Chemists used machine learning and molecular modeling to identify potential anticancer drugs

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RUDN University chemists and colleagues from China built several machine learning models and discovered a group of potential drugs that inhibit the enzyme responsible for uncontrolled cell division. The results were published in Biomedicines.

has not yet been found," said Alexander Novikov, Ph.D. in Chemistry, senior researcher at the Joint Institute of Chemical Research of RUDN University.

To find a candidate drug, chemists used machine learning methods. The authors built several models to find active inhibitors of CDK 2. The chemists built a molecular model using the molecular docking method, which can identify the most favorable molecular orientation for the formation of a stable complex.with 98% accuracy. Chemists tested each of them using molecular docking. Three substances worked best.

"Compared to the control drug dalpiciclib, the three calculated compounds showed more stable behavior and compactness. Despite the promising results, our study has several limitations. We need in-depth clinical trials in vitro and in vivo to confirm inhibitory activity and potential therapeutic efficacy. In addition, when developing drugs, it will be necessary to study the effect of compounds on off-target interactions and their toxicity," Alexander Novikov, Ph.D.

 

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