Computational design of CDK1 inhibitors with enhanced target affinity and drug-likeness using deep-learning framework
Zuokun Lu1,2, Jiayuan Han1, Yibo Ji1
1Food and Pharmacy College, Xuchang University, Xuchang, 461000, Henan, China.
Abstract:
Cyclin Dependent Kinase 1 (CDK1) plays a crucial role in cell cycle regulation, and dysregulation of its activity has been implicated in various cancers. Although several CDK1 inhibitors are currently in clinical trials, none have yet been approved for therapeutic use. This research utilized deep learning techniques, specifically Recurrent Neural Networks with Long Short-Term Memory (LSTM), to generate potential CDK1 inhibitors. Molecular docking, evaluation of molecular properties, and molecular dynamics simulations were conducted to identify the most promising candidates. The results showed that the generated ligands exhibited substantial improvements in target affinity and drug-likeness. Molecular docking results showed that the generated ligands had an average binding affinity of -10.65 ± 0.877 kcal/mol towards CDK1. The Quantitative Estimate of Drug-likeness (QED) values for the generated ligands averaged 0.733 ± 0.10, significantly higher than the 0.547 ± 0.15 observed for known CDK1 inhibitors (p < 0.001). Molecular dynamics simulations further confirmed the stability and favorable interactions of the selected ligands with the CDK1 complex. The identification of novel CDK1 inhibitors with improved binding affinities and drug-likeness properties could potentially fill the gap in the ongoing development of CDK inhibitors. However, it is imperative to note that extensive experimental validation is required prior to advancing these generated ligands to subsequent stages of drug development.
Insights
Deep learning generated novel Cyclin Dependent Kinase 1 (CDK1) inhibitors with enhanced binding affinity and drug-likeness. These potential drug candidates show promise for cancer therapy, pending further experimental validation.
Area of Science:
- Computational chemistry
- Drug discovery
- Bioinformatics
Background:
- Cyclin Dependent Kinase 1 (CDK1) is vital for cell cycle regulation; its dysregulation is linked to cancer.
- Current CDK1 inhibitors are in clinical trials but lack FDA approval, highlighting a therapeutic gap.
Purpose of the Study:
- To employ deep learning, specifically Recurrent Neural Networks with Long Short-Term Memory (LSTM), for generating novel CDK1 inhibitors.
- To evaluate the binding affinity, molecular properties, and stability of generated compounds using computational methods.
Main Methods:
- Recurrent Neural Networks with Long Short-Term Memory (LSTM) for de novo drug design.
- Molecular docking to assess binding affinity to CDK1.
- Quantitative Estimate of Drug-likeness (QED) for property evaluation.
- Molecular dynamics simulations for stability and interaction analysis.
Main Results:
- Generated ligands demonstrated superior binding affinity to CDK1 (average -10.65 kcal/mol) compared to existing inhibitors.
- The novel compounds exhibited significantly higher drug-likeness (average QED 0.733) than known CDK1 inhibitors (average QED 0.547, p < 0.001).
- Molecular dynamics simulations confirmed the stability and favorable interactions of the designed ligands with the CDK1 complex.
Conclusions:
- Deep learning effectively generated novel CDK1 inhibitors with improved target affinity and drug-likeness.
- These findings offer promising candidates to address the unmet need for approved CDK1-targeted cancer therapies.
- Extensive experimental validation is crucial before clinical progression of these computationally designed compounds.
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