NCSU researchers utilize molecular dynamics, machine learning for drug discovery
Researchers from North Carolina State University have demonstrated that molecular dynamics simulations and machine learning techniques could be integrated to create more accurate computer prediction models. These “hyper-predictive” models could be used to quickly predict which new chemical compounds could be promising drug candidates. Drug development is a costly and time-consuming process. To narrow down the number of chemical compounds that could be potential drug candidates, scientists utilize computer models that can predict how a particular chemical compound might interact with a biological target of interest – for example, a key protein that might be involved with a disease...
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