Co‐crystal Prediction by Artificial Neural Networks**

High-throughput encapsulated nanodroplet screening for accelerated co-crystal discovery

Co-crystals are composed of two or more chemically inequivalent molecular species, excluding solvents, generally in a stoichiometric ratio. Co-crystals are particularly important in pharmaceutical development, where a suitable co-crystal can significantly improve the physiochemical and pharmacokinetic properties of an active pharmaceutical ingredient. However, co-crystal discovery remains both practically challenging and resource intensive, requiring the extensive searching of complex experimental space. Herein, we demonstrate a high-throughput (HTP) nanoscale co-crystallisation method for the rapid screening of large areas of co-crystallisation space with minimal sample requirements, based on Encapsulated Nanodroplet Crystallisation (ENaCt). HTP co-crystallisation screening by ENaCt allowed rapid access to all 18 possible binary co-crystal combinations of 3 small molecules and 6 co-formers (A/B), through the use of 3456 individual experiments exploring solvent, encapsulating oil and stoichiometry, including 10 novel binary co-crystal structures elucidated by single crystal X-ray diffraction (SCXRD). Higher-order co-crystal (HOC) discovery, accessing co-crystals containing three or more molecules, is one of the most challenging co-crystal research areas, due to the highly complex experimental landscape that must be navigated. Herein, we further exemplify the power of ENaCt co-crystallisation by application to HOC discovery. HTP ENaCt co-crystallisation screening of three component (A/B/C) and four component (A/B/C/D) combinations gave ready access to both ternary and quaternary HOCs, each containing three or four different molecular species respectively. In total, 13 056 individual ENaCt experiments are presented resulting in 54 co-crystal structures by SCXRD, including 17 novel binary co-crystals, 8 novel ternary co-crystals and 4 novel quaternary co-crystals. ENaCt co-crystallisation is thus demonstrated to be a highly impactful and efficient tool in the search for small molecule co-crystals, through the employment of parallelised HTP nanoscale experimental workflows.

Evaluating experimental, knowledge-based and computational cocrystal screening methods to advance drug-drug cocrystal fixed-dose combination development

Fixed-dose combinations (FDCs) offer significant advantages to patients and the pharmaceutical industry alike through improved dissolution profiles, synergistic effects and extended patent lifetimes. Identifying whether two active pharmaceutical ingredients have the potential to form a drug-drug cocrystal (DDC) or interact is an essential step in determining the most suitable type of FDC to formulate. The lack of coherent strategies to determine if two active pharmaceutical ingredients that can be co-administered can form a cocrystal, has significantly impacted DDC commercialisation. This review aims to accelerate the development of FDCs and DDCs by evaluating existing experimental, knowledge-based and computational cocrystal screening methods; the background of their development, their application in screening for cocrystals and DDCs, and their limitations are discussed. The evaluation provided in this review will act as a guide for selecting suitable screening methods to accelerate FDC development.

Absolute Configuration Determination of Chiral API Molecules by MicroED Analysis of Cocrystal Powders Formed Based on Cocrystal Propensity Prediction Calculations

Establishing the absolute configuration of chiral active pharmaceutical ingredients (APIs) is of great importance. Single crystal X-ray diffraction (scXRD) has traditionally been the method of choice for such analysis, but scXRD requires the growth of large crystals, which can be challenging. Here, we present a method for determining absolute configuration that does not rely on the growth of large crystals. By examining microcrystals formed with chiral probes (small chiral compounds such as amino acids), absolute configuration can be unambiguously determined by microcrystal electron diffraction (MicroED). Our streamlined method employs three steps: (1) virtual screening to identify promising chiral probes, (2) experimental cocrystal screening and (3) structure determination by MicroED and absolute configuration assignment. We successfully applied this method to analyze two chiral API molecules currently on the market for which scXRD was not used to determine absolute configuration.

Progress in Research on Artificial Intelligence Applied to Polymorphism and Cocrystal Prediction

Artificial intelligence (AI) is a technology that builds an artificial system with certain intelligence and uses computer software and hardware to simulate intelligent human behavior. When combined with drug research and development, AI can considerably shorten this cycle, improve research efficiency, and minimize costs. The use of machine learning to discover novel materials and predict material properties has become a new research direction. On the basis of the current status of worldwide research on the combination of AI and crystal form and cocrystal, this mini-review analyzes and explores the application of AI in polymorphism prediction, crystal structure analysis, crystal property prediction, cocrystal former (CCF) screening, cocrystal composition prediction, and cocrystal formation prediction. This study provides insights into the future applications of AI in related fields.