View Proposal

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Proposer

Chengjia Wang

Title

AI-aided drug discovery using graphical neural network: Retrosynthesis with simulated restriction

Goal

To review, implement and review existing retrosynthesis methods and their potential applications.

Description

As a fundamental problem in chemistry, Retrosynthesis is the process of decomposing a target molecule into readily available starting materials. It aims at designing reaction pathways and intermediates for a target compound. The goal of artificial intelligence (AI)-aided retrosynthesis is to automate this process by learning from the previous chemical reactions to make new predictions. Although several models have demonstrated their potentials for automated retrosynthesis, there is still a significant need to further enhance the prediction accuracy to a more practical level. This project aims to review, implement and review existing retrosynthesis methods and their potential applications.

Resources

https://pubs.acs.org/doi/full/10.1021/jacsau.1c00246 https://hfooladi.github.io/files/papers_retrosynthsis.pdf

Background

Url

Difficulty Level

High

Ethical Approval

None

Number Of Students

2

Supervisor

Chengjia Wang

Keywords

ai, machine learning, graphical neural network, deep learning, drug discovery, nlp,

Degrees

Bachelor of Science in Computer Science
Bachelor of Science in Computer Systems
Bachelor of Science in Information Systems
Master of Engineering in Software Engineering
Master of Science in Artificial Intelligence
Master of Science in Artificial Intelligence with SMI
Master of Science in Business Information Management
Master of Science in Computer Science for Cyber Security
Master of Science in Computer Systems Management
Master of Science in Computing (2 Years)
Master of Science in Data Science
Master of Science in Human Robot Interaction
Master of Science in Robotics
Master of Science in Software Engineering
Bachelor of Science in Computing Science
Bachelor of Engineering in Robotics