Applied machine learning for predicting the lanthanide-ligand binding affinities

Prediction of Actinide-Ligand Complex Stability Constants by Machine Learning

Sustainable application of nuclear energy requires efficient sequestration of actinides, which relies on extensive understanding of actinide-ligand interactions to guide rational design of ligands. Currently, the design of novel ligands adopts mainly the time-consuming and labor-intensive trial-and-error strategy and is impeded by the heavy-metal toxicity and radioactivity of actinides. The advancement of machine learning techniques brings new opportunities given a sensible choice of appropriate descriptors. In this study, by using the binding equilibrium constant (log K1) to represent the binding affinity of ligand with metal ion, 14 typical algorithms were used to train machine learning models toward accurate predictions of log K1 between actinide ions and ligands, among which the Gradient Boosting model outperforms the others, and the most relevant 15 out of the 282 descriptors of ligands, metals, and solvents were identified, encompassing key physicochemical properties of ligands, solvents, and metals. The Gradient Boosting model achieved R2 values of 0.98 and 0.93 on the training and test sets, respectively, showing its ability to establish qualitative correlations between the features and log K1 for accurate prediction of log K1 values. The impact of these properties on log K1 values was discussed, and a quantitative correlation was derived using the SISSO model. The model was then applied to eight recently reported ligands for Am3+, Cm3+, and Th4+ outside of the training set, and the predicted values agreed with the experimental ones. This study enriches the understanding of the fundamental properties of actinide-ligand interactions and demonstrates the feasibility of machine-learning-assisted discovery and design of ligands for actinides.

Optimizing Cancer Treatment: Exploring the Role of AI in Radioimmunotherapy

Radioimmunotherapy (RIT) is a novel cancer treatment that combines radiotherapy and immunotherapy to precisely target tumor antigens using monoclonal antibodies conjugated with radioactive isotopes. This approach offers personalized, systemic, and durable treatment, making it effective in cancers resistant to conventional therapies. Advances in artificial intelligence (AI) present opportunities to enhance RIT by improving precision, efficiency, and personalization. AI plays a critical role in patient selection, treatment planning, dosimetry, and response assessment, while also contributing to drug design and tumor classification. This review explores the integration of AI into RIT, emphasizing its potential to optimize the entire treatment process and advance personalized cancer care.

Integrating Prior Chemical Knowledge into the Graph Transformer Network to Predict the Stability Constants of Chelating Agents and Metal Ions

The latest advancements in nuclear medicine indicate that radioactive isotopes and associated metal chelators play crucial roles in the diagnosis and treatment of diseases. The development of metal chelators mainly relies on traditional trial-and-error methods, lacking rational guidance and design. In this study, we propose the structure-aware transformer (SAT) combined with molecular fingerprint (SATCMF), a novel graph transformer network framework that incorporates prior chemical knowledge to construct coordination edges and learns the interactions between chelating agents and metal ions. SATCMF is trained on stability data collected from metal ion-ligand complexes, leveraging the SAT network to extract structural features relevant to the binding of ligands with metal ions. It further integrates molecular fingerprint features to refine the prediction of the stability constants of the chelating agents and metal ions. The experimental results on benchmark data set demonstrate that SATCMF achieves state-of-the-art performance based on four different graph neural network architectures. Additionally, visualizing the learned molecular attention distribution provides interpretable insights from the prediction results, offering valuable guidance for the development of novel metal chelators.

Hepatotoxicity and lipid metabolism disorders of 8:2 polyfluoroalkyl phosphate diester in zebrafish: In vivo and in silico evidence

8:2 polyfluoroalkyl phosphate diester (8:2 diPAP) has been shown to accumulate in the liver, but whether it induces hepatotoxicity and lipid metabolism disorders remains largely unknown. In this study, zebrafish embryos were exposed to 8:2 diPAP for 7 d. Hepatocellular hypertrophy and karyolysis were noted after exposure to 0.5 ng/L 8:2 diPAP, suggesting suppressed liver development. Compared to the water control, 8:2 diPAP led to significantly higher triglyceride and total cholesterol levels, but markedly lower levels of low-density lipoprotein, implying disturbed lipid homeostasis. The levels of two peroxisome proliferator activated receptor (PPAR) subtypes (pparα and pparγ) involved in hepatotoxicity and lipid metabolism were significantly upregulated by 8:2 diPAP, consistent with their overexpression as determined by immunohistochemistry. In silico results showed that 8:2 diPAP formed hydrogen bonds with PPARα and PPARγ. Among seven machine learning models, Adaptive Boosting performed the best in predicting the binding affinities of PPARα and PPARγ on the test set. The predicted binding affinity of 8:2 diPAP to PPARα (7.12) was higher than that to PPARγ (6.97) by Adaptive Boosting, which matched well with the experimental results. Our results revealed PPAR - mediated adverse effects of 8:2 diPAP on the liver and lipid metabolism of zebrafish larvae.

PROTACable Is an Integrative Computational Pipeline of 3-D Modeling and Deep Learning To Automate the De Novo Design of PROTACs

Proteolysis-targeting chimeras (PROTACs) that engage two biological targets at once are a promising technology in degrading clinically relevant protein targets. Since factors that influence the biological activities of PROTACs are more complex than those of a small molecule drug, we explored a combination of computational chemistry and deep learning strategies to forecast PROTAC activity and enable automated design. A new method named PROTACable was developed for the de novo design of PROTACs, which includes a robust 3-D modeling workflow to model PROTAC ternary complexes using a library of E3 ligase and linker and an SE(3)-equivariant graph transformer network to predict the activity of newly designed PROTACs. PROTACable is available at https://github.com/giaguaro/PROTACable/.

Prediction of stability constants of metal-ligand complexes by machine learning for the design of ligands with optimal metal ion selectivity

The new LOGKPREDICT program integrates HostDesigner molecular design software with the machine learning (ML) program Chemprop. By supplying HostDesigner with predicted log K values, LOGKPREDICT enhances the computer-aided molecular design process by ranking ligands directly by metal-ligand binding strength. Harnessing reliable experimental data from a historic National Institute of Standards and Technology (NIST) database and data from the International Union of Pure and Applied Chemistry (IUPAC), we train message passing neural net algorithms. The multi-metal NIST-based ML model has a root mean square error (RMSE) of 0.629 ± 0.044 (R2 of 0.960 ± 0.006), while two versions of lanthanide-only IUPAC-based ML models have, respectively, RMSE of 0.764 ± 0.073 (R2 of 0.976 ± 0.005) and 0.757 ± 0.071 (R2 of 0.959 ± 0.007). For relative log K predictions on an out-of-sample set of six ligands, demonstrating metal ion selectivity, the RMSE value reaches a commendably low 0.25. We showcase the use of LOGKPREDICT in identifying ligands with high selectivity for lanthanides in aqueous solutions, a finding supported by recent experimental evidence. We also predict new ligands yet to be verified experimentally. Therefore, our ML models implemented through LOGKPREDICT and interfaced with the ligand design software HostDesigner pave the way for designing new ligands with predetermined selectivity for competing metal ions in an aqueous solution.

An assessment of the strategies for the energy-critical elements necessary for the development of sustainable energy sources

There have been several strategies developed to increase the diversified supply of energy so that it can meet all of the future demands for energy. As a result, to ensure a healthy and sustainable energy future, it is imperative to warrant reliable and diverse energy supply sources if the "green energy economy" is to be realized. The purpose of developing and deploying clean energy technologies is to improve our overall energy security, reduce our carbon footprint, and ensure that the generation of energy is secure and reliable in the future, making sure that we can spur economic growth in the future. In this paper, advancements in alternative sources of energy sustainability and strategies will be examined to ensure there will be enough fuel to supply all the future demands for energy. Several emerging clean energy technologies rely heavily on the availability of materials that exhibit unique properties that are necessary for their development. This paper examines the roles that rare earth and other energy-critical materials play in securing a clean energy economy and the development of clean energy economies in general. For the development of these technologies to be successful and sustainable, a number of these energy-critical materials are at risk of becoming unavailable. This is due to their limited availability, disruptions in supply, and a lack of suitable resources for their development. An action plan focusing on producing energy-critical materials in energy-efficient ways is discussed as part of an initiative to advance the development of clean and sustainable energy.

Machine learning-based analysis of overall stability constants of metal-ligand complexes

The stability constants of metal(M)-ligand(L) complexes are industrially important because they affect the quality of the plating film and the efficiency of metal separation. Thus, it is desirable to develop an effective screening method for promising ligands. Although there have been several machine-learning approaches for predicting stability constants, most of them focus only on the first overall stability constant of M-L complexes, and the variety of cations is also limited to less than 20. In this study, two Gaussian process regression models are developed to predict the first overall stability constant and the n-th (n > 1) overall stability constants. Furthermore, the feature relevance is quantitatively evaluated via sensitivity analysis. As a result, the electronegativities of both metal and ligand are found to be the most important factor for predicting the first overall stability constant. Interestingly, the predicted value of the first overall stability constant shows the highest correlation with the n-th overall stability constant of the corresponding M-L pair. Finally, the number of features is optimized using validation data where the ligands are not included in the training data, which indicates high generalizability. This study provides valuable insights and may help accelerate molecular screening and design for various applications.

Machine-Learning-Guided Identification of Coordination Polymer Ligands for Crystallizing Separation of Cs/Sr

Separation of Cs/Sr is one of many coordination-chemistry-centered processes in the grand scheme of spent nuclear fuel reprocessing, a critical link for a sustainable nuclear energy industry. To deploy a crystallizing Cs/Sr separation technology, we planned to systematically screen and identify candidate ligands that can efficiently and selectively bind to Sr2+ and form coordination polymers. Therefore, we mined the Cambridge Structural Database for characteristic structural information and developed a machine-learning-guided methodology for ligand evaluation. The optimized machine-learning model, correlating the molecular structures of the ligands with the predicted coordinative properties, generated a ranking list of potential compounds for Cs/Sr selective crystallization. The Sr2+ sequestration capability and selectivity over Cs+ of the promising ligands identified (squaric acid and chloranilic acid) were subsequently confirmed experimentally, with commendable performances, corroborating the artificial-intelligence-guided strategy.

Advancing Rare-Earth Separation by Machine Learning

Constituting the bulk of rare-earth elements, lanthanides need to be separated to fully realize their potential as critical materials in many important technologies. The discovery of new ligands for improving rare-earth separations by solvent extraction, the most practical rare-earth separation process, is still largely based on trial and error, a low-throughput and inefficient approach. A predictive model that allows high-throughput screening of ligands is needed to identify suitable ligands to achieve enhanced separation performance. Here, we show that deep neural networks, trained on the available experimental data, can be used to predict accurate distribution coefficients for solvent extraction of lanthanide ions, thereby opening the door to high-throughput screening of ligands for rare-earth separations. One innovative approach that we employed is a combined representation of ligands with both molecular physicochemical descriptors and atomic extended-connectivity fingerprints, which greatly boosts the accuracy of the trained model. More importantly, we synthesized four new ligands and found that the predicted distribution coefficients from our trained machine-learning model match well with the measured values. Therefore, our machine-learning approach paves the way for accelerating the discovery of new ligands for rare-earth separations.

Harnessing selectivity in chemical sensing via supramolecular interactions: from functionalization of nanomaterials to device applications

Chemical sensing is a strategic field of science and technology ultimately aiming at improving the quality of our lives and the sustainability of our Planet. Sensors bear a direct societal impact on well-being, which includes the quality and composition of the air we breathe, the water we drink, and the food we eat. Pristine low-dimensional materials are widely exploited as highly sensitive elements in chemical sensors, although they suffer from lack of intrinsic selectivity towards specific analytes. Here, we showcase the most recent strategies on the use of (supra)molecular interactions to harness the selectivity of suitably functionalized 0D, 1D, and 2D low-dimensional materials for chemical sensing. We discuss how the design and selection of receptors via machine learning and artificial intelligence hold a disruptive potential in chemical sensing, where selectivity is achieved by the design and high-throughput screening of large libraries of molecules exhibiting a set of affinity parameters that dictates the analyte specificity. We also discuss the importance of achieving selectivity along with other relevant characteristics in chemical sensing, such as high sensitivity, response speed, and reversibility, as milestones for true practical applications. Finally, for each distinct class of low-dimensional material, we present the most suitable functionalization strategies for their incorporation into efficient transducers for chemical sensing.

Pairwise Difference Regression: A Machine Learning Meta-algorithm for Improved Prediction and Uncertainty Quantification in Chemical Search

Machine learning (ML) plays a growing role in the design and discovery of chemicals, aiming to reduce the need to perform expensive experiments and simulations. ML for such applications is promising but difficult, as models must generalize to vast chemical spaces from small training sets and must have reliable uncertainty quantification metrics to identify and prioritize unexplored regions. Ab initio computational chemistry and chemical intuition alike often take advantage of differences between chemical conditions, rather than their absolute structure or state, to generate more reliable results. We have developed an analogous comparison-based approach for ML regression, called pairwise difference regression (PADRE), which is applicable to arbitrary underlying learning models and operates on pairs of input data points. During training, the model learns to predict differences between all possible pairs of input points. During prediction, the test points are paired with all training set points, giving rise to a set of predictions that can be treated as a distribution of which the mean is treated as a final prediction and the dispersion is treated as an uncertainty measure. Pairwise difference regression was shown to reliably improve the performance of the random forest algorithm across five chemical ML tasks. Additionally, the pair-derived dispersion is both well correlated with model error and performs well in active learning. We also show that this method is competitive with state-of-the-art neural network techniques. Thus, pairwise difference regression is a promising tool for candidate selection algorithms used in chemical discovery.