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Abstract Traditional techniques to identify macromolecular targets for drugs utilize solely the information on a query drug and a putative target. Nonetheless, the mechanisms of action of many drugs depend not only on their binding affinity toward a single protein, but also on the signal transduction through cascades of molecular interactions leading to certain phenotypes. Although using protein-protein interaction networks and drug-perturbed gene expression profiles can facilitate system-level investigations of drug-target interactions, utilizing such large and heterogeneous data poses notable challenges. To improve the state-of-the-art in drug target identification, we developed GraphDTI, a robust machine learning framework integrating the molecular-level information on drugs, proteins, and binding sites with the system-level information on gene expression and protein-protein interactions. In order to properly evaluate the performance of GraphDTI, we compiled a high-quality benchmarking dataset and devised a new cluster-based cross-validation protocol. Encouragingly, GraphDTI not only yields an AUC of 0.996 against the validation dataset, but it also generalizes well to unseen data with an AUC of 0.939, significantly outperforming other predictors. Finally, selected examples of identified drugtarget interactions are validated against the biomedical literature. Numerous applications of GraphDTI include the investigation of drug polypharmacological effects, side effects through offtarget binding, and repositioningmore »opportunities.« less

Advancements in nanotechnology require the development of nanofabrication methods for a wide range of materials, length scales, and elemental distributions. Today’s nanofabrication methods are typically missing at least one demanded characteristic. Hence, a general method enabling versatile nanofabrication remains elusive. Here, we show that, when revealing and using the underlying mechanisms of thermomechanical nanomolding, a highly versatile nanofabrication toolbox is the result. Specifically, we reveal interface diffusion and dislocation slip as the controlling mechanisms and use their transition to control, combine, and predict the ability to fabricate general materials, material combinations, and length scales. Designing specific elemental distributions is based on the relative diffusivities, the transition temperature, and the distribution of the materials in the feedstock. The mechanistic origins of thermomechanical nanomolding and their homologous temperature-dependent transition suggest a versatile toolbox capable of combining many materials in nanostructures and potentially producing any material in moldable shapes on the nanoscale.

Direct measurement of critical cooling rates has been challenging and only determined for a minute fraction of the reported metallic glass forming alloys. Here, we report a method that directly measures critical cooling rate of thin film metallic glass forming alloys in a combinatorial fashion. Based on a universal heating architecture using indirect laser heating and a microstructure analysis this method offers itself as a rapid screening technique to quantify glass forming ability. We use this method to identify glass forming alloys and study the composition effect on the critical cooling rate in the Al–Ni–Ge system where we identified Al₅₁Ge₃₅Ni₁₄as the best glass forming composition with a critical cooling rate of 10⁴ K/s.