Molecule-like synthesis of ligand-protected metal nanoclusters
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Iterative printing of bulk metal and polymer for additive manufacturing of multi-layer electronic circuits
In pursuing advancing additive manufacturing (AM) techniques for 3D objects, this study combines AM techniques for bulk metal and polymer on a single platform for one-stop printing of multilayer 3D electronic circuits with two novel aspects. The first innovation involves the embedded integration of electronic circuits by printing low-resistance electrical traces from bulk metal into polymer channels. Cross-section grinding results reveal (92 ± 5)% occupancy of electrically conductive traces in polymer channels despite the different thermal properties of the two materials. The second aspect encompasses the possibility of printing vertical bulk metal vias up to 10 mm in height with the potential for expansion, interconnecting electrically conductive traces embedded in different layers of the 3D object. The work provides comprehensive 3D printing design guidelines for successfully integrating fully embedded electrically conductive traces and the interconnecting vertical bulk metal vias. A smooth and continuous workflow is also introduced, enabling a single-run print of functional multilayer embedded 3D electronics. The design rules and the workflow facilitate the iterative printing of two distinct materials, each defined by unique printing temperatures and techniques. Observations indicate that conductive traces using molten metal microdroplets show a 12-fold reduction in resistance compared to nanoparticle ink-based methods, meaning this technique greatly complements multi-material additive manufacturing (MM-AM). The work presents insights into the behavior of molten metal microdroplets on a polymer substrate when printed through the MM-AM process. It explores their characteristics in two scenarios: When they are deposited side-by-side to form conductive traces and when they are deposited out-of-plane to create vertical bulk metal vias. The innovative application of MM-AM to produce multilayer embedded 3D electronics with bulk metal and polymer demonstrates significant potential for realizing the fabrication of free-form 3D electronics.
Metal organic frameworks for wastewater treatment, renewable energy and circular economy contributions
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Narrowing the gap between machine learning scoring functions and free energy perturbation using augmented data
Machine learning offers great promise for fast and accurate binding affinity predictions. However, current models lack robust evaluation and fail on tasks encountered in (hit-to-) lead optimisation, such as ranking the binding affinity of a congeneric series of ligands, thereby limiting their application in drug discovery. Here, we address these issues by first introducing a novel attention-based graph neural network model called AEV-PLIG (atomic environment vector–protein ligand interaction graph). Second, we introduce a new and more realistic out-of-distribution test set called the OOD Test. We benchmark our model on this set, CASF-2016, and a test set used for free energy perturbation (FEP) calculations, that not only highlights the competitive performance of AEV-PLIG, but provides a realistic assessment of machine learning models with rigorous physics-based approaches. Moreover, we demonstrate how leveraging augmented data (generated using template-based modelling or molecular docking) can significantly improve binding affinity prediction correlation and ranking on the FEP benchmark (weighted mean PCC and Kendall’s τ increases from 0.41 and 0.26 to 0.59 and 0.42). These strategies together are closing the performance gap with FEP calculations (FEP+ achieves weighted mean PCC and Kendall’s τ of 0.68 and 0.49 on the FEP benchmark) while being ~400,000 times faster.
Augmented BindingNet dataset for enhanced ligand binding pose predictions using deep learning
High-quality data on protein-ligand complex structures and binding affinities are crucial for structure-based drug design. Existing datasets often lack diversity and quantity, limiting the comprehensive understanding of protein-ligand interactions. Here, we present BindingNet v2, an expanded dataset comprising 689,796 modeled protein-ligand binding complexes across 1794 protein targets. Constructed using an enhanced template-based modeling workflow from BindingNet v1, it incorporates pharmacophore and molecular shape similarities. BindingNet v2’s effectiveness in binding pose generation was evaluated, showing an improved generalization ability of Uni-Mol model for novel ligands. The success rate on the PoseBusters dataset increased from 38.55% with the PDBbind dataset alone to 64.25% with augmenting BindingNet v2. Coupled with physics-based refinement, the success rate rose to 74.07%, passing PoseBusters validity checks. These results highlight the value of larger, diverse datasets in enhancing the accuracy and reliability of deep learning models for binding pose prediction.
Comparing expedient and proactive approaches to the planning of protected area networks on Borneo
Protected areas are an important tool for wildlife conservation; however, research is increasingly revealing both biases and inadequacies in the global protected area network. One common criticism is that protected areas are frequently located in remote, high-elevation regions, which may face fewer threats compared to more accessible locations. To explore the conservation implications of this issue, we consider a thought experiment with seven different counterfactual scenarios for the Sunda clouded leopard’s conservation on Borneo. This allows us to examine two contrasting paradigms for conservation: “proactive conservation” which prioritises areas with high biodiversity and high risk of development, and “expedient conservation” which focusses on areas with the lowest development risk. We select clouded leopards as our focal species not only because of their emerging conservation importance, but also because, as top predators, they represent both keystone species and ambassadors for wider forest biodiversity. Furthermore, a published analysis of the likely impacts of forest loss in their habitat provides a benchmark for evaluating the modelled outcomes of alternative hypothetical conservation scenarios. We find that, across all metrics, expedient reserve design offered few benefits over the business-as-usual scenario, in contrast to the much greater conservation effectiveness of proactive protected area design. This paper sheds light on the challenging trade-offs between conservation goals and the competing land uses essential for the economic development and well-being of local communities.
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