Machine Learning-Based Drug Repositioning of Novel Janus Kinase 2 Inhibitors Utilizing Molecular Docking and Molecular Dynamic Simulation

Abstract

Machine learning algorithms have been increasingly applied in drug development due to their efficiency and effectiveness. Machine learning-based drug repurposing can contribute to the identification of novel therapeutic applications for drugs with other indications. The current study used a trained machine learning model to screen a vast chemical library for new JAK2 inhibitors, the biological activities of which were reported. Reference JAK2 inhibitors, comprising 1911 compounds, have experimentally determined IC 50 values. To generate the input to the machine learning model, reference compounds were subjected to RDKit, a cheminformatic toolkit, to extract molecular descriptors. A Random Forest Regression model from the Scikit-learn machine learning library was applied to obtain a predictive regression model and to analyze each molecular descriptor's role in determining IC 50 values in the reference data set. Then, IC 50 values of the library compounds, comprised of 1,576,903 compounds, were predicted using the generated regression model. Interestingly, some compounds that exhibit high IC 50 values from the prediction were reported to possess JAK inhibition activity, which indicates the limitations of the prediction model. To confirm the JAK2 inhibition activity of predicted compounds, molecular docking and molecular dynamics simulation were carried out with the JAK inhibitor reference compound, tofacitinib. The binding affinity of docked compounds in the active region of JAK2 was also analyzed by the gmxMMPBSA approach. Furthermore, experimental validation confirmed the results from the computational analysis. Results showed highly comparable outcomes concerning tofacitinib. Conclusively, the machine learning model can efficiently improve the virtual screening of drugs and drug development.

Drug Repositioning via Graph Neural Networks: Identifying Novel JAK2 Inhibitors from FDA-Approved Drugs through Molecular Docking and Biological Validation

“…CDocker energy displays the overall docking energy based on the 3D structural and physiochemical features of the ligand and protein, whereas the strength and nature of each individual contact between the ligand and the receptor are revealed by CDocker interaction energy. It calculates how much the overall binding strength is affected by intermolecular forces such Van der Waals forces, electrostatic interactions, and hydrogen bonds [ 41 , 42 , 43 ]. The top 20 docking results concerning the CDocker interaction energy score were depicted in Table 3 .…”

Discovery of Novel Aldose Reductase Inhibitors via the Integration of Ligand-Based and Structure-Based Virtual Screening with Experimental Validation

“…The protocols for the MD simulation experiment were adapted as previously published data of a 100 ns simulation . The top compounds exhibiting the lowest docking energy were selected for a 100 ns MD simulation.…”

“…The protocols for the MD simulation experiment were adapted as previously published data of a 100 ns simulation. 20 The top compounds exhibiting the lowest docking energy were selected for a 100 ns MD simulation. Additionally, Epalrestat, serving as a reference compound, underwent MD simulations for comparative analysis.…”

“…Computer-aided drug design (CADD) has revolutionized drug discovery, providing a more efficient alternative to traditional methods. By utilizing advanced computational techniques, CADD rapidly screens compound libraries, reducing the need for labor-intensive laboratory testing . This expedites drug candidate identification, streamlining synthesis, and evaluation.…”

“…By utilizing advanced computational techniques, CADD rapidly screens compound libraries, reducing the need for labor-intensive laboratory testing. 20 This expedites drug candidate identification, streamlining synthesis, and evaluation. Integration of artificial intelligence, particularly deep learning (DL), further transforms drug development.…”