Q&A with Justin Grace, Senior Data Scientist @ LabGenius
Back in June, we welcomed Justin Grace to the LabGenius Team as a Senior Data Scientist. Justin joins us from Healx, where he was utilizing data to repurpose existing drugs. With over 12 years of experience in developing and deploying machine learning and data science solutions, Justin will play a key part in delivering our ambitious internal and partnered programmes.
Let’s start with an easy one, can you tell us a bit more about your career to date and what first attracted you to the role at LabGenius?
My early career was focused on computational methods and their application in the understanding of the brain and human health. I developed tools to help unpick the complexities of health and disease in various neurological conditions, including phobia, addiction, stroke and progressive supranuclear palsy.
I went on to work in a variety of industries as a Data Scientist and Machine Learning Engineer, with a focus on deep learning methods applied to sequential data.
Prior to LabGenius, I worked at a biotech called Healx, where I contributed to large-scale information extraction and search systems to build knowledge graphs for drug repurposing.
I joined LabGenius for a number of reasons — in particular for the opportunity to apply machine learning to the design of protein-based therapeutics with the goal of accelerating the development of novel drugs to treat cancer.
How have you found the first couple of months in the role, and what has been your main focus?
It’s been a fast-paced few months, culminating in the generation of new molecules that are currently being produced in the wet-lab, and will soon be ready for analysis. It’s been exciting to be so close to the action and to have the opportunity to see the outputs of our computational work turned into potential drug candidates.
The LabGenius team is a wonderful group of people and I enjoy working in such a supportive environment with a lot of agency to help influence and grow the company.
Can you tell us about the part your role plays in achieving our mission?
The Data Science Team are responsible for collating and analyzing assay data that’s generated by running experiments on our in silico designed molecules. We use this data to develop machine learning technology that predicts novel molecule designs with better performance characteristics, which are shared with the wet-lab teams for production and analysis. This continuous design-build-test-learn cycle is the beating heart of LabGenius.
Where do you see LabGenius four years from now and what excites you about the company’s future?
In four years we plan to have a number of our antibody-based immune cell engager drug candidates in clinical trials. We envisage data science and machine learning playing an integral role in every step of the discovery process, from helping scientists select targets to optimizing our protein sequences and analyzing vast swathes of data produced in our fully automated lab.
Our computational methods will run simulated assays allowing us to search an otherwise infeasible number of designs, leaving our wet-lab to focus on running physical experiments on the best candidates.
Where do you think machine intelligence will have the biggest impact on the discovery and development of new drugs?
Drug discovery is essentially a complex search problem — we will always need to run physical experiments but it is intractable to solve this search problem with brute force alone. Machine intelligence can help by guiding our search so that we can focus the costly wet-lab process on candidates that have a high probability of success.
This approach enables us to run more projects in parallel, thereby increasing the likelihood of discovering effective drugs.
Furthermore, traditional rational design approaches, driven by human expertise alone, are unable to co-optimize the high dimensional feature space that our computational models consider. We marry human expertise with machine intelligence — our approach is capable of efficiently exploring both the typical rational design space and non-intuitive design space. This approach has the potential to lead us to good candidates that would not be proposed by human expertise alone.
Do you have plans to grow your team?
Data science and machine learning are at the very core of several internal ongoing drug discovery projects. With that in mind, we are always on the look-out for talented Data Scientists, Machine Learning & Software Engineers that come from an R&D environment and are passionate about being at the forefront of this intersection between science and technology.
Thank you for your insights Justin, and welcome to the team!
To finish, we asked our Founder & CEO, Dr. James Field, to comment on the importance and impact of the Data Scientist role at LabGenius.
“The role of Data Scientist is critically important to delivering our ambitious internal and partnered programmes. Justin and his team are helping LabGenius realize the vast potential that computational methods have to accelerate the discovery and development of advanced therapeutics.”
