An integrated deep learning and molecular dynamics pipeline for the discovery of novel CDK8 inhibitors against acute myeloid leukaemia.
TL;DR
An integrated computational pipeline combining neural network-based potency prediction with molecular dynamics simulations for CDK8 inhibitor discovery was developed and novel molecular structures beyond the training distribution were generated.
Abstract
Cyclin-dependent kinase 8 (CDK8) has emerged as a promising therapeutic target for acute myeloid leukaemia (AML). We developed an integrated computational pipeline combining neural network-based potency prediction with molecular dynamics (MD) simulations for CDK8 inhibitor discovery. A curated dataset of 1,200 unique CDK8 inhibitors was assembled from ChEMBL. The optimal neural network architecture (two hidden layers, 512→128 units) with dropout regularization achieved a test set r2 of 0.47 and RMSE of 0.87. Y-randomization testing confirmed genuine structure-activity relationships. Using an extrapolation-focused training strategy combined with a SELFIES-based genetic recombination algorithm, we generated novel molecular structures beyond the training distribution. SHAP analysis revealed fingerprint bits as critical determinants of potency. Applicability‑domain analysis confirmed that the novel hit falls within validated chemical space. The identified candidate exhibited a predicted pIC50 of 11.97, substantially exceeding the most potent training compound (pIC50 = 10.09) and compound 12 (pIC50 = 7.47). Molecular docking revealed Moldock scores of -143.9 kcal/mol for the novel hit versus -113.9 kcal/mol for compound 12. MD simulations demonstrated stable binding with the novel hit forming a highly stable hydrogen bond with Asp98. MM-GBSA calculations showed superior binding free energy for the novel hit (-99.10 vs. -47.77 kcal/mol).