Discovery and Design of Radiopharmaceuticals by In silico Methods
Abstract
There has been impressive growth in the use of radiopharmaceuticals for therapy, selective toxic payload delivery, and noninvasive diagnostic imaging of disease. The increasing timeframes and costs involved in the discovery and development of new radiopharmaceuticals have driven the development of more efficient strategies for this process. Computer-Aided Drug Design (CADD) methods and Machine Learning (ML) have become more effective over the last two decades for drug and materials discovery and optimization. They are now fast, flexible, and sufficiently accurate to accelerate the discovery of new molecules and materials. Radiopharmaceuticals have also started to benefit from rapid developments in computational methods. Here, we review the types of computational molecular design techniques that have been used for radiopharmaceuticals design. We also provide a thorough examination of success stories in the design of radiopharmaceuticals, and the strengths and weaknesses of the computational methods. We begin by providing a brief overview of therapeutic and diagnostic radiopharmaceuticals and the steps involved in radiopharmaceuticals design and development. We then review the computational design methods used in radiopharmaceutical studies, including molecular mechanics, quantum mechanics, molecular dynamics, molecular docking, pharmacophore modelling, and datadriven ML. Finally, the difficulties and opportunities presented by radiopharmaceutical modelling are highlighted.…
How strong is the evidence?
Evidence Hub reads this as a review, done in computer models (in silico), not in people. Its evidence level is 5 of 5 (Very low · case / narrative / animal / in vitro / in silico). Case reports, narrative reviews, and animal, cell or computer-model studies give the weakest evidence for human health.
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