Bridging informatics and medicinal inorganic chemistry: toward a database of metallodrugs and metallodrug candidates

Potential antiprostatic performance of novel lanthanide-complexes based on 5-nitropicolinic acid

Two new lanthanide-complexes based on the 5-nitropicolinate ligand (5-npic) were obtained and fully characterized. Single-crystal X-ray diffraction revealed that these compounds are isostructural to a Dy-complex, previously published by us, based on dinuclear monomers link together with an extended hydrogen bond network, providing a final chemical formula of [Ln2(5-npic)6(H2O)4]·(H2O)2, where Ln = Dy (1), Gd (2), and Tb (3). Preliminary photoluminescent studies exhibited a ligand-centered emission for all complexes. The potential antitumoral activity of these materials was assayed in a prostatic cancer cell line (PC-3; the 2nd most common male cancerous disease), showing a significant anticancer activity (50-60% at 500 μg·mL-1). In turn, a high biocompatibility by both, the complexes and their precursors in human immunological HL-60 cells, was evidenced. In view of the strongest toxic effect in the tumoral cell line provided by the free 5-npic ligand (~ 40-50%), the overall anticancer complex performance seems to be triggered by the presence of this molecule.

Harnessing Transition Metal Scaffolds for Targeted Antibacterial Therapy

Antimicrobial resistance, caused by persistent adaptation and growing resistance of pathogenic bacteria to overprescribed antibiotics, poses one of the most serious and urgent threats to global public health. The limited pipeline of experimental antibiotics in development further exacerbates this looming crisis and new drugs with alternative modes of action are needed to tackle evolving pathogenic adaptation. Transition metal complexes can replenish this diminishing stockpile of drug candidates by providing compounds with unique properties that are not easily accessible using pure organic scaffolds. We spotlight four emerging strategies to harness these unique properties to develop new targeted antibacterial agents.

Pd(II) Complexes of Chiral Proline-Derived Ligands: Application for Dynamic Thermodynamic Resolution of α-Amino Acids and Their Antibacterial Activities

Novel type of Pd(II) complexes have been synthesized under operationally simple and convenient conditions and applied in the dynamic thermodynamic resolution of racemic N,C-unprotected α-amino acids. After rapid hydrolysis, these Pd(II) complexes produced the corresponding α-amino acids in satisfactory yields and enantioselectivities, accompanied by the recyclable proline-derived ligand. In addition, the method can be readily applied for S/R interconversion to obtain unnatural (R)-α-amino acids from readily available (S)-α-amino acids. Furthermore, biological assays showed that Pd(II) complexes (S,S)-3i and (S,S)-3m exhibited significant antibacterial activities similar to vancomycin, which may represent promising lead structures for further development of antibacterial agents.

Quantifying Functional-Group-like Structural Fragments in Molecules and Its Applications in Drug Design

A functional group in a molecule is a structural fragment consisting of a few atoms or a single atom that imparts reactivity to a molecule. Hence, defining functional groups is crucial in chemistry to predict the properties and reactivities of molecules. However, there is no established method in the literature for defining functional groups based on reactivity parameters. In this work, we addressed this issue by designing a set of predefined structural fragments along with reactivity parameters like electron conjugation and ring strain. This approach uses bond orders and atom connectivities to quantify the presence of these fragments within an organic molecule based on a given input molecular coordinate. To assess the effectiveness of this approach, we performed a case study to show the benefits of using these newly designed structural fragments instead of traditional fingerprint-based methods for grouping potential COX1/COX2 inhibitors by screening an approved drug library against aspirin molecule. The structural fragment-based model for ternary classification of rat oral LD₅₀ of chemicals showed performance similar to the fingerprint-based models. In evaluating the regression model performance for aqueous solubility, log(S), predictions, our approach outperformed the fingerprint-based model.

Metals to combat antimicrobial resistance

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.

Chemoinformatics and artificial intelligence colloquium: progress and challenges in developing bioactive compounds

We report the main conclusions of the first Chemoinformatics and Artificial Intelligence Colloquium, Mexico City, June 15-17, 2022. Fifteen lectures were presented during a virtual public event with speakers from industry, academia, and non-for-profit organizations. Twelve hundred and ninety students and academics from more than 60 countries. During the meeting, applications, challenges, and opportunities in drug discovery, de novo drug design, ADME-Tox (absorption, distribution, metabolism, excretion and toxicity) property predictions, organic chemistry, peptides, and antibiotic resistance were discussed. The program along with the recordings of all sessions are freely available at https://www.difacquim.com/english/events/2022-colloquium/ .

Metal Complexes as Antifungals? From a Crowd-Sourced Compound Library to the First 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 Pt1 significantly reduces fungal load in an in vivo G. 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.

Prediction of the Ibuprofen Loading Capacity of MOFs by Machine Learning

Metal-organic frameworks (MOFs) have been widely researched as drug delivery systems due to their intrinsic porous structures. Herein, machine learning (ML) technologies were applied for the screening of MOFs with high drug loading capacity. To achieve this, first, a comprehensive dataset was gathered, including 40 data points from more than 100 different publications. The organic linkers, metal ions, and the functional groups, as well as the surface area and the pore volume of the investigated MOFs, were chosen as the model’s inputs, and the output was the ibuprofen (IBU) loading capacity. Thereafter, various advanced and powerful machine learning algorithms, such as support vector regression (SVR), random forest (RF), adaptive boosting (AdaBoost), and categorical boosting (CatBoost), were employed to predict the ibuprofen loading capacity of MOFs. The coefficient of determination (R²) of 0.70, 0.72, 0.66, and 0.76 were obtained for the SVR, RF, AdaBoost, and CatBoost approaches, respectively. Among all the algorithms, CatBoost was the most reliable, exhibiting superior performance regarding the sparse matrices and categorical features. Shapley additive explanations (SHAP) analysis was employed to explore the impact of the eigenvalues of the model’s outputs. Our initial results indicate that this methodology is a well generalized, straightforward, and cost-effective method that can be applied not only for the prediction of IBU loading capacity, but also in many other biomaterials projects.

Structural Bioinformatics and Deep Learning of Metalloproteins: Recent Advances and Applications

All living organisms require metal ions for their energy production and metabolic and biosynthetic processes. Within cells, the metal ions involved in the formation of adducts interact with metabolites and macromolecules (proteins and nucleic acids). The proteins that require binding to one or more metal ions in order to be able to carry out their physiological function are called metalloproteins. About one third of all protein structures in the Protein Data Bank involve metalloproteins. Over the past few years there has been tremendous progress in the number of computational tools and techniques making use of 3D structural information to support the investigation of metalloproteins. This trend has been boosted by the successful applications of neural networks and machine/deep learning approaches in molecular and structural biology at large. In this review, we discuss recent advances in the development and availability of resources dealing with metalloproteins from a structure-based perspective. We start by addressing tools for the prediction of metal-binding sites (MBSs) using structural information on apo-proteins. Then, we provide an overview of the methods for and lessons learned from the structural comparison of MBSs in a fold-independent manner. We then move to describing databases of metalloprotein/MBS structures. Finally, we summarizing recent ML/DL applications enhancing the functional interpretation of metalloprotein structures.