Mining predicted crystal structure landscapes with high throughput crystallisation: old molecules, new insights

Abstract

Organic molecules tend to close pack to form dense structures when they are crystallised from organic solvents. Porous molecular crystals defy this rule: they contain open space, which is typically stabilised by inclusion of solvent in the interconnected pores during crystallisation. The design and discovery of such structures is often challenging and time consuming, in part because it is difficult to predict solvent effects on crystal form stability. Here, we combine crystal structure prediction (CSP) with a robotic crystallisation screen to accelerate the discovery of stable hydrogen-bonded frameworks. We exemplify this strategy by finding new phases of two well-studied molecules in a computationally targeted way.Specifically, we find a new 'hidden' porous polymorph of trimesic acid, d-TMA, that has a guest-free hexagonal pore structure, as well as three new solvent-stabilized diamondoid frameworks of adamantane-1,3,5,7-tetracarboxylic acid (ADTA). Beyond porous solids, this hybrid computationalexperimental approach could be applied to a wide range of materials problems, such as organic electronics and drug formulation.

High‐Throughput Approaches for the Discovery of Supramolecular Organic Cages

“…CSP has been used in porous molecular systems to correctly predict the experimentallyobserved crystal structure, [42] preference for enantiopure or racemic packing, [42] pore-size, [43] as well as identifying a priori the most promising molecules for targeted synthesis of properties via energy-structure-function maps. [44][45][46] We recently combined the whole workflow of structure prediction, from reaction outcome, molecular structure to crystal packing for the first time. [47] With a computational model of the molecular or solid-state structure, it is then possible to perform a variety of calculations to determine the properties of the material, such as poretopology, surface area, guest uptake and selectivity.…”

“…For example, the synthesis of peptides, [56] oligonucleotides, [57] and oligosaccharides, [58] and a range of pharmaceutical and naturalproduct molecules, [59][60][61][62] has been automated, but there are relatively fewer examples of automating the synthesis of organic materials and supramolecular assemblies. [46] In relation to discrete organic molecules, automation has been used to determine the optimal reaction conditions required to synthesise a target compound, [62] discover new chemical transformations using 'accelerated serendipity', [63] discover new molecules, evaluate potential catalysts for couplings, [59] and multi-step scale-up of target compounds. [64] However, the majority of these approaches have focused on using either solid-phase synthesis or flow chemistry to automate synthesis, [60,65,66] which can limit the number of molecules that can be screened at once with reactions typically run in sequence.…”

“…[47] Additionally, this workflow could have other techniques incorporated at each stage, and be developed to link with property screening, such as crystal packing, porosity, and solubility (Figure 2a, bottom box). For example, Cooper and co-workers demonstrated how robotic crystallisation screens can be carried out, [46] and how the gas uptake ability of solid porous materials can be rapidly screened by monitoring the temperature change on exposing the samples to different gases after being evacuated under vacuum. [74] Alternatively, the workflow can be expanded for the discovery of new porous liquids by investigating the solubility of organic cages in cavityexcluded solvents, [16] and screening any highly soluble combinations for porosity.…”

“…[75] Recent examples include a combined approach in the discovery of inorganic photoelectrocatalysts, [76] metal oxides, [77] thin films, [78] organic polymer photocatalysts, [74] and porous molecular crystals. [46] Our own work has focussed on combining state-of-the-art automation with computational modelling in hybrid workflows for the synthesis of supramolecular organic cages (Figure 4). [39] Not only did this accelerate their discovery, it led to the synthetic Figure 3) are shown.…”

In vivo real-time tracking of tumor-specific biocatalysis in cascade nanotheranostics enables synergistic cancer treatment

“…[10][11][12] Most of the reported GOD-based biocatalytic strategies have mainly focused on the entrapment of GOD with tumor-specic nanocarriers for prolonged blood circulation duration, increased stability, and improved tumor-targeting ability. [13][14][15][16][17][18][19][20][21] Despite their signicant advances, there remains a lack of desirable GOD-based design strategy to predict in vivo behaviors and regulate the therapeutic efficiency of GOD in clinical applications.…”