Graphormer-IR: Graph Transformers Predict Experimental IR Spectra Using Highly Specialized Attention.

Infrared (IR) spectroscopy is an important analytical tool in various chemical and forensic domains and a great deal of effort has gone into developing in silico methods for predicting experimental spectra. A key challenge in this regard is generating highly accurate spectra quickly to enable real-time feedback between computation and experiment. Here, we employ Graphormer, a graph neural network (GNN) transformer, to predict IR spectra using only simplified molecular-input line-entry system (SMILES) strings. Our data set includes 53,528 high-quality spectra, measured in five different experimental media (i.e., phases), for molecules containing the elements H, C, N, O, F, Si, S, P, Cl, Br, and I. When using only atomic numbers for node encodings, Graphormer-IR achieved a mean test spectral information similarity (SISµ) value of 0.8449 ± 0.0012 (n = 5), which surpasses that the current state-of-the-art model Chemprop-IR (SISµ = 0.8409 ± 0.0014, n = 5) with only 36% of the encoded information. Augmenting node embeddings with additional node-level descriptors in learned embeddings generated through a multilayer perceptron improves scores to SISµ = 0.8523 ± 0.0006, a total improvement of 19.7σ (t = 19). These improved scores show how Graphormer-IR excels in capturing long-range interactions like hydrogen bonding, anharmonic peak positions in experimental spectra, and stretching frequencies of uncommon functional groups. Scaling our architecture to 210 attention heads demonstrates specialist-like behavior for distinct IR frequencies that improves model performance. Our model utilizes novel architectures, including a global node for phase encoding, learned node feature embeddings, and a one-dimensional (1D) smoothing convolutional neural network (CNN). Graphormer-IR's innovations underscore its value over traditional message-passing neural networks (MPNNs) due to its expressive embeddings and ability to capture long-range intramolecular relationships.

Bridging the Gap between Differential Mobility, Log S, and Log P Using Machine Learning and SHAP Analysis.

Aqueous solubility, log S, and the water-octanol partition coefficient, log P, are physicochemical properties that are used to screen the viability of drug candidates and to estimate mass transport in the environment. In this work, differential mobility spectrometry (DMS) experiments performed in microsolvating environments are used to train machine learning (ML) frameworks that predict the log S and log P of various molecule classes. In lieu of a consistent source of experimentally measured log S and log P values, the OPERA package was used to evaluate the aqueous solubility and hydrophobicity of 333 analytes. With ion mobility/DMS data (e.g., CCS, dispersion curves) as input, we used ML regressors and ensemble stacking to derive relationships with a high degree of explainability, as assessed via SHapley Additive exPlanations (SHAP) analysis. The DMS-based regression models returned scores of R2 = 0.67 and RMSE = 1.03 ± 0.10 for log S predictions and R2 = 0.67 and RMSE = 1.20 ± 0.10 for log P after 5-fold random cross-validation. SHAP analysis reveals that the regressors strongly weighted gas-phase clustering in log P correlations. The addition of structural descriptors (e.g., # of aromatic carbons) improved log S predictions to yield RMSE = 0.84 ± 0.07 and R2 = 0.78. Similarly, log P predictions using the same data resulted in an RMSE of 0.83 ± 0.04 and R2 = 0.84. The SHAP analysis of log P models highlights the need for additional experimental parameters describing hydrophobic interactions. These results were achieved with a smaller dataset (333 instances) and minimal structural correlation compared to purely structure-based models, underscoring the value of employing DMS data in predictive models.

Selective Chelation of the Exotic Meitner-Auger Emitter Mercury-197 m/g with Sulfur-Rich Macrocyclic Ligands: Towards the Future of Theranostic Radiopharmaceuticals.

Mercury-197 m/g are a promising pair of radioactive isomers for incorporation into a theranostic as they can be used as a diagnostic agent using SPECT imaging and a therapeutic via Meitner-Auger electron emissions. However, the current absence of ligands able to stably coordinate 197m/g Hg to a tumour-targeting vector precludes their use in vivo. To address this, we report herein a series of sulfur-rich chelators capable of incorporating 197m/g Hg into a radiopharmaceutical. 1,4,7,10-Tetrathia-13-azacyclopentadecane (NS4 ) and its derivatives, (2-(1,4,7,10-tetrathia-13-azacyclopentadecan-13-yl)acetic acid (NS4 -CA) and N-benzyl-2-(1,4,7,10-tetrathia-13-azacyclopentadecan-13-yl)acetamide (NS4 -BA), were designed, synthesized and analyzed for their ability to coordinate Hg2+ through a combination of theoretical (DFT) and experimental coordination chemistry studies (NMR and mass spectrometry) as well as 197m/g Hg radiolabeling studies and in vitro stability assays. The development of stable ligands for 197m/g Hg reported herein is extremely impactful as it would enable their use for in vivo imaging and therapy, leading to personalized treatments for cancer.