Abstract
Advances in drug discovery and clinical research have shifted the bottleneck in medicines development to chemistry, manufacturing, and controls activities, a critically step for regulatory approval. This includes formulation and process development of a new drug product, which traditionally requires extensive resources, often leading to suboptimal outcomes. These development processes must adapt to follow the advances in drug discovery and clinical research and ultimately shorten timelines while ensuring product quality and safety. In this work, we present an integrated platform for tablet formulation and process development that couples a digital formulator, an in-silico optimisation tool using a predictive material-to-tablet model, with a self-driving tableting data factory, which applies Bayesian optimisation within an automated, fully integrated per-tablet manufacturing to testing workflow. The results demonstrate a reduction in the time from material characterisation to in-specification tablets to 6 h and a reduction in API material use by 65% compared to current state-of-the-art methods.
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The data that support the findings of this study are available from the Source Repository124 and are from the corresponding authors upon request. Source data are provided in this paper.
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References
Weissler, E. H. et al. The role of machine learning in clinical research: transforming the future of evidence generation. Trials 22, 537 (2021).
Alowais, S. A. et al. Revolutionizing healthcare: the role of artificial intelligence in clinical practice. BMC Med. Educ. 23, 689 (2023).
Jayatunga, M. K., Xie, W., Ruder, L., Schulze, U. & Meier, C. AI in small-molecule drug discovery: a coming wave. Nat. Rev. Drug Discov. 21, 175–176 (2022).
Madhukar, N. S. et al. A Bayesian machine learning approach for drug target identification using diverse data types. Nat. Commun. 10, 5221 (2019).
Tripathi, N., Goshisht, M. K., Sahu, S. K. & Arora, C. Applications of artificial intelligence to drug design and discovery in the big data era: a comprehensive review. Mol. Divers. 25, 1643–1664 (2021).
Tropsha, A., Isayev, O., Varnek, A., Schneider, G. & Cherkasov, A. Integrating QSAR modelling and deep learning in drug discovery: the emergence of deep QSAR. Nat. Rev. Drug Discov. 23, 141–155 (2024).
Schneider, G. Automating drug discovery. Nat. Rev. Drug Discov. 17, 97–113 (2018).
Zhao, Y., Zeng, D., Socinski, M. A. & Kosorok, M. R. Reinforcement learning strategies for clinical trials in non-small cell lung cancer. Biometrics 67, 1422–1433 (2011).
Glicksberg, B. S. et al. Automated disease cohort selection using word embeddings from Electronic Health Records. Pac. Symp. Biocomput. 23, 145–156 (2018).
Lee, H. et al. Development of a system to support warfarin dose decisions using deep neural networks. Sci. Rep. 11, 14745 (2021).
Blasiak, A. et al. PRECISE CURATE.AI: A prospective feasibility trial to dynamically modulate personalized chemotherapy dose with artificial intelligence. J. Clin. Oncol. 40, 1574 (2022).
Martin, G. L. et al. Validation of artificial intelligence to support the automatic coding of patient adverse drug reaction reports, using nationwide pharmacovigilance data. Drug Saf. 45, 535–548 (2022).
Subbiah, V. The next generation of evidence-based medicine. Nat. Med. 29, 49–58 (2023).
Kapoor, Y. et al. Flexibility in drug product development: a perspective. Mol. Pharm. 18, 2455–2469 (2021).
Harnett, A., Byrne, S., O’Connor, J., Lyons, D. & Sahm, L. J. Adult patients with difficulty swallowing oral dosage forms: a systematic review of the quantitative literature. Pharmacy 11, 167 (2023).
De la Torre, B. G. & Albericio, F. The pharmaceutical industry in 2023: An analysis of FDA drug approvals from the perspective of molecules. Molecules 29, 585 (2024).
Mullard, A. 2023 FDA approvals. Nat. Rev. Drug Discov. 23, 88–95 (2024).
Leane, M., Pitt, K. & Reynolds, G. Manufacturing classification system (MCS) working group. A proposal for a drug product Manufacturing Classification System (MCS) for oral solid dosage forms. Pharm. Dev. Technol. 20, 12–21 (2015).
Megarry, A. J., Swainson, S. M., Roberts, R. J. & Reynolds, G. K. A big data approach to pharmaceutical flow properties. Int. J. Pharm. 555, 337–345 (2019).
Jivraj, M., Martini, L. G. & Thomson, C. M. An overview of the different excipients useful for the direct compression of tablets. Pharm. Sci. Technol. Today 3, 58–63 (2000).
Sun, C. C. Mechanism of moisture induced variations in true density and compaction properties of microcrystalline cellulose. Int. J. Pharm. 346, 93–101 (2008).
Leane, M. Manufacturing classification system (MCS): enabling better understanding of oral solid dosage formulation design. Pharm. Dev. Technol. 29, 1–3 (2024).
Leane, M. et al. Ten years of the manufacturing classification system: A review of literature applications and an extension of the framework to continuous manufacture. Pharm. Dev. Technol. 29, 369–388 (2024).
Kawakita, K. & Lüdde, K. H. Some considerations on powder compression equations. Powder Technol. 4, 61–68 (1971).
Kuentz, M. & Leuenberger, H. A new theoretical approach to tablet strength of a binary mixture consisting of a well and a poorly compactable substance. Eur. J. Pharm. Biopharm. 49, 151–159 (2000).
White, L. R., Molloy, M., Shaw, R. J. & Reynolds, G. K. System model driven selection of robust tablet manufacturing processes based on drug loading and formulation physical attributes. Eur. J. Pharm. Sci. 172, 106140 (2022).
Tait, T. et al. Empirical model variability: Developing a new global optimisation approach to populate compression and compaction mixture rules. Int. J. Pharm. 662, 124475 (2024).
Djuriš, J., Kurćubić, I. & Ibrić, S. Review of machine learning algorithms application in pharmaceutical technology. Arh. Farm. 71, 302–317 (2021).
Mäki-Lohiluoma, E. et al. Use of machine learning in prediction of granule particle size distribution and tablet tensile strength in commercial pharmaceutical manufacturing. Int. J. Pharm. 609, 121146 (2021).
Moreno-Benito, M. et al. Digital twin of a continuous direct compression line for drug product and process design using a hybrid flowsheet modelling approach. Int. J. Pharm. 628, 122336 (2022).
Vassileiou, A. D. et al. A unified ML framework for solubility prediction across organic solvents. Digital Discovery 2, 356–367 (2023).
Galata, D. L. et al. Applications of machine vision in pharmaceutical technology: A review. Eur. J. Pharm. Sci. 159, 105717 (2021).
Unnikrishnan, S., Donovan, J., Macpherson, R. & Tormey, D. In-process analysis of pharmaceutical emulsions using computer vision and artificial intelligence. Chem. Eng. Res. Des. 166, 281–294 (2021).
Boyle, C., Brown, C., Sefcik, J. & Cardona, J. Improved particle characterisation from in-line PAT: comparison of deep learning and white-box methods. in AIChE Annual Meeting 2022 1–5 (AIChE, 2022).
Floryanzia, S. et al. Disintegration testing augmented by computer vision technology. Int. J. Pharm. 619, 121668 (2022).
Salehian, M. et al. A hybrid system of mixture models for the prediction of particle size and shape, density, and flowability of pharmaceutical powder blends. Int. J. Pharm. X 8, 100298 (2024).
Rogers, A. & Ierapetritou, M. Challenges and opportunities in pharmaceutical manufacturing modeling and optimization. Comput. Aided Chem. Eng. 34, 144–149 (2014).
Wang, Z., Escotet-Espinoza, M. S. & Ierapetritou, M. Process analysis and optimization of continuous pharmaceutical manufacturing using flowsheet models. Comput. Chem. Eng. 107, 77–91 (2017).
Sano, S., Kadowaki, T., Tsuda, K. & Kimura, S. Application of Bayesian optimization for pharmaceutical product development. J. Pharm. Innov. 15, 333–343 (2020).
Triantafyllou, N., Shah, N., Papathanasiou, M. M. & Kontoravdi, C. A bi-level decomposition approach for CAR-T cell therapies supply chain optimisation. Comput. Aided Chem. Eng. 52, 381–386 (Elsevier, 2023).
Jin, Y. & Kumar, P. V. Bayesian optimisation for efficient material discovery: a mini review. Nanoscale 15, 10975–10984 (2023).
Ungredda, J. & Branke, J. Bayesian optimisation for constrained problems. ACM Trans. Model. Comput. Simul. 34, 1–26 (2024).
Kegl, T. & Kralj, A. K. Multi-objective optimization of anaerobic digestion process using a gradient-based algorithm. Energy Convers. Manag. 226, 113560 (2020).
Salehian, M., Sefat, M. H. & Muradov, K. A robust, multi-solution framework for well placement and control optimization. Comput. Geosci. 26, 897–914 (2022).
Conn, A. R., Scheinberg, K. & Vicente, L. N. Introduction to Derivative-Free Optimization (SIAM, 2009).
Rios, L. M. & Sahinidis, N. V. Derivative-free optimization: a review of algorithms and comparison of software implementations. J. Glob. Optim. 56, 1247–1293 (2013).
Abolhasani, M. & Kumacheva, E. The rise of self-driving labs in chemical and materials sciences. Nat. Synth. 2, 483–492 (2023).
Tom, G. et al. Self-driving laboratories for chemistry and materials science. Chem. Rev. 124, 9633–9732 (2024).
Plowright, A. T. et al. Hypothesis driven drug design: improving quality and effectiveness of the design-make-test-analyse cycle. Drug Discovery Today 17, 56–62 (2012).
Wesolowski, S. S. & Brown, D. G. The strategies and politics of successful design, make, test, and analyze (DMTA) cycles in Lead Generation in Lead Generation: Methods and Strategies (Wiley, 2016).
Seifrid, M. et al. Autonomous chemical experiments: Challenges and perspectives on establishing a self-driving lab. Acc. Chem. Res. 55, 2454–2466 (2022).
Vijayan, R., Kihlberg, J., Cross, J. B. & Poongavanam, V. Enhancing preclinical drug discovery with artificial intelligence. Drug Discovery Today 27, 967–984 (2022).
Edvinsson, F. & Jonsson, V. Autonomous Drug Design with Reinforcement Learning.(MSc thesis, Chalmers University of Technology, 2023).
Yotsumoto, Y., Nakajima, Y., Takamoto, R., Takeichi, Y. & Ono, K. Autonomous robotic experimentation system for powder X-ray diffraction. Digit. Discov. 3, 2523–2532 (2024).
Burger, B. et al. A mobile robotic chemist. Nature 583, 237–241 (2020).
Zhu, Q. et al. An all-round AI-Chemist with a scientific mind. Natl. Sci. Rev. 9, nwac022 (2022).
Feng, S. et al. A robotic AI-Chemist system for multi-modal AI-ready database. Natl. Sci. Rev. 10, nwad159 (2023).
Klimša, V. et al. Spray drying robot for high-throughput combinatorial fabrication of multicomponent solid dispersions. Powder Technol. 428, 118872 (2023).
Hysmith, H. et al. The future of self-driving laboratories: from human in the loop interactive AI to gamification. Digit. Discov. 3, 621–636 (2024).
Lunt, A. M. et al. Modular, multi-robot integration of laboratories: an autonomous workflow for solid-state chemistry. Chem. Sci. 15, 2456–2463 (2024).
Schweidtmann, A. M. et al. Machine learning meets continuous flow chemistry: Automated optimization towards the Pareto front of multiple objectives. Chem. Eng. J. 352, 277–282 (2018).
Coley, C. W. et al. A robotic platform for flow synthesis of organic compounds informed by AI planning. Science 365, eaax1566 (2019).
Mustoe, C. L. et al. Quality by digital design to accelerate sustainable medicines development. Int. J. Pharm. 670, 125625 (2025).
Sykes, R. A. et al. What has scripting ever done for us? The CSD Python application programming interface (API). J. Appl. Crystallogr. 57, 1235–1250 (2024).
Salehian, M., Sefat, M. H. & Muradov, K. Multi-solution well placement optimization using ensemble learning of surrogate models. J. Pet. Sci. Eng. 210, 110076 (2022).
Tyralis, H. & Papacharalampous, G. A review of predictive uncertainty estimation with machine learning. Artif. Intell. Rev. 57, 94 (2024).
Shi, Y., Wei, P., Feng, K., Feng, D.-C. & Beer, M. A survey on machine learning approaches for uncertainty quantification of engineering systems. Mach. Learn. Comput. Sci. Eng. 1, 1–39 (2025).
Xu, T., Shao, C. & Wang, K. in HSLA Steels 2015, Microalloying 2015 & Offshore Engineering Steels 2015 229–235 (Wiley, 2015).
Toropov, A. A., Toropova, A. P., Mukhamedzhanova, D. V. & Gutman, I. Simplified molecular input line entry system (SMILES) as an alternative for constructing quantitative structure-property relationships (QSPR). Indian J. Chem. A 44, 1545–1552 (2005).
Wang, N., Sun, H., Dong, J. & Ouyang, D. PharmDE: A new expert system for drug-excipient compatibility evaluation. Int. J. Pharm. 607, 120962 (2021).
Serajuddin, A. T. et al. Selection of solid dosage form composition through drug–excipient compatibility testing. J. Pharm. Sci. 88, 696–704 (1999).
Ramos, P. Thermal compatibility assessment of selected excipients used in the oral anti-cancer formulation containing busulfan. Farmacia 70, 1073–1081 (2022).
Rojek, B. & Wesolowski, M. A combined differential scanning calorimetry and thermogravimetry approach for the effective assessment of drug substance-excipient compatibility. J. Therm. Anal. Calorim. 148, 845–858 (2023).
Wyttenbach, N., Birringer, C., Alsenz, J. & Kuentz, M. Drug-excipient compatibility testing using a high-throughput approach and statistical design. Pharm. Dev. Technol. 10, 499–505 (2005).
Jain, S. & Shah, R. P. Drug-excipient compatibility study through a novel vial-in-vial experimental setup: a benchmark study. AAPS PharmSciTech 24, 117 (2023).
Shangraw, R. F. Compressed tablets by direct compression. Pharm. Dosage Forms Tablets 1, 195–246 (1989).
Zhang, Y., Law, Y. & Chakrabarti, S. Physical properties and compact analysis of commonly used direct compression binders. AAPS PharmSciTech 4, 62 (2003).
Thoorens, G., Krier, F., Leclercq, B., Carlin, B. & Evrard, B. Microcrystalline cellulose, a direct compression binder in a quality by design environment—A review. Int. J. Pharm. 473, 64–72 (2014).
Choi, D.-H. et al. Material properties and compressibility using Heckel and Kawakita equation with commonly used pharmaceutical excipients. J. Pharm. Investig. 40, 237–244 (2010).
Salim, I., Kehinde, O. A., Abdulsamad, A., Mohammed, K. G. & Gwarzo, M. S. Physicomechanical behaviour of novel directly compressible Starch-MCC-Povidone composites and their application in ascorbic acid tablet formulation. Br. J. Pharm. 3, 1–13 (2018).
Akkaramongkolporn, P., Ngawhirunpat, T. & Opanasopit, P. Evaluation of a weakly cationic exchange poly (methacrylic acid-co-divinylbenzene) resin as filler-binder for direct compression tablets. Trop. J. Pharm. Res. 11, 371–378 (2012).
Yanes, D., Shinebaum, R., Papakostas, G., Reynolds, G. K. & Swainson, S. M. A pragmatic mixing model for the evaluation of powder flow properties of multicomponent pharmaceutical blends. Int. J. Pharm. X 9, 100339 (2025).
Owasit, A., Tripathi, S., Davé, R. & Young, J. Predicting powder blend flowability from individual constituent properties using machine learning. Pharm. Res. 42, 1–19 (2025).
Duckworth, W. Discussion of Ryshkewitch paper by Winston Duckworth. J. Am. Ceram. Soc. 36, 68–69 (1953).
Freeman, R. Measuring the flow properties of consolidated, conditioned and aerated powders—A comparative study using a powder rheometer and a rotational shear cell. Powder Technol. 174, 25–33 (2007).
Leung, L. Y., Mao, C., Srivastava, I., Du, P. & Yang, C.-Y. Flow function of pharmaceutical powders is predominantly governed by cohesion, not by friction coefficients. J. Pharm. Sci. 106, 1865–1873 (2017).
Shah, D. S. et al. A concise summary of powder processing methodologies for flow enhancement. Heliyon 9, e15340 (2023).
Leyva, N. & Mullarney, M. Modeling pharmaceutical powder-flow performance using particle-size distribution data. Pharm. Technol. 33, 54–62 (2009).
Bai, Y., Wagner, G. & Williams, C. B. Effect of bimodal powder mixture on powder packing density and sintered density in binder jetting of metals. Rapid Prototyp. J. 21, 555–561 (2015).
Leaper, M. C. Measuring the flow functions of pharmaceutical powders using the Brookfield powder flow tester and Freeman FT4. Processes 9, 2032 (2021).
Deng, T. et al. Comparative studies of powder flow predictions using milligrams of powder for identifying powder flow issues. Int. J. Pharm. 628, 122309 (2022).
Hiestand, E., Wells, J., Peot, C. & Ochs, J. Physical processes of tableting. J. Pharm. Sci. 66, 510–519 (1977).
Agrawal, A. et al. Development of tablet formulation of amorphous solid dispersions prepared by hot melt extrusion using quality by design approach. AAPS PharmSciTech 17, 214–232 (2016).
Sharma, M., Sharma, V. & Majumdar, D. K. Influence of tableting on enzymatic activity of papain along with determination of its percolation threshold with microcrystalline cellulose. Int. Sch. Res. Notices 2014, 140891 (2014).
Hornick, T. et al. In silico formulation optimization and particle engineering of pharmaceutical products using a generative artificial intelligence structure synthesis method. Nat. Commun. 15, 9622 (2024).
McGeary, R. Mechanical packing of spherical particles. J. Am. Ceram. Soc. 44, 513–522 (1961).
Clares, A. P., Gao, Y., Stebbins, R., van Duin, A. C. & Manogharan, G. Increasing density and mechanical performance of binder jetting processing through bimodal particle size distribution. Mater. Sci. Addit. Manuf. 1, 20 (2022).
Khomane, K. S. & Bansal, A. K. Effect of particle size on in-die and out-of-die compaction behavior of ranitidine hydrochloride polymorphs. AAPS PharmSciTech 14, 1169–1177 (2013).
Jenike, A. W. Storage and flow of solids. Bulletin No. 123 (Utah Engineering Experiment Station, (1964).
Roberts, R. & Rowe, R. The effect of the relationship between punch velocity and particle size on the compaction behaviour of materials with varying deformation mechanisms. J. Pharm. Pharmacol. 38, 567–571 (1986).
Winter, E. in Handbook of Game Theory with Economic Applications (Elsevier, 2002).
Marchenko, O., Fedorov, V., Lee, J. J., Nolan, C. & Pinheiro, J. Adaptive clinical trials: overview of early-phase designs and challenges. Ther. Innov. Regul. Sci. 48, 20–30 (2014).
Ingels, J. et al. Small-scale manufacturing of neoantigen-encoding messenger RNA for early-phase clinical trials. Cytotherapy 24, 213–222 (2022).
Milliken, R. L., Quinten, T., Andersen, S. K. & Lamprou, D. A. Application of 3D printing in early phase development of pharmaceutical solid dosage forms. Int. J. Pharm. 653, 123902 (2024).
Alves, J. B., Marques, B., Dias, P. & Santos, B. S. Using augmented reality for industrial quality assurance: a shop floor user study. Int. J. Adv. Manuf. Technol. 115, 105–116 (2021).
Volk, A. A. & Abolhasani, M. Performance metrics to unleash the power of self-driving labs in chemistry and materials science. Nat. Commun. 15, 1378 (2024).
Reynolds, G. K., Campbell, J. I. & Roberts, R. J. A compressibility based model for predicting the tensile strength of directly compressed pharmaceutical powder mixtures. Int. J. Pharm. 531, 215–224 (2017).
Wünsch, I., Finke, J. H., John, E., Juhnke, M. & Kwade, A. The influence of particle size on the application of compression and compaction models for tableting. Int. J. Pharm. 599, 120424 (2021).
Corrigan, J. et al. An interaction-based mixing model for predicting porosity and tensile strength of directly compressed ternary blends of pharmaceutical powders. Int. J. Pharm. 664, 124587 (2024).
Kalaria, D. R., Parker, K., Reynolds, G. K. & Laru, J. An industrial approach towards solid dosage development for first-in-human studies: Application of predictive science and lean principles. Drug Discovery Today 25, 505–518 (2020).
Abdi, H. & Williams, L. J. Principal component analysis. WIREs Comput. Stat. 2, 433–459 (2010).
Reddy, C. M. in Engineering Crystallography: From Molecule to Crystal to Functional Form (Springer, 2017).
Huesmann, K., Klemm, S., Linsen, L. & Risse, B. Exploiting the full capacity of deep neural networks while avoiding overfitting by targeted sparsity regularization. Preprint at https://doi.org/10.48550/arXiv.2002.09237 (2020).
Yang, Y., Tan, C., Cheng, Y., Luo, X. & Qiu, X. Using a deep neural network with small datasets to predict the initial production of tight oil horizontal wells. Electronics 12, 4570 (2023).
Ishii, M. & Sato, A. in 2019 International Joint Conference on Neural Networks (IJCNN) 1–8 (IEEE, 2019).
Phung, V. H. & Rhee, E. J. A high-accuracy model average ensemble of convolutional neural networks for classification of cloud image patches on small datasets. Appl. Sci. 9, 4500 (2019).
Hussein, B. M. & Shareef, S. M. in ITM Web of Conferences (EDP Sciences, 2019).
Sonthalia, R., Lok, J. & Rebrova, E. On regularization via early stopping for least squares regression. Preprint at https://doi.org/10.48550/arXiv.2406.04425 (2024).
Deb, K., Pratap, A., Agarwal, S. & Meyarivan, T. A fast and elitist multi-objective genetic algorithm: NSGA-II. IEEE Trans. Evol. Comput. 6, 182–197 (2002).
Kawakita, K. & Tsutsumi, Y. An empirical equation of state for powder compression. Jpn. J. Appl. Phys. 4, 56 (1965).
Kawakita, K. & Tsutsumi, Y. A comparison of equations for powder compression. Bull. Chem. Soc. Jpn. 39, 1364–1368 (1966).
Ryshkewitch, E. Compression strength of porous sintered alumina and zirconia: 9th communication to ceramography. J. Am. Ceram. Soc. 36, 65–68 (1953).
Adams, M. & McKeown, R. Micromechanical analyses of the pressure-volume relationship for powders under confined uniaxial compression. Powder Technol. 88, 155–163 (1996).
Abbas, N. et al. Data for “Accelerated Medicines Development using a Digital Formulator and a Self-Driving Tableting DataFactory”. https://doi.org/10.15129/9126a667-2245-4838-9f17-f073e0696c39 (2026).
Acknowledgements
The authors thank the Digital Medicines Manufacturing (DM2) Research Centre Co-funded by the Made Smarter Innovation challenge at UK Research and Innovation (Grant Ref: EP/V062077/1) for funding this work. The authors also thank the EPSRC ARTICULAR project (Grant ref: EP/R032858/1) and EPSRC Future Continuous Manufacturing and Advanced Crystallisation Research Hub (Grant Ref: EP/P006965/1) for the data generated and exploited in this work. This work was also supported by Research England and the Scottish Funding Council under the UK Research Partnership Investment Fund Net Zero Medicines Manufacturing Research Pilot.
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F.A., M.S., A.F., and D.M. contributed to the conceptualisation of the project. M.S. developed the digital formulator with input from D.M. F.A. developed the automation of the tableting data factory with contributions from A.T., P.H., and D.M. F.A. developed the orchestration software for the tableting data factory. J. Moores, J. Goldie and T.T. contributed to the data generation for the digital formulator. F.A., J. Goldie and A.T. contributed to the data generation on the tableting data factory. Q.B. and R.T. contributed to the modification and digital integration of the compaction simulator. A.S., J.J.S., and J. Guerin contributed the digital integration of the dosing unit. A.G.P.M., A.A.M., and M.S. contributed to the calculation of the particle informatics descriptors and their integration into the digital formulator. G.K.R. and J. Mantanus contributed to defining the objectives and constraints for the workflow demonstrations. V.P. developed the XR application with input from P.C., C.C., and F.A. F.A., M.S., and D.M. drafted the initial manuscript, and they revised the manuscript with all authors’ input. D.M. supervised the project.
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Abbas, F., Salehian, M., Hou, P. et al. Accelerated drug development using a digital formulator and a self-driving tableting data factory. Nat Commun (2026). https://doi.org/10.1038/s41467-026-71204-6
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DOI: https://doi.org/10.1038/s41467-026-71204-6
