Logo image
Back
Discovery and design: an in silico approach to small molecule inhibitors
Dissertation   Open access

Discovery and design: an in silico approach to small molecule inhibitors

Jerica Katlynn Wilson
Doctor of Philosophy (Ph.D.), Drexel University
Sep 2022
DOI:
https://doi.org/10.17918/00001322
pdf
Wilson_Jerica_202237.26 MBDownloadView
PDF Open Access Open Access (License Unspecified)

Abstract

Drugs--Design Pharmaceutical chemistry Fragment-based drug design Small molecule inhibitors Structure-based drug design TIPE2
Drug discovery is a high-risk low reward process. To mitigate the financial repercussions and improve efficiency and efficacy, the continued incorporation of computer-based techniques is necessary. Molecular docking is an essential tool for medicinal chemists, enabling high throughput virtual screening of millions of ligands in fractions of the time and cost, in comparison to traditional screening methods. We have employed computer aided drug design to the fragment- based approach for the development of an inhibitor for the proangiogenic transport protein, TIPE2. Controlling the formation of both the leading and trailing edge of a polarized leukocyte, TIPE2 initiates a sequence of events ultimately leading to the sustainment of chronic inflammation and subsequently, tumorigenesis. Applying a quadrant grid system to our virtual screening has enabled a complete search of chemical space. High binding fragments with desired ADME properties were linked, forming a new ligand which was docked again with the protein. Ligands with the highest binding affinity underwent molecular dynamic studies to examine the stability of the binding complex. Additionally, utilizing reinforcement learning, molecular docking, and a combination of structure and fragment-based drug design techniques, we have developed a pipeline able to construct a higher binding ligand than the input screened ligands. The pipeline fragments libraries of screened drug-like ligands, identifying patterns of highest binding fragments in a protein's active site. Using reinforcement learning, the agent then builds a new ligand from the identified fragments. The pipeline has been executed with three different protein targets and has succeeded in constructing a ligand with greater binding affinity for each.

Metrics

Details

Logo image