Discovery of CDK4 Inhibitors By Convolutional Neural Networks

Abstract

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.

Graphical abstract

Keywords: :

• CDK4 inhibitors
• convolutional neural networks
• drug discovery
• machine learning
• virtual screening

Financial & competing interests disclosure

This work was supported by funds from the National Key R&D Program of China, China (2018YFC0311004) and the “Double First-Class” University Project, China (CPU2018GY15). The authors have no other relevant affiliations or financial involvement with any organization or entity with a financial interest in or financial conflict with the subject matter or materials discussed in the manuscript apart from those disclosed.

No writing assistance was utilized in the production of this manuscript.