481227 Recent Advances in Crystal Structure Prediction: Perspectives and Opportunities

Monday, November 14, 2016: 1:25 PM
Cyril Magnin III (Parc 55 San Francisco)
Claire S. Adjiman, Department of Chemical Engineering, Imperial College London, Center for Process Systems Engineering, London, United Kingdom

The prediction of the possible crystal structure(s) of organic molecules is an important activity for the pharmaceutical and agrochemical industries, among others, due to the prevalence of crystalline products [1]. The specific crystal structure adopted has implications for product performance as well as manufacturing. In this talk, recent progress in the area of crystal structure prediction is reviewed, focusing on the general requirements that crystal structure prediction (CSP) methodologies need to fulfil in order to achieve reliable predictions over a wide range of organic systems [2]. The current status of a multistage CSP methodology that has proven successful for a number of systems of practical interest, including pharmaceutical compounds and co-crystals, is discussed. The approach is based on a global search stage, with the CrystalPredictor algorithm [3], and a refinement stage based on local minimization with more accurate models, using the CrystalOptimizer algorithm [4]. The application of the approach to specific compounds is discussed, with particular emphasis on reliability and computational tractability. Further development needs are highlighted and ways in which one might progress from a description of the most likely solid phases to the prediction of properties of relevance to process and product development, such as solubility, are discussed. This requires the prediction, from a computed crystalline energy landscape, of the free energies of various solid phases, and the use of recent advances in the predictive modelling of the free energy of liquid phases [5].

[1] Storey R.A., Ymén I. (2011). Solid State Characterization of Pharmaceuticals. Wiley, Chichester, U.K.
[2] Pantelides, C.C., Adjiman, C.S., Kazantsev, A.V., 2014. General Computational Algorithms for Ab Initio Crystal Structure Prediction for Organic Molecules, in: Atahan-Evrenk, S., Aspuru-Guzik, A. (Eds.), Topics in Current Chemistry. Springer, pp. 25-58.
[3] Karamertzanis, P.G., Pantelides, C.C., 2005. Ab initio crystal structure prediction — I. Rigid molecules. Journal of Computational Chemistry 26, 304-324 ; Karamertzanis, P.G., Pantelides, C.C., 2007. Ab initio crystal structure prediction. II. Flexible molecules. Molecular Physics 105, 273-291; Habgood, M., Sugden, I.J., Kazantsev, A., Adjiman, C.S., Pantelides, C.C., 2015. Efficient Handling of Molecular Flexibility in Ab Initio Generation of Crystal Structures, JCTC, 11, 1957-1969; Sugden, I.J., Adjiman, C.S., Pantelides, C.C., Accurate and efficient representation of intramolecular energy in ab initio generation of crystal structures. Part I: Adaptive local approximate models, Acta Cryst. B, accepted (2016).
[4] Kazantsev, A.V., Karamertzanis, P.G., Adjiman, C.S., Pantelides, C.C., 2011a. Efficient Handling of Molecular Flexibility in Lattice Energy Minimization of Organic Crystals. Journal of Chemical Theory and Computation 7, 1998-2016.
[5] 64. Papaioannou, V., Lafitte, T., Avendaño, C., Adjiman, C.S., Jackson, G., Müller, E.A., Galindo, A., 2014. Group contribution methodology based on the statistical associating fluid theory for heteronuclear molecules formed from Mie segments, J. Chem. Phys., 14 , 054107.

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