A Workflow Combining Machine Learning with Molecular Simulations Uncovers Potential Dual-Target Inhibitors against BTK and JAK3.

Abstract

The drug development process suffers from low success rates and requires expensive and time-consuming procedures. The traditional one drug-one target paradigm is often inadequate to treat multifactorial diseases. Multitarget drugs may potentially address problems such as adverse reactions to drugs. With the aim to discover a multitarget potential inhibitor for B-cell lymphoma treatment, herein, we developed a general pipeline combining machine learning, the interpretable model SHapley Additive exPlanation (SHAP), and molecular dynamics simulations to predict active compounds and fragments. Bruton's tyrosine kinase (BTK) and Janus kinase 3 (JAK3) are popular synergistic targets for B-cell lymphoma. We used this pipeline approach to identify prospective potential dual inhibitors from a natural product database and screened three candidate inhibitors with acceptable drug absorption, distribution, metabolism, excretion, and toxicity (ADMET) properties. Ultimately, the compound CNP0266747 with specialized binding conformations that exhibited potential binding free energy against BTK and JAK3 was selected as the optimum choice. Furthermore, we also identified key residues and fingerprint features of this dual-target inhibitor of BTK and JAK3.