Publications by authors named "M Aldeghi"

Article Synopsis
  • *Using polymer materials in these formulations allows for diverse drug properties, but predicting their performance is complex due to various interacting factors.
  • *The study shows that machine learning can predict drug release from these systems and aid in designing new formulations, potentially reducing development time and costs.
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Additive manufacturing at the micro- and nanoscale has seen a recent upsurge to suit an increasing demand for more elaborate structures. However, the integration of multiple distinct materials at small scales remains challenging. To this end, capillarity-assisted particle assembly (CAPA) and two-photon polymerization direct laser writing (2PP-DLW) are combined to realize a new class of multimaterial microstructures.

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Synthetic polymers are versatile and widely used materials. Similar to small organic molecules, a large chemical space of such materials is hypothetically accessible. Computational property prediction and virtual screening can accelerate polymer design by prioritizing candidates expected to have favorable properties.

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An oracle that correctly predicts the outcome of every particle physics experiment, the products of every possible chemical reaction or the function of every protein would revolutionize science and technology. However, scientists would not be entirely satisfied because they would want to comprehend how the oracle made these predictions. This is scientific understanding, one of the main aims of science.

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In molecular discovery and drug design, structure-property relationships and activity landscapes are often qualitatively or quantitatively analyzed to guide the navigation of chemical space. The roughness (or smoothness) of these molecular property landscapes is one of their most studied geometric attributes, as it can characterize the presence of activity cliffs, with rougher landscapes generally expected to pose tougher optimization challenges. Here, we introduce a general, quantitative measure for describing the roughness of molecular property landscapes.

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