Development of Virtual Screening and In Silico Biomarker Identification Model for Pharmaceutical Agents

Abstract

Virtual screening (VS) especially machine learning based VS is increasingly used in search for novel lead compounds. In this thesis, we introduced a new two-step support vector machines target-binding and selectivity screening method for searching dopamine receptor subtype-selective ligands and demonstrated the usefulness of the new method in searching subtype selective ligands from large compound libraries. In order to reduce the cost and time in developing novel IKK? inhibitors, the machine learning method is used to build a prediction and screening model of IKK? inhibitors.

Some drugs such as anticancer EGFR tyrosine kinase inhibitors elicit markedly different clinical response rates due to differences in drug bypass signaling as well as genetic variations of drug target and downstream drug-resistant genes. In this thesis, we systematically analyzed expression profiles together with the mutational, amplification and expression profiles of EGFR and drug-resistance related genes and investigated their usefulness as new sets of biomarkers for response of EGFR tyrosine kinase inhibitors.