Ensemble Guided Fine-Tuning Pre-Trained Models for Kinase Inhibitor Design

This study introduces an innovative framework, Ensemble Guide Fine-tuning (EGFit), for Pre-Trained Models, designed to address the challenges of data scarcity in kinase targeted drug discovery. Protein kinases play a pivotal role in cancer, immune diseases, and other complex disorders, making them a critical drug target. Despite over 100,000 recorded kinase inhibitors, only about 75 small-molecule kinase drugs have received FDA approval, underscoring the difficulty of developing kinase drugs. EGFit combines pre-trained large language models, with advanced machine learning techniques, including random forest, support vector machine, multilayer perceptrons, and logistic regression, to iteratively evaluate and refine generated compounds. Under limited data conditions, the framework efficiently explores a vast chemical space, producing biologically relevant and structurally diverse kinase inhibitors. Experimental validation on four kinases (EGFR, MET, PIM1, and CDK5) demonstrates significant improvements in compound similarity to known inhibitors while maintaining compliance with drug-likeness criteria. The iterative feedback mechanism further ensures chemical novelty and biological significance, showcasing the potential of EGFit to optimize compound generation for kinase-specific applications. This framework offers a scalable and effective solution to the challenges of kinase drug discovery, accelerating the development of novel therapeutics and paving the way for broader applications in future studies.