Modern diagnostic technologies rely on both in vitro and in vivo modalities to provide a complete understanding of the clinical state of a patient. Nanoparticle-antibody conjugates have emerged as promising systems to confer increased sensitivity and accuracy for in vitro diagnostics (e.g., immunoassays). Meanwhile, in vivo applications have benefited from the targeting ability of nanoparticle-antibody conjugates, as well as payload flexibility and tailored biodistribution. This review provides an encompassing overview of nanoparticle-antibody conjugates, from chemistry to applications in medical immunoassays and tumor imaging, highlighting the underlying principles and unique features of relevant preclinical applications employing commonly used imaging modalities (e.g., optical/photoacoustics, positron-emission tomography, magnetic resonance imaging, X-ray computed tomography).
Aim: Descriptors of molecules are important in the discovery of lead compounds. Most of these descriptors are used to represent molecular structures, although structural formulas are the most intuitive representation. Convolutional neural networks (ConvNets) are effective for managing intuitive information. Results/methodology: Convolutional neural networks (ConvNets) based on two-dimensional structural formulas were used for the preliminary screening of CDK4 inhibitors. After supervised learning of our homemade dataset, our models screened out ten approved drugs, including indocyanine green and candesartan cilexetil, with IC50 values of 2.0 and 5.2 μM, respectively. Conclusion: Depending only on intuitive information, the developed method was shown to be feasible, thus providing a new method of lead compound discovery.
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