/j.compchemeng.2026.109670_bib0019","doi-asserted-by":"crossref","first-page":"3098","DOI":"10.1021\/acs.molpharmaceut.7b00346","article-title":"druGAN: an advanced generative adversarial autoencoder model for de Novo generation of new molecules with desired molecular properties in Silico","volume":"14","author":"Kadurin","year":"2017","journal-title":"Mol. Pharm."},{"key":"10.1016\/j.compchemeng.2026.109670_bib0020","doi-asserted-by":"crossref","first-page":"43","DOI":"10.1021\/acs.jcim.8b00263","article-title":"Conditional molecular design with deep generative models","volume":"59","author":"Kang","year":"2019","journal-title":"J. Chem. Inf. Model."},{"key":"10.1016\/j.compchemeng.2026.109670_bib0021","series-title":"Auto-encoding variational bayes","author":"Kingma","year":"2013"},{"key":"10.1016\/j.compchemeng.2026.109670_bib0022","series-title":"2nd International Conference on Learning Representations, ICLR 2014 - Conference Track Proceedings","article-title":"Auto-encoding variational bayes","author":"Kingma","year":"2014"},{"key":"10.1016\/j.compchemeng.2026.109670_bib0023","doi-asserted-by":"crossref","first-page":"254","DOI":"10.1038\/s42256-020-0174-5","article-title":"Direct steering of de novo molecular generation with descriptor conditional recurrent neural networks","volume":"2","author":"Kotsias","year":"2020","journal-title":"Nat. Mach. Intell."},{"key":"10.1016\/j.compchemeng.2026.109670_bib0024","series-title":"Proceedings of the 2018 Conference on Empirical Methods in Natural Language Processing: System Demonstrations","first-page":"66","article-title":"SentencePiece: a simple and language independent subword tokenizer and detokenizer for neural text processing","author":"Kudo","year":"2018"},{"issue":"79\u201386","key":"10.1016\/j.compchemeng.2026.109670_bib0025","first-page":"78","article-title":"On information and sufficiency","volume":"22","author":"Kullback","year":"1951","journal-title":"Ann. Math. Stat."},{"key":"10.1016\/j.compchemeng.2026.109670_bib0026","unstructured":"Landrum, G. (2019). RDKit: open-source cheminformatics software (version 2021.09.1). In."},{"key":"10.1016\/j.compchemeng.2026.109670_bib0027","doi-asserted-by":"crossref","first-page":"436","DOI":"10.1038\/nature14539","article-title":"Deep learning","volume":"521","author":"LeCun","year":"2015","journal-title":"Nature"},{"key":"10.1016\/j.compchemeng.2026.109670_bib0028","first-page":"10","article-title":"Deep learning for complex chemical systems","author":"Li","year":"2023","journal-title":"Natl. Sci. Rev."},{"key":"10.1016\/j.compchemeng.2026.109670_bib0029","doi-asserted-by":"crossref","DOI":"10.1016\/j.drudis.2022.103373","article-title":"Deep learning methods for molecular representation and property prediction","volume":"27","author":"Li","year":"2022","journal-title":"Drug Discov. Today"},{"key":"10.1016\/j.compchemeng.2026.109670_bib0030","doi-asserted-by":"crossref","first-page":"10370","DOI":"10.1002\/anie.201504018","article-title":"Multidimensional design of anticancer peptides","volume":"54","author":"Lin","year":"2015","journal-title":"Angew. Chem. Int. Ed."},{"key":"10.1016\/j.compchemeng.2026.109670_bib0031","doi-asserted-by":"crossref","first-page":"3","DOI":"10.1016\/S0169-409X(00)00129-0","article-title":"Experimental and computational approaches to estimate solubility and permeability in drug discovery and development settings","volume":"46","author":"Lipinski","year":"2001","journal-title":"Adv. Drug Deliv. Rev."},{"key":"10.1016\/j.compchemeng.2026.109670_bib0032","first-page":"8065","article-title":"Graph diffusion transformers for multi-conditional molecular generation","volume":"37","author":"Liu","year":"2024","journal-title":"Adv. Neural Inf. Process. Syst."},{"key":"10.1016\/j.compchemeng.2026.109670_bib0033","doi-asserted-by":"crossref","DOI":"10.1016\/j.matdes.2022.110888","article-title":"Data-driven multi-objective molecular design of ionic liquid with high generation efficiency on small dataset","volume":"220","author":"Liu","year":"2022","journal-title":"Mater. Des."},{"key":"10.1016\/j.compchemeng.2026.109670_bib0034","doi-asserted-by":"crossref","DOI":"10.1016\/j.fluid.2022.113531","article-title":"Hybrid, interpretable machine learning for thermodynamic property estimation using Grammar2vec for molecular representation","volume":"561","author":"Mann","year":"2022","journal-title":"Fluid Ph Equilib."},{"key":"10.1016\/j.compchemeng.2026.109670_bib0035","doi-asserted-by":"crossref","DOI":"10.1016\/j.fluid.2023.113734","article-title":"Group contribution-based property modeling for chemical product design: a perspective in the AI era","volume":"568","author":"Mann","year":"2023","journal-title":"Fluid Ph Equilib."},{"key":"10.1016\/j.compchemeng.2026.109670_bib0036","article-title":"Distributed representations ofwords and phrases and their compositionality","author":"Mikolov","year":"2013","journal-title":"Adv. Neural Inf. Process. Syst."},{"key":"10.1016\/j.compchemeng.2026.109670_bib0037","doi-asserted-by":"crossref","first-page":"6313","DOI":"10.1021\/acs.iecr.4c00401","article-title":"Surfactant-specific AI-driven molecular design: integrating generative models, predictive modeling, and reinforcement learning for tailored Surfactant synthesis","volume":"63","author":"Nnadili","year":"2024","journal-title":"Ind. Eng. Chem. Res."},{"key":"10.1016\/j.compchemeng.2026.109670_bib0038","doi-asserted-by":"crossref","first-page":"18860","DOI":"10.1002\/anie.202008366","article-title":"Molecular machine learning: the future of synthetic chemistry?","volume":"59","author":"Pfl\u00fcger","year":"2020","journal-title":"Angew Chem. Int. Ed."},{"key":"10.1016\/j.compchemeng.2026.109670_bib0039","first-page":"11","article-title":"Molecular sets (MOSES): a benchmarking platform for Molecular generation models","author":"Polykovskiy","year":"2020","journal-title":"Front. Pharmacol."},{"key":"10.1016\/j.compchemeng.2026.109670_bib0040","doi-asserted-by":"crossref","first-page":"21","DOI":"10.2174\/092986709787002817","article-title":"Topological polar surface area: a useful descriptor in 2D-QSAR","volume":"16","author":"Prasanna","year":"2009","journal-title":"Curr. Med. Chem."},{"key":"10.1016\/j.compchemeng.2026.109670_bib0041","doi-asserted-by":"crossref","first-page":"74","DOI":"10.1186\/s13321-019-0397-9","article-title":"A de novo molecular generation method using latent vector based generative adversarial network","volume":"11","author":"Prykhodko","year":"2019","journal-title":"J. Cheminform."},{"key":"10.1016\/j.compchemeng.2026.109670_bib0042","first-page":"360","article-title":"Inverse molecular design using machine learning: generative models for matter engineering","volume":"361","author":"Sanchez-Lengeling","year":"2018","journal-title":"Science (1979)"},{"key":"10.1016\/j.compchemeng.2026.109670_bib0043","doi-asserted-by":"crossref","DOI":"10.1002\/anie.202415056","article-title":"Inverse design of singlet-fission materials with uncertainty-controlled genetic optimization","volume":"64","author":"Schaufelberger","year":"2025","journal-title":"Angew. Chem. Int. Ed."},{"key":"10.1016\/j.compchemeng.2026.109670_bib0044","doi-asserted-by":"crossref","first-page":"120","DOI":"10.1021\/acscentsci.7b00512","article-title":"Generating focused molecule libraries for drug discovery with recurrent neural networks","volume":"4","author":"Segler","year":"2018","journal-title":"ACS. Cent. Sci."},{"key":"10.1016\/j.compchemeng.2026.109670_bib0045","doi-asserted-by":"crossref","first-page":"8667","DOI":"10.1021\/acs.jmedchem.9b02120","article-title":"Current and future roles of artificial intelligence in medicinal chemistry synthesis","volume":"63","author":"Struble","year":"2020","journal-title":"J. Med. Chem."},{"key":"10.1016\/j.compchemeng.2026.109670_bib0046","series-title":"26th International Conference on Artificial Intelligence and Statistics (AISTATS) (Vol. 206)","article-title":"Bounding evidence and estimating log-likelihood in VAE","author":"Struski","year":"2023"},{"key":"10.1016\/j.compchemeng.2026.109670_bib0047","doi-asserted-by":"crossref","DOI":"10.1002\/aic.16678","article-title":"An architecture of deep learning in QSPR modeling for the prediction of critical properties using molecular signatures","volume":"65","author":"Su","year":"2019","journal-title":"AIChe J."},{"key":"10.1016\/j.compchemeng.2026.109670_bib0048","doi-asserted-by":"crossref","DOI":"10.1126\/sciadv.aay4275","article-title":"Machine learning\u2013assisted molecular design and efficiency prediction for high-performance organic photovoltaic materials","volume":"5","author":"Sun","year":"2019","journal-title":"Sci. Adv."},{"key":"10.1016\/j.compchemeng.2026.109670_bib0049","doi-asserted-by":"crossref","DOI":"10.1088\/2632-2153\/aca23d","article-title":"Self-supervised learning of materials concepts from crystal structures via deep neural networks","volume":"3","author":"Suzuki","year":"2022","journal-title":"Mach. Learn. Sci. Technol."},{"key":"10.1016\/j.compchemeng.2026.109670_bib0050","doi-asserted-by":"crossref","first-page":"14011","DOI":"10.1021\/acs.jmedchem.1c00927","article-title":"Generative models for De Novo drug design","volume":"64","author":"Tong","year":"2021","journal-title":"J. Med. Chem."},{"key":"10.1016\/j.compchemeng.2026.109670_bib0051","doi-asserted-by":"crossref","DOI":"10.1016\/j.compchemeng.2023.108392","article-title":"Computer aided molecular design coupled to deep learning techniques as a less-expensive approach to design organic photoredox catalysts","volume":"178","author":"Valencia-Marquez","year":"2023","journal-title":"Comput. Chem. Eng."},{"key":"10.1016\/j.compchemeng.2026.109670_bib0052","doi-asserted-by":"crossref","first-page":"2615","DOI":"10.1021\/jm020017n","article-title":"Molecular properties that influence the oral bioavailability of drug candidates","volume":"45","author":"Veber","year":"2002","journal-title":"J. Med. Chem."},{"key":"10.1016\/j.compchemeng.2026.109670_bib0053","doi-asserted-by":"crossref","first-page":"60","DOI":"10.1186\/s13321-022-00638-z","article-title":"From theory to experiment: transformer-based generation enables rapid discovery of novel reactions","volume":"14","author":"Wang","year":"2022","journal-title":"J. Cheminform."},{"key":"10.1016\/j.compchemeng.2026.109670_bib0054","doi-asserted-by":"crossref","first-page":"3867","DOI":"10.1039\/D0GC01122C","article-title":"A novel unambiguous strategy of molecular feature extraction in machine learning assisted predictive models for environmental properties","volume":"22","author":"Wang","year":"2020","journal-title":"Green. Chem."},{"key":"10.1016\/j.compchemeng.2026.109670_bib0055","doi-asserted-by":"crossref","DOI":"10.1016\/j.compchemeng.2022.107739","article-title":"Identification of optimal metal-organic frameworks by machine learning: structure decomposition, feature integration, and predictive modeling","volume":"160","author":"Wang","year":"2022","journal-title":"Comput. Chem. Eng."},{"key":"10.1016\/j.compchemeng.2026.109670_bib0056","doi-asserted-by":"crossref","first-page":"31","DOI":"10.1021\/ci00057a005","article-title":"SMILES, a chemical language and information system. 1. Introduction to methodology and encoding rules","volume":"28","author":"Weininger","year":"1988","journal-title":"J. Chem. Inf. Comput. Sci."},{"key":"10.1016\/j.compchemeng.2026.109670_bib0057","doi-asserted-by":"crossref","first-page":"20473","DOI":"10.1021\/acs.iecr.3c02305","article-title":"A systematic review on intensifications of artificial intelligence assisted green solvent development","volume":"62","author":"Wen","year":"2023","journal-title":"Ind. Eng. Chem. Res."},{"key":"10.1016\/j.compchemeng.2026.109670_bib0058","doi-asserted-by":"crossref","first-page":"868","DOI":"10.1021\/ci990307l","article-title":"Prediction of physicochemical parameters by atomic contributions","volume":"39","author":"Wildman","year":"1999","journal-title":"J. Chem. Inf. Comput. Sci."},{"key":"10.1016\/j.compchemeng.2026.109670_bib0059","doi-asserted-by":"crossref","first-page":"8016","DOI":"10.1039\/C9SC01928F","article-title":"Efficient multi-objective molecular optimization in a continuous latent space","volume":"10","author":"Winter","year":"2019","journal-title":"Chem. Sci."},{"key":"10.1016\/j.compchemeng.2026.109670_bib0060","doi-asserted-by":"crossref","DOI":"10.1002\/aic.18741","article-title":"Machine learning potential model for accelerating quantum chemistry-driven property prediction and molecular design","volume":"71","author":"Wu","year":"2025","journal-title":"AIChe J."},{"key":"10.1016\/j.compchemeng.2026.109670_bib0061","first-page":"196","article-title":"ChemBERTa embeddings and ensemble learning for prediction of density and melting point of deep eutectic solvents with hybrid features","author":"Wu","year":"2025","journal-title":"Comput. Chem. Eng."},{"key":"10.1016\/j.compchemeng.2026.109670_bib0062","article-title":"DiffMC-gen: a dual denoising diffusion model for multi-conditional molecular generation","volume":"12","author":"Yang","year":"2025","journal-title":"Adv. Sci."},{"key":"10.1016\/j.compchemeng.2026.109670_bib0063","doi-asserted-by":"crossref","DOI":"10.1016\/j.cej.2023.142768","article-title":"Quantitative structure-property relationship (QSPR) framework assists in rapid mining of highly thermostable polyimides","volume":"465","author":"Yu","year":"2023","journal-title":"Chem. Eng. J."},{"key":"10.1016\/j.compchemeng.2026.109670_bib0064","doi-asserted-by":"crossref","DOI":"10.1016\/j.compchemeng.2023.108335","article-title":"A deep learning-based framework towards inverse green solvent design for extractive distillation with multi-index constraints","volume":"177","author":"Zhang","year":"2023","journal-title":"Comput. Chem. Eng."},{"key":"10.1016\/j.compchemeng.2026.109670_bib0065","doi-asserted-by":"crossref","DOI":"10.1016\/j.ces.2022.117624","article-title":"Message-passing neural network based multi-task deep-learning framework for COSMO-SAC based \u03c3-profile and VCOSMO prediction","volume":"254","author":"Zhang","year":"2022","journal-title":"Chem. Eng. Sci."},{"key":"10.1016\/j.compchemeng.2026.109670_bib0066","doi-asserted-by":"crossref","first-page":"706","DOI":"10.1021\/acs.jmedchem.4c02668","article-title":"F-CPI: a multimodal deep learning approach for predicting compound bioactivity changes induced by fluorine substitution","volume":"68","author":"Zhang","year":"2025","journal-title":"J. Med. Chem."},{"key":"10.1016\/j.compchemeng.2026.109670_bib0067","first-page":"714","article-title":"Intelligent particle swarm optimization in multiobjective optimization","volume":"1","author":"Zhang","year":"2005","journal-title":"IEEE Proc. Evol. Comput."},{"key":"10.1016\/j.compchemeng.2026.109670_bib0068","doi-asserted-by":"crossref","first-page":"1038","DOI":"10.1038\/s41587-019-0224-x","article-title":"Deep learning enables rapid identification of potent DDR1 kinase inhibitors","volume":"37","author":"Zhavoronkov","year":"2019","journal-title":"Nat. Biotechnol."},{"key":"10.1016\/j.compchemeng.2026.109670_bib0069","doi-asserted-by":"crossref","DOI":"10.1002\/adma.202302530","article-title":"Deep learning in mechanical metamaterials: from prediction and generation to inverse design","volume":"35","author":"Zheng","year":"2023","journal-title":"Adv. Mater."},{"key":"10.1016\/j.compchemeng.2026.109670_bib0070","doi-asserted-by":"crossref","first-page":"5336","DOI":"10.1021\/acs.iecr.2c04070","article-title":"Treat molecular linear notations as sentences: accurate quantitative structure-property relationship modeling via a natural language processing approach","volume":"62","author":"Zhou","year":"2023","journal-title":"Ind. Eng. Chem. Res."}],"container-title":["Computers &amp; Chemical Engineering"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/api.elsevier.com\/content\/article\/PII:S0098135426001237?httpAccept=text\/xml","content-type":"text\/xml","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/api.elsevier.com\/content\/article\/PII:S0098135426001237?httpAccept=text\/plain","content-type":"text\/plain","content-version":"vor","intended-application":"text-mining"}],"deposited":{"date-parts":[[2026,5,13]],"date-time":"2026-05-13T23:28:52Z","timestamp":1778714932000},"score":1,"resource":{"primary":{"URL":"https:\/\/linkinghub.elsevier.com\/retrieve\/pii\/S0098135426001237"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2026,8]]},"references-count":70,"alternative-id":["S0098135426001237"],"URL":"https:\/\/doi.org\/10.1016\/j.compchemeng.2026.109670","relation":{},"ISSN":["0098-1354"],"issn-type":[{"value":"0098-1354","type":"print"}],"subject":[],"published":{"date-parts":[[2026,8]]},"assertion":[{"value":"Elsevier","name":"publisher","label":"This article is maintained by"},{"value":"TAL-GA: A task-adaptive and lightweight generative architecture for multi-property molecular design in drug discovery","name":"articletitle","label":"Article Title"},{"value":"Computers & Chemical Engineering","name":"journaltitle","label":"Journal Title"},{"value":"https:\/\/doi.org\/10.1016\/j.compchemeng.2026.109670","name":"articlelink","label":"CrossRef DOI link to publisher maintained version"},{"value":"article","name":"content_type","label":"Content Type"},{"value":"\u00a9 2026 Elsevier Ltd. All rights are reserved, including those for text and data mining, AI training, and similar technologies.","name":"copyright","label":"Copyright"}],"article-number":"109670"}}