TUCAN: A molecular identifier and descriptor applicable to the whole periodic table from hydrogen to oganesson

Brammer, Jan C.; Blanke, Gerd; Kellner, Claudia; Hoffmann, Alexander; Herres‐Pawlis, Sonja; Schatzschneider, Ulrich

doi:10.1186/s13321-022-00640-5

Cited by 14 publications

(8 citation statements)

References 38 publications

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“…To this end, consistent and canonical identifiers for metal-containing compounds are urgently needed, as they are currently not accurately represented in chemical databases using standard chemical notation, such as SMILES (Simplified Molecular Input Line Entry System) strings. Recent efforts in this area have yielded promising candidates (for example, TUCAN 271 or SELFIES 272 ). Once standardized structure nomenclatures for metal complexes are in place, efficient and open machine-processing tools can be developed for databases and machine-learning tasks 273 .…”

Section: Discussionmentioning

confidence: 99%

Metals to combat antimicrobial resistance

et al. 2023

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Bacteria, similar to most organisms, have a love–hate relationship with metals: a specific metal may be essential for survival yet toxic in certain forms and concentrations. Metal ions have a long history of antimicrobial activity and have received increasing attention in recent years owing to the rise of antimicrobial resistance. The search for antibacterial agents now encompasses metal ions, nanoparticles and metal complexes with antimicrobial activity (‘metalloantibiotics’). Although yet to be advanced to the clinic, metalloantibiotics are a vast and underexplored group of compounds that could lead to a much-needed new class of antibiotics. This Review summarizes recent developments in this growing field, focusing on advances in the development of metalloantibiotics, in particular, those for which the mechanism of action has been investigated. We also provide an overview of alternative uses of metal complexes to combat bacterial infections, including antimicrobial photodynamic therapy and radionuclide diagnosis of bacterial infections.

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Section: Discussionmentioning

confidence: 99%

Metals to combat antimicrobial resistance

et al. 2023

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“…One reason is that most conventional methods to generate descriptors or feature vectors for molecules rely on molecular representations, such as SMILES, which cannot be easily extrapolated onto metal complexes with multiple coordinating ligands. While some solutions have been proposed, they have not been widely applied yet [33,34] . In our case we took advantage of the fact that all 288 tested compounds and any compound of this class we wished to predict shared several similarities.…”

Section: Resultsmentioning

confidence: 99%

Using Machine Learning to Predict the Antibacterial Activity of Ruthenium Complexes**

Orsi,

Shing Loh,

Weng

et al. 2024

Angew Chem Int Ed

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Rising antimicrobial resistance (AMR) and lack of innovation in the antibiotic pipeline necessitate novel approaches to discovering new drugs. Metal complexes have proven to be promising antimicrobial compounds, but the number of studied compounds is still low compared to the millions of organic molecules investigated so far. Lately, machine learning (ML) has emerged as a valuable tool for guiding the design of small organic molecules, potentially even in low‐data scenarios. For the first time, we extend the application of ML to the discovery of metal‐based medicines. Utilising 288 modularly synthesized ruthenium arene Schiff‐base complexes and their antibacterial properties, a series of ML models were trained. The models perform well and are used to predict the activity of 54 new compounds. These displayed a 5.7x higher hit‐rate (53.7%) against methicillin‐resistant Staphylococcus aureus (MRSA) compared to the original library (9.4%), demonstrating that ML can be applied to improve the success‐rates in the search of new metalloantibiotics. This work paves the way for more ambitious applications of ML in the field of metal‐based drug discovery.

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“…To make generative models more application-relevant, new methods are required that e.g., allow to include constraints in the design process, in the simplest case symmetries of generated molecules and materials, or in more complex scenarios additional (empirical or analytical) objectives such as synthesizability. A large step in that direction is new representations, not only for organic molecules but also for (3D) materials [263][264][265] .…”

Section: Discussionmentioning

confidence: 99%

Graph neural networks for materials science and chemistry

et al. 2022

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Machine learning plays an increasingly important role in many areas of chemistry and materials science, being used to predict materials properties, accelerate simulations, design new structures, and predict synthesis routes of new materials. Graph neural networks (GNNs) are one of the fastest growing classes of machine learning models. They are of particular relevance for chemistry and materials science, as they directly work on a graph or structural representation of molecules and materials and therefore have full access to all relevant information required to characterize materials. In this Review, we provide an overview of the basic principles of GNNs, widely used datasets, and state-of-the-art architectures, followed by a discussion of a wide range of recent applications of GNNs in chemistry and materials science, and concluding with a road-map for the further development and application of GNNs.

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TUCAN: A molecular identifier and descriptor applicable to the whole periodic table from hydrogen to oganesson

Cited by 14 publications

References 38 publications

Metals to combat antimicrobial resistance

Metals to combat antimicrobial resistance

Using Machine Learning to Predict the Antibacterial Activity of Ruthenium Complexes**

Graph neural networks for materials science and chemistry

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