Prioritizing Computational Cocrystal Prediction Methods for Experimental Researchers: A Review to Find Efficient, Cost-Effective, and User-Friendly Approaches

International Journal of Molecular Sciences, Год журнала: 2024, Номер 25(22), С. 12045 - 12045

Опубликована: Ноя. 9, 2024

Pharmaceutical cocrystals offer a versatile approach to enhancing the properties of drug compounds, making them an important tool in formulation and development by improving therapeutic performance patient experience pharmaceutical products. The prediction involves using computational theoretical methods identify potential cocrystal formers understand interactions between active ingredient coformers. This process aims predict whether two or more molecules can form stable structure before performing experimental synthesis, thus saving time resources. In this review, commonly used are first overviewed then evaluated based on three criteria: efficiency, cost-effectiveness, user-friendliness. Based these considerations, we suggest researchers without strong experiences which tools should be tested as step workflow rational design cocrystals. However, optimal choice depends specific needs resources, combining from different categories powerful approach.

Язык: Английский

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Progress in the understanding of traditional and nontraditional molecular interactions

Elsevier eBooks, Год журнала: 2025, Номер unknown

Опубликована: Янв. 1, 2025

Язык: Английский

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Navigating Solid-Form Screening Using In Silico Methods Validated with Experimental Data for a Drug-like Molecule

Crystal Growth & Design, Год журнала: 2025, Номер unknown

Опубликована: Апрель 23, 2025

Язык: Английский

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Resources for Accelerating Medicine Discovery

Burger's Medicinal Chemistry and Drug Discovery, Год журнала: 2025, Номер unknown, С. 1 - 57

Опубликована: Май 26, 2025

Abstract Using artificial intelligence and machine learning, computational tools are increasingly accelerating new medicine discovery. Applications to the stages of drug discovery, including target identification, hit lead optimization, developability assessment, described. This chapter provides a compilation databases, software packages, web‐based applications for evaluation, optimization molecules.

Язык: Английский

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Ammonium Organosulfonates as Machine Learning-Driven “Crystallization Cocktails” for the Structure Determination of Liquid Molecules

Crystal Growth & Design, Год журнала: 2025, Номер unknown

Опубликована: Май 29, 2025

Язык: Английский

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Novel and efficient synthesis of 5-chloro-6-methoxy-3-(2-((1-(aryl)-1H-1,2,3-triazol-4-yl)methoxy)ethyl)benzo[d]isoxazole derivatives as new α-glucosidase inhibitors

Biochemistry and Biophysics Reports, Год журнала: 2025, Номер 43, С. 102074 - 102074

Опубликована: Июнь 5, 2025

Язык: Английский

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Praziquantel Fifty Years on: A Comprehensive Overview of Its Solid State

Pharmaceutics, Год журнала: 2023, Номер 16(1), С. 27 - 27

Опубликована: Дек. 24, 2023

This review discusses the entire progress made on anthelmintic drug praziquantel, focusing solid state and, therefore, anhydrous crystalline polymorphs, amorphous forms, and multicomponent systems (i.e., hydrates, solvates, cocrystals). Despite having been extensively studied over last 50 years, new polymorphs greater part of their cocrystals have only identified in past decade. Progress crystal engineering science (e.g., use mechanochemistry as a form screening tool more strategic structure-based methods), along with development analytical techniques, including Synchrotron X-ray analyses, spectroscopy, microscopy, furthered identification unknown structures drug. Also, computational modeling has significantly contributed to prediction design by considering structural conformations interactions energy. Whilst insights praziquantel discussed present will give significant contribution controlling formation during manufacturing formulation, detailed forms help designing implementing future praziquantel-based functional materials. The latter hopefully overcome praziquantel’s numerous drawbacks exploit its potential field neglected tropical diseases.

Язык: Английский

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Computational predictions of cocrystal formation: A benchmark study of 28 assemblies comparing five methods from high‐throughput to advanced models

Journal of Computational Chemistry, Год журнала: 2024, Номер 45(29), С. 2465 - 2475

Опубликована: Июль 3, 2024

Abstract Cocrystals are assemblies of more than one type molecule stabilized through noncovalent interactions. They promising materials for improved drug formulation in which the stability, solubility, or biocompatibility active pharmaceutical ingredient (API) is by including a coformer. In this work, range density functional theory (DFT) and tight binding (DFTB) models systematically compared their ability to predict lattice enthalpy broad existing pharmaceutically relevant cocrystals. These from cocrystals containing model compounds 4,4′‐bipyridine oxalic acid those with well benchmarked APIs aspirin paracetamol, all tested large set alternative coformers. For simple cocrystals, there general consensus calculated different DFT models. API coformers predictions depend strongly on model. The significantly lighter DFTB unrealistic values even

Язык: Английский

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Deep Supramolecular Language Processing for Co-crystal Prediction

Опубликована: Июнь 18, 2024

Approximately 40% of marketed drugs exhibit suboptimal pharmacokinetic profiles. Co-crystallization, where pairs molecules form a multicomponent crystal, constitutes promising strategy to enhance physicochemical properties without compromising the pharmacological activity. However, finding co-crystal is resource-intensive, due vast number possible combinations. We present DeepCocrystal, novel deep learning approach designed predict formation by processing 'chemical language' from supramolecular vantage point. Rigorous validation DeepCocrystal showed balanced accuracy 78% in realistic scenarios, outperforming existing models. By leveraging molecular string representations, can also estimate uncertainty its predictions. harness this capability challenging prospective study, and successfully discovered two diflunisal, an anti-inflammatory drug. This study underscores potential -- particular chemical language accelerate co-crystallization, ultimately drug development, both academic industrial contexts.

Язык: Английский

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Leveraging Deep Chemical Language Processing for Co-crystal Prediction

Опубликована: Май 29, 2024

Most hits identified in the drug discovery pipelines and even 40% of marketed drugs suffer from suboptimal pharmacokinetic profiles. Co- crystallization, wherein a (or candidate) another organic molecule form multi- component crystal, can optimize physicochemical properties those molecules without hampering their pharmacological activity. However, finding promising co-crystal pairs is resource-intensive due to vast search space. Here we propose DeepCrystal, deep learning model based on chemical language predict co-crystallization. We rigorously validate DeepCrystal find that it achieves 78% accuracy realistic settings displays superior performance existing models. Leveraging represent molecules, estimate uncertainty its predictions. exploit this capability challenging prospective study discover two novel diflunisal, an antiinflammatory drug. This exemplifies successful application accelerate co-crystallization process lab, highlighting potential, both academic industrial settings.

Язык: Английский

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