Abstract
Successful methodologies for theoretical crystal structure prediction (CSP) on flexible pharmaceutical-like organic molecules explore the lattice energy surface to find a set of plausible crystal structures. The initial search stages of CSP studies use relatively simple lattice energy approximations as hundreds of thousands of minima have to be considered. These generated crystal structures often have poor molecular geometries, as well as inaccurate lattice energy rankings, and performing reasonably accurate but computationally affordable optimisations of the crystal structures generated in a search would be highly desirable. Here, we seek to explore whether semi-empirical quantum-mechanical methods can perform this task. We employed the dispersion-corrected tight-binding Hamiltonian (DFTB3-D3) to relax all the inter- and intra-molecular degrees of freedom of several thousands of generated crystal structures of five pharmaceutical-like molecules, saving a large amount of computational effort compared to earlier studies. The computational cost scales better with molecular size and flexibility than other CSP methods, suggesting that it could be extended to even larger and more flexible molecules. On average, this optimisation improved the average reproduction of the eight experimental crystal structures (RMSD15) and experimental conformers (RMSD1) by 4% and 23%, respectively. The intermolecular interactions were then further optimised using distributed multipoles, derived from the molecular wave-functions, to accurately describe the electrostatic components of the intermolecular energies. In all cases, the experimental crystal structures are close to the top of the lattice energy ranking. Phonon calculations on some of the lowest energy structures were also performed with DFTB3-D3 methods to calculate the vibrational component of the Helmholtz free energy, providing further insights into the solid-state behaviour of the target molecules. We conclude that DFTB3-D3 is a cost-effective method for optimising flexible molecules, bridging the gap between the approximate methods used in CSP searches for generating crystal structures and more accurate methods required in the final energy ranking.
Highlights
In computational materials science, approaches that are based on the fundamental laws of quantum mechanics (QM) are integral to almost any materials design initiative in academia or industry.[1,2,3] The prevalence of polymorphism in organic molecules[4] and its importance in determining the physical properties of organic solids[1,5,6,7,8] has led to an interest in using simulation methods to predict the range of possible crystal structures and their properties, possibly to design molecular materials with desired characteristics, like high porosity.[9]
CSP studies aim to predict all the possible putative polymorphs (PPMs) of a molecule, starting from the chemical diagram only.[10]. This is important for pharmaceutical development when performed as a complement to solid form screening, which attempts to establish the range of solid forms that could either be developed into the pharmaceutical product or must not appear during manufacturing and storage.[8,11]
We present the rst use of semi-empirical QM methods within a full CSP work ow
Summary
Approaches that are based on the fundamental laws of quantum mechanics (QM) are integral to almost any materials design initiative in academia or industry.[1,2,3] The prevalence of polymorphism in organic molecules[4] and its importance in determining the physical properties of organic solids[1,5,6,7,8] has led to an interest in using simulation methods to predict the range of possible crystal structures and their properties, possibly to design molecular materials with desired characteristics, like high porosity.[9]. Whilst optimising all atomic positions simultaneously with the crystal lattice positions avoids the problem of selecting the crystal packing-dependent conformational degrees of freedom, the need to balance the inter- and intramolecular forces sufficiently accurately makes this exceptionally demanding. This has been demonstrated by the challenges posed by the largest molecules in the recent Blind Tests of CSP.[1,27,28] These molecules, though large enough to generate interest in CSP methods from industry, are still small and of limited exibility compared with pharmaceuticals in development. If CSP is to ful l its promise for pharmaceutical development, innovations that allow CSP studies on larger molecules within a restricted time frame[11] are needed
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