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
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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.
Suppressed ballistic transport of dislocations at strain rates up to 109 s–1 in a stable nanocrystalline alloy
Dislocations are crucial to plastic deformation in crystals. At extreme strain rates, their motion shifts from thermally activated glide to ballistic transport, causing significant drag due to interactions with phonons, which can lead to embrittlement and failure in metals. The concept of dislons, quantized dislocations, has emerged to better understand these types of interactions. Similar to quantum treatment of dislocation-electron interactions, confining dislocations to nanometer scales, especially in nanocrystalline metals, could also yield unique mechanical behaviors different from bulk materials. Here, we present evidence showing that in Cu-3Ta, a thermo-mechanically stable nanocrystalline alloy, the phonon drag effect is entirely suppressed even at ultra-high strain rates (109 s−1). This is due to the stable confinement of dislocations within several-nanometer range, limiting their velocity and interaction with phonons. Our study indicates that in confined environments, the dislocation-phonon drag effect is minimal, potentially improving material performance under extreme conditions.
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