Search for: All records

Abstract Physics-informed machine learning bridges the gap between the high fidelity of mechanistic models and the adaptive insights of artificial intelligence. In chemical reaction network modeling, this synergy proves valuable, addressing the high computational costs of detailed mechanistic models while leveraging the predictive power of machine learning. This study applies this fusion to the biomedical challenge of A$$\beta$$fibril aggregation, a key factor in Alzheimer’s disease. Central to the research is the introduction of an automatic reaction order model reduction framework, designed to optimize reduced-order kinetic models. This framework represents a shift in model construction, automatically determining the appropriate level of detail for reaction network modeling. The proposed approach significantly improves simulation efficiency and accuracy, particularly in systems like A$$\beta$$aggregation, where precise modeling of nucleation and growth kinetics can reveal potential therapeutic targets. Additionally, the automatic model reduction technique has the potential to generalize to other network models. The methodology offers a scalable and adaptable tool for applications beyond biomedical research. Its ability to dynamically adjust model complexity based on system-specific needs ensures that models remain both computationally feasible and scientifically relevant, accommodating new data and evolving understandings of complex phenomena. 

This paper presents a study of designing phase-specific and -mixed Ir−Ru−Mn trimetallic electrocatalysts with enhanced performance. By changing the content of Ru, the alloy electrocatalyst evolved from a face-centered tetragonal (fct) phase to a mixture of fct and hexagonal close-packed (hcp) phases and finally to the hcp phase. Among these trimetallic systems, the hcp-phase Ir0.23Ru0.20Mn0.57 electrocatalyst (Ru/Ir = 0.47:0.53) delivered the best performance toward the oxygen evolution reaction (OER), achieving an overpotential of 226 mV at 10 mA cm−2 and a Tafel slope of ∼46.8 mV dec−1. Interestingly, this low-Ir hcp-phase catalyst maintained stable operation for >57 h at a current density of 100 mA cm−2 in 0.1 M HClO4, whereas the Ir-rich fct-phase counterpart (Ir0.35Ru0.07Mn0.58) degraded within 22 h under identical conditions. Potentiodynamic polarization curve study indicated that oxidative dissolution is the dominant degradation pathway, and the structural characterizations indicated that the hcp-phase alloy remained intact, while rutile-type IrRuMnOx oxide was formed for the fct-phase alloy electrocatalyst. These results underscore the effect of the crystal phase on OER durability of the electrocatalyst and point to a design strategy for improving the durability of OER electrocatalysts without increasing the Ir content. 

Abstract Conventional drug discovery is expensive, time-consuming, and prone to failure. Artificial intelligence has become a potent substitute over the last decade, providing strong answers to challenging biological issues in this field. Among these difficulties, drug-target binding (DTB) is a key component of drug discovery techniques. In this context, drug-target affinity and drug–target interaction are complementary and essential frameworks that work together to improve our comprehension of DTB dynamics. In this work, we thoroughly analyze the most recent deep learning models, popular benchmark datasets, and assessment metrics for DTB prediction. We look at the paradigm shift in the development of drug discovery research since researchers started using deep learning as a potent tool for DTB prediction. In particular, we examine how methodologies have evolved, starting with early heterogeneous network-based approaches, progressing to graph-based approaches that were widely accepted, followed by modern attention-based architectures, and finally, the most recent multimodal approaches. We also provide case studies utilizing an extensive compound library against specific protein targets implicated in critical cancer pathways to demonstrate the usefulness of these approaches. In addition to summarizing the latest developments in DTB prediction models, this review also identifies their drawbacks. It also highlights the outlook for the DTB prediction domain and future research directions. Combined, these studies present a more comprehensive view of how deep learning offers a quantitative framework for researching drug-target relationships, speeding up the identification of new drug candidates and making it easier to identify possible DTBs. 

Abstract Quantitative structure–activity relationship (QSAR) modeling has become a critical tool in drug design. Recently proposed Topological Regression (TR), a computationally efficient and highly interpretable QSAR model that maps distances in the chemical domain to distances in the activity domain, has shown predictive performance comparable to state-of-the-art deep learning-based models. However, TR’s dependence on simple random sampling-based anchor selection and utilization of radial basis function for response reconstruction constrain its interpretability and predictive capacity. To address these limitations, we propose Adaptive Topological Regression (AdapToR) with adaptive anchor selection and optimization-based reconstruction. We evaluated AdapToR on the NCI60 GI50 dataset, which consists of over 50,000 drug responses across 60 human cancer cell lines, and compared its performance to Transformer CNN, Graph Transformer, TR, and other baseline models. The results demonstrate that AdapToR outperforms competing QSAR models for drug response prediction with significantly lower computational cost and greater interpretability as compared to deep learning-based models. 

We adapt the Edwards–Muthukumar theoretical framework for a single polymer chain to investigate the interplay between proton binding and counterion condensation for poly-acids. We find that changes to pH enable non-monotonic transitions between anti- and conventional polyelectrolyte behaviors. In the former, the net charge and the overall dimensions increase with increasing salt concentration, while the converse is true for conventional polyelectrolytes. The polymeric nature and local solvent polarization drive significant pKa shifts when compared to the values of reference monoacids. These pKa shifts are enhanced in semi-flexible chains. 

Staphylococcus aureus is the leading cause of skin infections in the U.S., and its rapid evolution and resistance to antibiotics create a barrier to effective treatment. In this study, we engineered a composite membrane with bacterial cellulose and carbon nanotubes (BC-CNT) as an electroactive dressing to rapidly eradicate vancomycin-intermediate S. aureus. Nonpathogenic Komagataeibacter sucrofermentans produced the BC membrane at an air-liquid interface. Then, carboxyl-functionalized multi-walled CNTs were integrated into decellularized BC to create stable and electrically conductive BC-CNT dressings. The electric potential and ionic flux across BC-CNT were modeled and standardized via chronoamperometry for experimental validation. We found that treatment with electroactive BC-CNT increases S. aureus sensitivity to vancomycin and prevents macro-scale biofilm formation. The bactericidal efficacy of the composite membrane is consistent with electrochemical stress caused by voltage mediated with BC-CNT. After a single hour of combinatorial electrical and drug treatment, biofilm-forming capacity was inhibited by nearly 92 %. These results advance applications of electrochemistry in medicine and create a new direction to overcome S. aureus infections on skin and soft tissues. 

Abstract In this article, we develop a weighted approach to estimation for right-censored time to event data in the presence of external predictions available from a prediction model. There are several advantages to the proposed approach. First, the method allows for arbitrary forms for the external prediction model. Second, the methodology can be fit easily using standard software packages that allow for subject-specific weights. Third, all that is needed from the external models are access to predictions and not the actually prediction equation. A complication is that inference becomes challenging, so we develop new theoretical results along with a perturbation-based method for inference. The methodology is applied to three publicly available datasets.