Date of Award

Spring 2022

Document Type

Thesis

Publisher

Santa Clara : Santa Clara University, 2022.

Departments

Bioengineering; Computer Science and Engineering

First Advisor

Silvia Figueira

Second Advisor

Zhiwen (Jonathan) Zhang

Abstract

Modern antibacterial drugs are quickly becoming insufficient for medical, agricultural and veterinary use, and the drug design techniques used to supplement the supply are not able to adequately and quickly produce enough effective candidates. We took a multi-pronged approach to remedying this problem, including shifting the target mechanism from antibiotic drugs in favor of anti-infective drugs, developing an AI which aimed to predict the interactions of small molecule fragments with the target protein, and refining wet lab testing protocols in order to make the drug design process holistically more efficient and effective. We have shown that there is potential in this design space, as the design techniques are faster and more effective than those previously used, and the drug candidates are promising as they undergo further testing. Research into broader applications of this technology to other target proteins and expansion of the AI's scope in specific applications will allow for fast and safe design of life-saving drugs.

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