Multifunctional Cyclopentadienes as a Scaffold for Combinatorial Bioorganometallics in [(η5-C5H2R1R2R3)M(CO)3] (M=Re, 99mTc) Piano-Stool Complexes

Multifunctional cyclopentadiene (Cp) ligands and their rhenium and 99mTc complexes were prepared by a versatile synthetic route. The properties of these Cp ligands can be tuned on demand, either during their synthesis (variation of R1) or through post-synthetic functionalization with two equal or different vectors (V1 and V2). Variation of these groups enables a combinatorial approach in the synthesis of bioorganometallic complexes. This is demonstrated by the preparation of Cp ligands containing both electron-donating and electron-withdrawing groups at the R1 position and their subsequent homo- or heterofunctionalization with biovector models (benzylamine and phenylalanine) under standard amide bond-formation conditions. All ligands can be coordinated to the fac-[Re(CO)3]+ and fac-[99mTc(CO)3]+ cores to give tetrafunctional complexes in straightforward and functional-group-tolerant procedures. The 99mTc complexes were prepared in one step, in 30 min, and under aqueous conditions from generator-eluted [99mTcO4]-

Ultrafast Vibrational Energy Transfer in Catalytic Monolayers at Solid–Liquid Interfaces

We investigate the ultrafast vibrational dynamics of monolayers from adsorbed rhenium–carbonyl CO2-reduction catalysts on a semiconductor surface (indium–tin-oxide (ITO)) with ultrafast two-dimensional attenuated total reflection infrared (2D ATR IR) spectroscopy. The complexes are partially equipped with isotope-labeled (13C) carbonyl ligands to generate two spectroscopically distinguishable forms of the molecules. Ultrafast vibrational energy transfer between the molecules is observed via the temporal evolution of cross-peaks between their symmetric carbonyl stretching vibrations. These contributions appear with time constant of 70 and 90 ps for downhill and uphill energy transfer, respectively. The energy transfer is thus markedly slower than any of the other intramolecular dynamics. From the transfer rate, an intermolecular distance of ∼4–5 Å can be estimated, close to the van der Waals distance of the molecular head groups. The present paper presents an important cornerstone for a better understanding of intermolecular coupling mechanisms of molecules on surfaces and explains the absence of similar features in earlier studies

SELFIES and the future of molecular string representations

Artificial intelligence (AI) and machine learning (ML) are expanding in popularity for broad applications to challenging tasks in chemistry and materials science. Examples include the prediction of properties, the discovery of new reaction pathways, or the design of new molecules. The machine needs to read and write fluently in a chemical language for each of these tasks. Strings are a common tool to represent molecular graphs, and the most popular molecular string representation, Smiles, has powered cheminformatics since the late 1980s. However, in the context of AI and ML in chemistry, Smiles has several shortcomings—most pertinently, most combinations of symbols lead to invalid results with no valid chemical interpretation. To overcome this issue, a new language for molecules was introduced in 2020 that guarantees 100% robustness: SELF-referencing embedded string (Selfies). Selfies has since simplified and enabled numerous new applications in chemistry. In this perspective, we look to the future and discuss molecular string representations, along with their respective opportunities and challenges. We propose 16 concrete future projects for robust molecular representations. These involve the extension toward new chemical domains, exciting questions at the interface of AI and robust languages, and interpretability for both humans and machines. We hope that these proposals will inspire several follow-up works exploiting the full potential of molecular string representations for the future of AI in chemistry and materials science

Platinum Cyclooctadiene Complexes with Activity against Gram-positive Bacteria

Antimicrobial resistance is a looming health crisis, and it is becoming increasingly clear that organic chemistry alone is not sufficient to continue to provide the world with novel and effective antibiotics. Recently there has been an increased number of reports describing promising antimicrobial properties of metal-containing compounds. Platinum complexes are well known in the field of inorganic medicinal chemistry for their tremendous success as anticancer agents. Here we report on the promising antibacterial properties of platinum cyclooctadiene (COD) complexes. Amongst the 15 compounds studied, the simplest compounds Pt(COD)X$_{2}$ (X=Cl, I, Pt1 and Pt2) showed excellent activity against a panel of Gram-positive bacteria including vancomycin and methicillin resistant Staphylococcus aureus. Additionally, the lead compounds show no toxicity against mammalian cells or haemolytic properties at the highest tested concentrations, indicating that the observed activity is specific against bacteria. Finally, these compounds showed no toxicity against Galleria mellonella at the highest measured concentrations. However, preliminary efficacy studies in the same animal model found no decrease in bacterial load upon treatment with Pt1 and Pt2. Serum exchange studies suggest that these compounds exhibit high serum binding which reduces their bioavailability in vivo, mandating alternative administration routes such as e. g. topical application

Correction: Metal complexes as a promising source for new antibiotics

Correction for ‘Metal complexes as a promising source for new antibiotics’ by Angelo Frei et al., Chem. Sci., 2020, 11, 2627–2639

Metal complexes as a promising source for new antibiotics

There is a dire need for new antimicrobial compounds to combat the growing threat of widespread antibiotic resistance. With a currently very scarce drug pipeline, consisting mostly of derivatives of known antibiotics, new classes of antibiotics are urgently required. Metal complexes are currently in clinical development for the treatment of cancer, malaria and neurodegenerative diseases. However, only little attention has been paid to their application as potential antimicrobial compounds. We report the evaluation of 906 metal-containing compounds that have been screened by the Community for Open Antimicrobial Drug Discovery (CO-ADD) for antimicrobial activity. Metal-bearing compounds display a significantly higher hit-rate (9.9%) when compared to the purely organic molecules (0.87%) in the CO-ADD database. Out of 906 compounds, 88 show activity against at least one of the tested strains, including fungi, while not displaying any cytotoxicity against mammalian cell lines or haemolytic properties. Herein, we highlight the structures of the 30 compounds with activity against Gram-positive and/or Gram-negative bacteria containing Mn, Co, Zn, Ru, Ag, Eu, Ir and Pt, with activities down to the nanomolar range against methicillin resistant S. aureus (MRSA). 23 of these complexes have not been reported for their antimicrobial properties before. This work reveals the vast diversity that metal-containing compounds can bring to antimicrobial research. It is important to raise awareness of these types of compounds for the design of truly novel antibiotics with potential for combatting antimicrobial resistance

Metal Complexes as Antifungals? From a Crowd-Sourced Compound Library to the First InVivo{In Vivo} Experiments

There are currently fewer than 10 antifungal drugs in clinical development, but new fungal strains that are resistant to most current antifungals are spreading rapidly across the world. To prevent a second resistance crisis, new classes of antifungal drugs are urgently needed. Metal complexes have proven to be promising candidates for novel antibiotics, but so far, few compounds have been explored for their potential application as antifungal agents. In this work, we report the evaluation of 1039 metal-containing compounds that were screened by the Community for Open Antimicrobial Drug Discovery (CO-ADD). We show that 20.9% of all metal compounds tested have antimicrobial activity against two representative Candida and Cryptococcus strains compared with only 1.1% of the >300,000 purely organic molecules tested through CO-ADD. We identified 90 metal compounds (8.7%) that show antifungal activity while not displaying any cytotoxicity against mammalian cell lines or hemolytic properties at similar concentrations. The structures of 21 metal complexes that display high antifungal activity (MIC ≤1.25 μM) are discussed and evaluated further against a broad panel of yeasts. Most of these have not been previously tested for antifungal activity. Eleven of these metal complexes were tested for toxicity in the Galleria mellonella moth larva model, revealing that only one compound showed signs of toxicity at the highest injected concentration. Lastly, we demonstrated that the organo-Pt(II) cyclooctadiene complex $\textbf{Pt1}$ significantly reduces fungal load in an in vivoG. mellonella infection model. These findings showcase that the structural and chemical diversity of metal-based compounds can be an invaluable tool in the development of new drugs against infectious diseases

SELFIES and the future of molecular string representations

Artificial intelligence (AI) and machine learning (ML) are expanding in popularity for broad applications to challenging tasks in chemistry and materials science. Examples include the prediction of properties, the discovery of new reaction pathways, or the design of new molecules. The machine needs to read and write fluently in a chemical language for each of these tasks. Strings are a common tool to represent molecular graphs, and the most popular molecular string representation, SMILES, has powered cheminformatics since the late 1980s. However, in the context of AI and ML in chemistry, SMILES has several shortcomings -- most pertinently, most combinations of symbols lead to invalid results with no valid chemical interpretation. To overcome this issue, a new language for molecules was introduced in 2020 that guarantees 100\% robustness: SELFIES (SELF-referencIng Embedded Strings). SELFIES has since simplified and enabled numerous new applications in chemistry. In this manuscript, we look to the future and discuss molecular string representations, along with their respective opportunities and challenges. We propose 16 concrete Future Projects for robust molecular representations. These involve the extension toward new chemical domains, exciting questions at the interface of AI and robust languages and interpretability for both humans and machines. We hope that these proposals will inspire several follow-up works exploiting the full potential of molecular string representations for the future of AI in chemistry and materials science.Comment: 34 pages, 15 figures, comments and suggestions for additional references are welcome

SELFIES and the future of molecular string representations

Artificial intelligence (AI) and machine learning (ML) are expanding in popularity for broad applications to challenging tasks in chemistry and materials science. Examples include the prediction of properties, the discovery of new reaction pathways, or the design of new molecules. The machine needs to read and write fluently in a chemical language for each of these tasks. Strings are a common tool to represent molecular graphs, and the most popular molecular string representation, SMILES, has powered cheminformatics since the late 1980s. However, in the context of AI and ML in chemistry, SMILES has several shortcomings -- most pertinently, most combinations of symbols lead to invalid results with no valid chemical interpretation. To overcome this issue, a new language for molecules was introduced in 2020 that guarantees 100\% robustness: SELFIES (SELF-referencIng Embedded Strings). SELFIES has since simplified and enabled numerous new applications in chemistry. In this manuscript, we look to the future and discuss molecular string representations, along with their respective opportunities and challenges. We propose 16 concrete Future Projects for robust molecular representations. These involve the extension toward new chemical domains, exciting questions at the interface of AI and robust languages and interpretability for both humans and machines. We hope that these proposals will inspire several follow-up works exploiting the full potential of molecular string representations for the future of AI in chemistry and materials science

SELFIES and the future of molecular string representations

Artificial intelligence (AI) and machine learning (ML) are expanding in popularity for broad applications to challenging tasks in chemistry and materials science. Examples include the prediction of properties, the discovery of new reaction pathways, or the design of new molecules. The machine needs to read and write fluently in a chemical language for each of these tasks. Strings are a common tool to represent molecular graphs, and the most popular molecular string representation, Smiles, has powered cheminformatics since the late 1980s. However, in the context of AI and ML in chemistry, Smiles has several shortcomings—most pertinently, most combinations of symbols lead to invalid results with no valid chemical interpretation. To overcome this issue, a new language for molecules was introduced in 2020 that guarantees 100% robustness: SELF-referencing embedded string (Selfies). Selfies has since simplified and enabled numerous new applications in chemistry. In this perspective, we look to the future and discuss molecular string representations, along with their respective opportunities and challenges. We propose 16 concrete future projects for robust molecular representations. These involve the extension toward new chemical domains, exciting questions at the interface of AI and robust languages, and interpretability for both humans and machines. We hope that these proposals will inspire several follow-up works exploiting the full potential of molecular string representations for the future of AI in chemistry and materials science