Accelerating Polymorph Screening with AI & ML: A New Era in Drug Development
DOI:
https://doi.org/10.55544/jrasb.4.2.16Keywords:
Intelligence, Computational polymorph screening, Experimental Polymorph screening, Machine learningAbstract
Polymorph screening plays a crucial role in pharmaceutical development, influencing the solubility, stability, and bioavailability of active pharmaceutical ingredients (APIs). Traditional screening methods are time-consuming, labor-intensive, and often yield unpredictable results. Recent advancements in artificial intelligence (AI), machine learning (ML), and computational modeling such as molecular dynamics (MD) and density functional theory (DFT) have revolutionized this process, enabling faster and more accurate predictions of polymorphs, solvates, hydrates, cocrystals, and salts. The integration of AI-driven and computational models in polymorph screening is examined in this paper, with an emphasis on their potential applications in predicting the stability of amorphous solids, solubility of APIs, and the solvates, hydrates and cocrystals for the drug development. We also discussed how thermodynamic viability of solvate and hydrate formation and desolvation kinetics can be analyzed using computational techniques. AI and ML provides increase polymorph screening's success rate, which will impact the drug polymorphs selection for the manufacturing and regulatory compliance. The most recent advancements, challenges, and contemporary approaches in the use of AI/ML, MD and DFT in solid-state drug development are the main topics of this review.
Downloads
References
Karpinski, P.H., (2006) Polymorphism of Active Pharmaceutical Ingredients. Chem. Eng. Technol., 29, 233-237.
Censi R, Di Martino P. (2015) Polymorph Impact on the Bioavailability and Stability of Poorly Soluble Drugs. Molecules. 20 (10):18759-76.
Panda, Ramakant, S. Lankalapalli (2023). Bioavailability and polymorphic stability challenges affecting drug product’s potential: a critical evaluation and pertinent solution. Asian Journal of Pharmaceutical and Clinical Research, 16, 11, 9-23
Shiyi Zhou, Tongtong Yang, Cheng Qian, Fei Wu, Yanlong Hong, Xiao Lin, (2024) An update on solid-state form, crystal modification and transition issues related to tablet manufacturing, Journal of Drug Delivery Science and Technology, 100, 106135.
Ana Simões, Francisco Veiga, and Carla Vitorino, (2024) Question-based review for pharmaceutical development: An enhanced quality approach, European Journal of Pharmaceutics and Biopharmaceutics, 195, 114174. https://www.pharmafocuseurope.com/articles/ ritonavir-how-the-discovery-of-a-new-polymorph-changed-drug-development-forever
Sanjay R. Chemburkar, John Bauer, Kris Deming, Harry Spiwek, Ketan Patel, John Morris, Rodger Henry, Stephen Spanton, Walter Dziki, William Porter, John Quick, Phil Bauer, John Donaubauer, B. A. Narayanan, Mauro Soldani, Dave Riley, Kathyrn mcfarland, (2000) Dealing with the Impact of Ritonavir Polymorphs on the Late Stages of Bulk Drug Process Development. Org. Proc. Res. Dev. 4, 5, 413–417
Dedong Wu, Emma S.E. Eriksson, Sten O. Nilsson Lill, James F. Mccabe, Christoph Bauer, Michelle L. Lamb, (2025) Discovery of the most stable form of an adenosine receptor antagonist through virtual polymorph screening and targeted crystallization, Journal of Pharmaceutical Sciences, 114 (2), 829-840.
Qin Shi, Haibiao Chen, Yanan Wang, Jia Xu, Ziying Liu, Chen Zhang, (2022) Recent advances in drug polymorphs: Aspects of pharmaceutical properties and selective crystallization, International Journal of Pharmaceutics, 611, 121320.
Banga, S., Chawla, G., Varandani, D., et al. (2007). Modification of the crystal Habit of celecoxib for improved processability. J. Pharm. Pharmacol., 59, 29–39.
Khomane, K.S. and Bansal, A.K. (2013) Weak hydrogen bonding interactions influence slip system activity and compaction behavior of pharmaceutical powders. J. Pharm. Sci., 102, 4242–4245.
Joiris, E., Di Martino, P., Berneron, C., et al. (1998). Compression behavior of orthorhombic paracetamol. Pharm. Res. 15, 1122–1130.
Singhal, D. (2004). Drug polymorphism and dosage form design: a practical perspective. Adv. Drug Delivery Rev. 56, 335–347.
Picker-Freyer, K.M., Liao, X., Zhang, G., and Wiedmann, T.S. (2007) Evaluation of the compaction of sulfathiazole polymorphs. J. Pharm. Sci., 96: 2111–2124.
Summers, M.P., Enever, R.P., and Carless, J.E. (1976). The influence of crystal form on the radial stress transmission characteristics of pharmaceutical materials. J. Pharm. Pharmacol. 28, 89–99.
Khomane, K.S. and Bansal, A.K. (2013) Effect of particle size on in-die and out-of-die compaction behavior of ranitidine hydrochloride polymorphs. AAPS PharmSciTech, 14, 1169–1177.
Yin, X.-Z., Wu, L., Li, Y., et al. (2016). Visualization and quantification of deformation behavior of clopidogrel bisulfate polymorphs during tableting. Sci. Rep., 6: 21770.
Ann Newman. (2013). Specialized Solid Form Screening Techniques Org. Process Res. Dev. 17, 3, 457–471
Abramov, Yuriy. (2016) Computational Pharmaceutical Solid State Chemistry.
G. R. Desiraju. (2002) Cryptic crystallography. Nature Materials. 1 (2): 77–79.
S. M. Woodley, R. Catlow; Catlow. (2008) "Crystal structure prediction from first principles." Nature Materials. 7 (12): 937–946.
L. Pauling. (1929) The principles determining the structure of complex ionic crystals. Journal of the American Chemical Society. 51 (4): 1010–1026.
Martonak R., Laio A., Parrinello M. (2003) Predicting crystal structures: The Parrinello-Rahman method revisited. Physical Review Letters. 90 (3): 75502.
Martonak R., Donadio D., Oganov A. R., Parrinello M.; Donadio; Oganov; Parrinello. (2006) Crystal structure transformations in SiO₂ from classical and ab initio metadynamics. Nature Materials. 5 (8): 623–626.
Oganov, A. R.; Glass, C. W. (2006). Crystal structure prediction using ab initio evolutionary techniques: principles and applications. Journal of Chemical Physics. 124 (24): 244704.
Pickard, C. J.; Needs, R. J. (2006) High-Pressure Phases of Silane. Physical Review Letters. 97 (4): 045504.
Price, Sarah L. (2014) Predicting crystal structures of organic compounds. Chemical Society Reviews. 43 (7): 2098–2111.
Stone, Anthony. (2013) The Theory of Intermolecular Forces. Oxford University Press.
Nyman, Jonas; Day, Graeme M. (2015) Static and lattice vibrational energy differences between polymorphs. Crystengcomm. 17 (28): 5154–5165.
K. Sanderson. (2007) Model predicts structure of crystals. Nature. 450 (7171): 771.
Day, Graeme M.; Cooper, Timothy G.; Cruz-Cabeza, Aurora J.; Hejczyk, Katarzyna E.; Ammon, Herman L.; Boerrigter, Stephan X. M.; Tan, Jeffrey S.; Della Valle, Raffaele G.; Venuti, Elisabetta; Jose, Jovan; Gadre, Shridhar R.; Desiraju, Gautam R.; Thakur, Tejender S.; Van Eijck, Bouke P.; Facelli, Julio C.; Bazterra, Victor E.; Ferraro, Marta B.; Hofmann, Detlef W. M.; Neumann, Marcus A.; Leusen, Frank J. J.; Kendrick, John; Price, Sarah L.; Misquitta, Alston J.; Karamertzanis, Panagiotis G.; Welch, Gareth W. A.; Scheraga, Harold A.; Arnautova, Yelena A.; Schmidt, Martin U.; Van De Streek, Jacco; et al. (2009) Significant progress in predicting the crystal structures of small organic molecules—a report on the fourth blind test. Acta Crystallographica B. 65 (Pt 2): 107–125.
M. A. Neumann, F. J. J. Leusen, J. Kendrick; Leusen; Kendrick. (2008) A Major Advance in Crystal Structure Prediction. Angewandte Chemie International Edition. 47 (13): 2427–2430.
Reilly, Anthony M.; Cooper, Richard I.; Adjiman, Claire S.; Bhattacharya, Saswata; Boese, A. Daniel; Brandenburg, Jan Gerit; Bygrave, Peter J.; Bylsma, Rita; Campbell, Josh E.; Car, Roberto; Case, David H.; Chadha, Renu; Cole, Jason C.; Cosburn, Katherine; Cuppen, Herma M.; Curtis, Farren; Day, Graeme M.; Stasio, Robert A.; Dzyabchenko, Alexander; Van Eijck, Bouke P.; Elking, Dennis M.; Van Den Ende, Joost A.; Facelli, Julio C.; Ferraro, Marta B.; Fusti-Molnar, Laszlo; Gatsiou, Christina Anna; Gee, Thomas S.; De Gelder, Rene; Ghiringhelli, Luca M.; et al. (2016) Report on the sixth blind test of organic crystal structure prediction methods. Acta Crystallographica B. 72 (4): 439–459.
Dybeck, Eric C.; Abraham, Nathan S.; Schieber, Natalie P.; Shirts, Michael R. (2017) Capturing Entropic Contributions to Temperature-Mediated Polymorphic Transformations Through Molecular Modeling. Journal of Chemical Theory and Computation. 17 (4): 1775–1787.
Josef M. Gallmetzer, Jakob Gamper, Stefanie Kröll, Stefanie Kröll, Thomas S. Hofer, (2025) Comparative Study of UMCM-9 Polymorphs: Structural, Dynamic, and Hydrogen Storage Properties via Atomistic Simulations, J. Phys. Chem. C, 129, 11, 5645–5655
Arturo Sauza-de la Vega a, Leonardo J. Duarte bc, Arnaldo F. Silva b, Jonathan M. Skelton d, Tomás Rocha-Rinza a and Paul L. A. Popelier , (2022) Towards an atomistic understanding of polymorphism in molecular solids, Phys. Chem. Chem. Phys., 24, 11278-11294
Zuoxuan Zhu, Yuan Zhang, Zhixuan Wang, Weiwei Tang, Jingkang Wang, and Junbo Gong. (2024) Artificial Intelligence Assisted Pharmaceutical Crystallization, Cryst. Growth Des. 24, 10, 4245–4270
Firaha, D., Liu, Y.M., van de Streek, J., et al. (2023). Predicting crystal form stability under real-world conditions. Nature, 623, 324–328.
Rafael Barbas, Mercè Font-Bardia, Anant Paradkar, Christopher A. Hunter, and Rafel Prohens. (2018) Combined Virtual/Experimental Multicomponent Solid Forms Screening of Sildenafil: New Salts, Cocrystals, and Hybrid Salt–Cocrystals, Cryst. Growth Des. 18, 12, 7618–7627.
Sean Askin, Jeremy K. Cockcroft, Louise S. Price, Andrea D. Gonçalves, Min Zhao, Derek A. Tocher, Gareth R. Williams, Simon Gaisford, Duncan Q. M. Craig, (2019) Olanzapine Form IV: Discovery of a New Polymorphic Form Enabled by Computed Crystal Energy Landscapes. Cryst. Growth Des. 19, 5, 2751–2757.
Francesca Cenci, Samir Diab, Paola Ferrini, Catajina Harabajiu, Massimiliano Barolo, Fabrizio Bezzo, and Pierantonio Facco (2024), Predicting drug solubility in organic solvent mixtures: A machine-learning approach supported by high-throughput experimentation, International Journal of Pharmaceutics, 660, 124233.
Nastaran Meftahi, Michael L. Walker, and Brian J. Smith, (2021) Predicting aqueous solubility by QSPR modeling, Journal of Molecular Graphics and Modelling, 106, 107901.
Cristianini, N., Ricci, E. (2008). Support Vector Machines. In: Kao, MY. (eds.) Encyclopedia of Algorithms. Springer, Boston, MA.
Ho, Tin Kam. (1995) Random Decision Forests. Proceedings of the 3rd International Conference on Document Analysis and Recognition, Montreal, QC, 14–16, 278–282.
Ho TK. (1998) The Random Subspace Method for Constructing Decision Forests. IEEE Transactions on Pattern Analysis and Machine Intelligence. 20 (8): 832–844.
Shakhawath Hossain, Aleksei Kabedev, Albin Parrow, Christel A.S. Bergström, Per Larsson, (2019) Molecular simulation as a computational pharmaceutics tool to predict drug solubility, solubilization processes and partitioning, European Journal of Pharmaceutics and Biopharmaceutics, 137, 46-55
Lunna Li, Tim Totton, and Daan Frenkel. (2017). Computational methodology for solubility prediction: Application to the sparingly soluble solutes. J. Chem. Phys. 146, 214110.
Sumin Lee, Myeonghun Lee, Ki-Won Gyak, Sung Dug Kim, Mi-Jeong Kim, Kyoungmin Min, (2022) Novel Solubility Prediction Models: Molecular Fingerprints and Physicochemical Features vs Graph Convolutional Neural Networks, ACS Omega, 7, 14, 12268–12277
Ahmad, W.; Tayara, H.; Shim, H.; Chong, K.T. (2024) solpredictor: Predicting Solubility with Residual Gated Graph Neural Network. Int. J. Mol. Sci. 25, 715.
Renato Contreras, José Muñoz-Espinoza, and Bruno Sánchez (2024). Theoretical models of solubility for organic solvents. A conceptual density functional theory approach, Journal of Molecular Liquids, 403, 124736.
Isabella W. Cordova, Gabriel Teixeira, Paulo J. A. Ribeiro-Claro, Dinis O. Abranches, Simão P. Pinho, Olga Ferreira, João A. P. Coutinho, (2024) Using Molecular Conformers in COSMO-RS to Predict Drug Solubility in Mixed Solvents, Ind. Eng. Chem. Res. 63, 21, 9565–9575
Akosua B. Anane-Adjei, Esther Jacobs, Samuel C. Nash, Sean Askin, Ramesh Soundararajan, Mary Kyobula, Jonathan Booth, and Andrew Campbell, (2022) Amorphous solid dispersions: Utilization and challenges in preclinical drug development within AstraZeneca, International Journal of Pharmaceutics, 614, 121387.
George Zografi and Ann Newman, (2017) Interrelationships between Structure and the Properties of Amorphous Solids of Pharmaceutical Interest, Journal of Pharmaceutical Sciences, 106, 1, 5-27.
Kirk Swanson, Shubhendu Trivedi, Joshua Lequieu, Kyle Swanson, Risi Kondor. (2020) Deep learning for automated classification and characterization of amorphous materials. Soft Matter, 16, 435-446.
Bookwala, M., DeBoyace, K., Buckner, I.S., et al. (2020). Predicting Density of Amorphous Solid Materials Using Molecular Dynamics Simulation. AAPS PharmSciTech, 21, 96.
Tao, JL., Wang, YJ. (2024) An Artificial Intelligence Constitutive Model for Amorphous Solids Utilizing Graph Neural Networks. JOM, 76, 5777–5784
Ata Madanchi, Emna Azek, Karim Zongo, Laurent K. Béland, Normand Mousseau, Lena Simine, (2025) Is the Future of Materials Amorphous? Challenges and Opportunities in Simulations of Amorphous Materials, ACS Phys. Chem Au, 5, 1, 3–16.
Loschen C, Klamt A. (2016) Computational Screening of Drug Solvates. Pharm Res 33(11):2794-804.
Ipsha Shruti, Mubarak Almehairbi, Zeinab M. Saeed, Tamador Alkhidir, Wesam A. Ali, Rampal Vishwakarma, Sharmarke Mohamed, and Deepak Chopra (2022). Unraveling the Origin of Solvate Formation in the Anticancer Drug Trametinib: Insights from Crystal Structure Analysis and Computational Modeling. Cryst. Growth Des. 22, 10, 5861–5871
Asif A. Malik, Zeinab M. Saeed, Ishtiyaq Ahmad, Tamador Alkhidir, Praveen B. Managutti, Sharmarke Mohamed, and Aijaz A. Dar. (2024) Polymorphism Steered Thermochromism in a Sulfonamide. ACS Applied Optical Materials, 2 (8) , 1709-1720.
Cruz-Cabeza, A. J.; Day, G. M.; Jones, W. (2008) Towards Prediction of Stoichiometry in Crystalline Multicomponent Complexes. Chem.─Eur. J. 14, 8830–8836
Mohamed, S.; Li, L. (2018) From serendipity to supramolecular design: assessing the utility of computed crystal form landscapes in inferring the risks of crystal hydration in carboxylic acids. CrystEngComm, 20, 6026–6039
Braun, D. E.; Bhar dwaj, R. M.; Florence, A. J.; Tocher, D. A.; Price, S. L. (2013) Complex Polymorphic System of Gallic Acid-Five Monohydrates, Three Anhydrates, and over 20 Solvates. Cryst. Growth Des. 13, 19–23
Abramov YA, Loschen C, Klamt A. (2012) Rational coformer or solvent selection for pharmaceutical cocrystallization or desolvation. J Pharm Sci. 101(10):3687-97.
Cavallari C, Santos BP, Fini A. (2013) Olanzapine solvates. J Pharm Sci. 102(11):4046-56.
Tshepelevitsh S, Hernits K, Leito I. (2018) Prediction of partition and distribution coefficients in various solvent pairs with COSMO-RS. J Comput Aided Mol Des. 32(6):711-722.
Zhang X, Wang C, Zhou L, Yang W, Zhou L, Bao Y, Zhang M, Hou B, Xu Z, Yin Q. (2018) Effects of Hydrogen Bond Acceptor Ability of Solvents on Molecular Self-Assembly of Sulfadiazine Solvates. J Pharm Sci. 107(11):2823-2828.
Shaikh TR, Shelke N, Tothadi S. (2023) Multicomponent Solvate Crystals of 3,5-Dinitrobenzoic Acid and Acetamide and CSD Analysis of Solvates. ACS Omega. 8(27):24644-24653.
Ying Yang, Bingjie Hu, Markus A. Lill, (2014) Analysis of Factors Influencing Hydration Site Prediction Based on Molecular Dynamics Simulations, J. Chem. Inf. Model. 54, 10, 2987–2995
Healy, A. M., et al. (2002). Solid-state transformations of drugs: mechanochemical approaches to solvate formation. J. Pharm. Sci., 91(3), 884–891.
Marenich, A. V., Cramer, C. J., Truhlar, D. G. (2009). Universal solvation model based on solute electron density and on a continuum model of the solvent defined by the bulk dielectric constant and atomic surface tensions. J. Phys. Chem. B, 113(18), 6378–6396
Llinàs, A., Goodman, J. M. (2008). Polymorph control: past, present, and future. Drug Discovery Today, 13(5-6), 198–210.
Shihui Sun, Jian-Rong Wang, Lei Tang, Zhehuan Fan, Xutong Li, Zifu Huang, Weizhong Han, Qinggong Wang, Jie Yu, Zhiyin Xie, Wenzhi Yang, Zeen Yang, Mingyue Zheng, Dingyan Wang, Xuefeng Mei, Xiaomin Luo, (2025) Prediction of Solvate Based on Graph Attention Network, Cryst. Growth Des. 25, 2, 297–308
Wang, H.; Lian, D.; Zhang, Y.; Qin, L.; Lin, X. Gognn: (2020) Graph of graphs neural network for predicting structured entity interactions, IJCAI-20, 7, 1317–1323.
Wang, Y.; Min, Y.; Chen, X.; Wu, J. (2021) Multi-view Graph Contrastive Representation Learning for Drug-Drug Interaction Prediction. In Proceedings of the Web Conference 2021, ACM, 2921–2933.
Takieddin, K.; Khimyak, Y. Z.; Fábián, L. (2016) Prediction of Hydrate and Solvate Formation Using Statistical Models. Cryst. Growth Des. 16 (1), 70–81
Bhardwaj, R. M.; Reutzel-Edens, S. M.; Johnston, B. F.; Florence, A. J. (2018) A random forest model for predicting crystal packing of olanzapine solvates. CrystEngComm 20 (28), 3947–3950
Johnston, A.; Johnston, B. F.; Kennedy, A. R.; Florence, A. J. (2008) Targeted crystallisation of novel carbamazepine solvates based on a retrospective Random Forest classification. CrystEngComm, 10 (1), 23–25,
Jia, L. H.; Guo, M. X.; Dai, J. Y.; Chen, W.; Zhou, L.; Yin, Q. X. (2023) Assessing Solvate Prediction Approaches: A Case of Spironolactone. Cryst. Growth Des. 23 (2), 832–841.
Loschen C, Klamt A. (2015) Solubility prediction, solvate and cocrystal screening as tools for rational crystal engineering. J Pharm Pharmacol. 67(6):803-11.
Tavagnacco L, Schnupf U, Mason PE, Saboungi ML, Cesàro A, Brady JW. (2011) Molecular dynamics simulation studies of caffeine aggregation in aqueous solution. J Phys Chem B. 115(37):10957-66.
Xie, Y.; Shi, G.; Sun, J.; Li, S.; Gao, W.; Hu, Y.; Zu, C.; Tang, W.; Gong, J. (2022) Computational Screening and Experimental Validation on Multicomponent Crystals of a New Class of Janus Kinase (JAK) Inhibitor Drug with Improved Solubility. Crystals, 12, 1722
Karfunkel, H. R.; Gdanitz, R. J. (1992) Ab Initio prediction of possible crystal structures for general organic molecules. J. Comput. Chem. 13 (10), 1171–1183
Spek, A. (2009) Structure validation in chemical crystallography. Acta Crystallogr. Sect. D. Biol. Crystallogr. 65, 148–155,
Mohamed, S. (2018) Integrating Computed Crystal Energy Landscapes in Crystal Form Discovery and Characterisation, in Understanding Intermolecular Interactions in the Solid State: Approaches and Technique, 1– 31
Wicker, J. G. P.; Crowley, L. M.; Robshaw, O.; Little, E. J.; Stokes, S. P.; Cooper, R. I.; Lawrence, S. E. (2017) Will they co-crystallize? . CrystEngComm. 19, 5336–5340
Cruz-Cabez5336–5340Taylor, E.; Sugden, I. J.; Bowskill, D. H.; Wright, S. E.; Abdullahi, H.; Tulegenov, D.; Sadiq, G.; Davey, R. J. (2020) Can solvated intermediates inform us about nucleation pathways? The case of β-pABA. CrystEngComm, 22, 7447– 7459,
Hoja, Johannes and Ko, Hsin-Yu and Neumann, Marcus A. and Car, Roberto and DiStasio, Robert A. and Tkatchenko, Alexandre, (2019) Reliable and practical computational description of molecular crystal polymorphs, Sci. Adv. 5, eaau3338.
Zhou, D., Bier, I., Santra, B. et al. (2025) A robust crystal structure prediction method to support small molecule drug development with large scale validation and blind study. Nat Commun, 16, 2210.
Gregory J. O. Beran, (2023) Frontiers of molecular crystal structure prediction for pharmaceuticals and functional organic materials, Chem. Sci.,14, 13290-13312
Vasileiadis, M., Pantelides, C. C. & Adjiman, C. S. (2015) Prediction of the crystal structures of axitinib, a polymorphic pharmaceutical molecule. Chem. Eng. Sci. 121, 60–76
Gelbrich T, Braun DE, Ellern A, Griesser UJ. (2013) Four polymorphs of methyl paraben: structural relationships and relative energy differences. . Cryst. Growth Des. 13,1206–17
Pantelides CC, Adjiman CS, Kazantsev AV. (2014) General computational algorithms for ab initio crystal structure prediction for organic molecules. In Prediction and Calculation of Crystal Structures, ed. S Atahan-Evrenk, A Aspuru-Guzik , 22–58. Berlin:: Springer
Podeszwa R, Rice BM, Szalewicz K. (2009) Crystal structure prediction for cyclotrimethylene trinitramine (RDX) from first principles. Phys. Chem. Chem. Phys. 11::5512–18
Martoňák R, Laio A, Parrinello M. (2003) Predicting crystal structures: the Parrinello–Rahman method revisited. Phys. Rev. Lett. 90::075503
Downloads
Published
How to Cite
Issue
Section
License
Copyright (c) 2025 Anantha Rajmohan Muthusamy, Diwakar Chauhan, Arvind Kumar Jain, Meenakshi Sundaram Somasundaram, Amit Singh
This work is licensed under a Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License.
10.55544/jrasb
editor@jrasb.com or mailtojrasb@gmail.com | 
+91-8874396939
This work is licensed under a 