Decoding the Language of Nature Using Artificial Intelligence

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1. SUMMARY

Nature can assemble complex organic molecules from simple starting materials with apparent ease. Currently, there is no established technique that allows the function of a compound produced by nature to be predicted. My project presents an innovative approach using interdisciplinary techniques that can provide an answer. The project combines data on metabolites with analysis of 'Big Data' using artificial intelligence (AI). AI teases out meaningful patterns and useful knowledge from big data, leading to integrated relationships and logic links between the metabolite structures and functions. Technological advances in AI, especially in the field of deep learning, hold the potential to transform modern industrial development to support reasonable prediction. The outcome is that deep learning-based algorithms predict the function of metabolites based on their chemical structures. This provides significant knowledge to translate many more naturally produced compounds into sustainable products resulting in replacement of synthetic compounds by clean green environmentally-friendly products.

2. BENEFIT

This project contributes to Australia’s cultural, industrial, economic and social advancement by using existing Australian talent, expertise and capacity in the foundation discipline of chemistry, particularly organic chemistry and chemical biology, which includes natural products chemistry, to facilitate academic learning and translation to industry with the very latest ability to use artificial intelligence to interrogate large datasets to predict structure- function relationships. This project also ensures that Australian researchers are fully conversant with modern chemical technologies and the latest ‘Big Data’ approaches applicable to academia and industry, and are experienced in identifying and pursuing important challenges in basic and applied science. Success will lead to a heightened local and international awareness of Australian scientific capabilities, which will in turn lead to new R&D opportunities and improved access to research funding, infrastructure, employment and training.


The goal of my project is to develop an innovative understanding of the function of the metabolites produced by nature. Once the function of metabolites can be rationally predicted, using Artificial Intelligence, there will be increased Australian capacity for a variety of purposes. Currently agricultural industries rely upon agrochemicals to maximise yields but many of these cause unintended environmental damage. Replacing agrichemicals with naturally sourced products may enable better safeguard of the natural environment. Increasing knowledge of the function of metabolites is important for bracing our agriculture sector for future food security. My project combines strengths in artificial intelligence (AI) with knowledge of metabolites to create a new area of science – predicting the function of molecules – and aims to develop a comprehensive discovery platform to define the best AI algorithms and the experimental data that best allows AI to achieve such predictions. Training in the multidisciplinary approach will provide increased job prospects for Australians in Queensland.


The predicative value of machine learning for deep extraction of connectivities embedded in genomics and proteomics data is well recognised and utilised. For linear information molecules such as nucleic acids and proteins, the primary sequence structure can be readily digitised for computation and AI algorithms. For nonlinear information molecules such as natural products that are not amenable to linear coding, various manifolds of data sets have to be first collected, cross annotated, assembled and then multiplexed to capture, as much as possible, about these metabolites and their interactive partners, before the next set of AI algorithms can be implemented. While the scale and complexity of this deep learning may be unprecedented and daunting, it is the next grand challenge that will push functional chemogenomics and chemoproteomics into a new future. More importantly, the ability to predict function from structure for molecules such as natural products will forge a new paradigm for finding the next generations of precision medicines.

3. ENGAGEMENT

I’m the first and so far the only women in my family who has obtained a PhD degree. I decided to study for a PhD and continue to work as a postdoc because I wanted to know more, I wanted to figure out things and for that reason, research is the best way. Even though I do have deadlines, I still have enough time and space to spend on developing thoughts that simply seem interesting to me. Also, being able to work on my project in all the freedom I like is the most ideal work for me. During the past several years, I have been developing many skills much more than I expected to do. I've had the opportunity to attend workshops and trainings, but I have also had the opportunities to bring into practice what I've learned from these workshops, such as presenting in front of various audiences, traveling to conferences and working several projects at the same time.

What I love about being a scientist is the wide variety of responsibilities that I get to take on. I of course spend a lot of time in the lab, designing and performing experiments to find answers to the questions that I’m asking. Apart from my own research, I’m also very passionate about communicating my work and science in general, with a range of audiences. I have organised and participated in various STEM activities within my institute as well as outside. I’ve been able to talk to the public and students including high school students about my research. When I was in high school, I didn’t know anyone in research science and actually I’m the first scientist in my family. I find it so rewarding to talk to people about my research because it’s an opportunity I never had. I hope that one day I might inspire someone just as I was inspired in my career.

I have received many encouragement and inspirations from my peers and mentors, and now I hope to inspire others, especially female young scientists. I’m doing something that I never heard of when I was young. Being a scientist, not only makes a big difference to our own lives, but also will have a chance to change the world and solve tomorrow’s challenges. Research is a team effort and I am fortunate enough to work with a great group of people who share the same ambition – to create knowledge that transforms lives.

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