Perturbation Modeling for Molecular Design of Protein Tyrosine Kinase Inhibitors using Unsupervised Machine Learning

<p>The field of computational drug discovery and development has grown, with the aid of new computational tools for novel molecule discovery. In specific, generative deep learning models have excelled as tools to aid in navigating the large space of known molecules and in the creation of new molecules. These models are fed various representations of molecules as inputs and learn to perform a variety of things, such as the optimization of these molecules towards a targeted property. Ultimately, these generative learning models allow us to build bridges between chemical and continuous spaces to understand the compromise between invoking small incremental changes to radical modifications and generate optimal molecules for therapeutic benefits. The goal of this study pertains to creating a perturbation modeling framework in which we can conduct transformations of small molecules to SRC kinase inhibitors using a combination of a generative learning technique called Variational Auto-Encoders and Unsupervised Machine Learning techniques. This study focuses specifically using the methods above to transform molecules from various kinase inhibitor families to SRC Kinase Inhibitors. These generated molecules are evaluated using various physicochemical metrics and similarity metrics for validity of transformation. The results of this study demonstrate that Machine Learning based perturbation techniques can aid in the evolution of molecules from one chemical space to another. By combining Generative Learning frameworks with targeted based alteration of the continuous space, we allow for the emergence of novel molecules that are structurally adjacent to the various molecular scaffolds of SRC Kinase Inhibitors. Our findings also suggested that these vi molecules exhibit chemical and drug-likeness similarity to known SRC Kinase Inhibitors, displaying potential for synthetic and therapeutic benefits. Further studies should be conducted to determine whether the novel molecules that are generated contain potential to be synthesized for practical use.</p>

Name thesis:degree_name

Master of Science (MS)

Level thesis:degree_level

Thesis

Discipline thesis:degree_discipline

Computational and Data Sciences

Year dc:date.available

2022

Author dc:creator

• Krishnan, Keerthi

Contributors dc:contributor

• Gennady Verkhivker
• Cyril Rakovski
• Mohamed Allali

• Machine Learning
• Cheminformatics
• Generative Learning
• Protein Tyrosine Kinases
• Kinase Inhibitors
• Perturbation Modeling
• Biochemistry
• Biotechnology
• Data Science
• Molecular Biology
• Pharmaceutics and Drug Design
• Software Engineering

Repository record dc:identifier

https://digitalcommons.chapman.edu/cads_theses/16

OAI identifier oai:identifier

oai:digitalcommons.chapman.edu:cads_theses-1015