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5 minute read
From genetic analysis to new drug development 3billion interview
Journalist | Jinyoo Park | truthfree@yonsei.ac.kr
Designer | Jinyoo Park | truthfree@yonsei.ac.kr
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What drugs will pharmaceutical companies develop and generate profits from in the future? Looking at the world trends, cancer, mental illness, and rare diseases are receiving a lot of investment. In addition, in the Corona era, the development of new drugs using artificial intelligence has received a lot of attention. This is because of the advantages of saving time and money. There is a company that has captured both future food and artificial intelligence, 3 Billion. The story of 3 Billion was captured through an interview with 3 Billion’s artificial intelligence team leader and current CSO KyoungYeul Lee.
Introducing the interviewee
KyoungYeul Lee, (Kyle Lee)
2008~2012 : KAIST Undergraduate student
2012~2014 : KAIST Master student
2014~2020 : KAIST Researcher Ph.D. student
2020~2023 : 3billion AI Team Leader
2023~ : 3billion Chief Scientific Officer
Keyword of 3billion: Rare disease
What is target of 3billion’s?
He introduced 3billion as a company that aims to “diagnose and treat patients with rare diseases.”
There are about 7,000 to 8,000 rare diseases, and 250 new ones are discovered every year. It is difficult to make a diagnosis according to the speed at which the disease is discovered. Because Diagnosis of rare genetic diseases is necessary to verify which genes cause diseases in a large amount of genetic information and whether they actually affect them.
“The diagnosis of a rare disease is a task that requires a lot of calculation compared to common diseases that are diagnosed through symptoms. That is why artificial intelligence can only be applied more and more,” he explained.
3billion rare disease diagnosis assistance
3billion’s diagnosis assistant provides a report summarizing the patient’s expected disease name and causative genetic mutations through sequencing of the genetic sample and internal data processing when the patient’s genetic sample and symptoms are provided.
What stands out among them is the daily re-diagnosis service.
Regarding the daily re-diagnosis service, he said, “3billion’s database improves as more genomes are tested. Therefore, a patient’s specimen that was not diagnosed in the past can be diagnosed after a certain period of time. We provide a daily re-diagnosis service that stores the data of the specimens that have been tested and re-diagnoses as the database is updated”
3billion’s artificial intelligence team
3billion’s artificial intelligence team can be largely divided into diagnosis and new drug development.
In the field of diagnosis, a mutation pathogenic prediction model using deep learning is developed. 3Cnet was developed that way. 3Cnet is an AI variant pathogenic predictor using Clinical Data, Common Variants, and Conservation Data. If this model simply produced scores, 3billion is aiming for explainable AI with the following model. AI learning has become so complex that it can no longer be understood at the human level. This is expressed as a black box, something that cannot be looked into. Explainable artificial intelligence is to improve this and explain the decision-based indicators from which artificial intelligence derived scores so that artificial intelligence users can use them with more confidence, or further utilize them.
In the new drug development AI team, we are developing our model for drug creation verification and new drugs. Some of the drug design done by medicinal chemists in the past was entrusted to artificial intelligence.
He emphasized that the database of biology itself is highly biased, especially in the field of new drug development.
Since the learning data of artificial intelligence already has a bias, the learning result also has a bias and presents a structure similar to the existing one rather than a creative structure.
Regarding these issues, “The structure of newly approved drugs that have passed clinical trials is not confined to the bias of the database. To break away from the bias of the existing database, we are trying to create a realistic synthesis and new structure as much as possible by developing correction artificial intelligence.” He replied, adding that artificial intelligence is presenting drugs that can be synthesized with a probability of about 90%.
Pharmacist with 3billion 3billion has 2 pharmacists working. One is on the diagnosis side, contributing to the final judgment based on an understanding of rare diseases. The other person belongs to the artificial intelligence team and checks whether there are any problems with the drug in the medicinal chemistry project and whether the results of the insilico are inconsistent with reality.
“It’s called Human in the Loop,” says Kyle Lee. “At the end of the day, AI gets better and better when people are in the process. If artificial intelligence makes a new drug, experts should evaluate it and make up for the weaknesses of artificial intelligence.” he said, mentioning the need for experts.
Pharmacists working on the AI team are selected through practical tests. Although programming skills are not looked at during subsequent interviews, it is said that pharmacists who want to enter 3billion are usually interested in programming. That’s because 3billion is a computer-based company rather than an experiment-driven.
He said, “3billion has an artificial intelligence team, as well as a mix of people who have specialized in other fields such as chemistry. In this environment, it seems that we grow while interacting with each other,” explaining the learning atmosphere of the company.
How should pharmacy students prepare for artificial inteligence?
With artificial intelligence receiving attention, more and more pharmacy students are setting their careers in the field of artificial intelligence. For those pharmacy students, Kyle Lee emphasized the background.
He said, “I think there are three necessary abilities. The first is background knowledge of biology. After all, knowledge is fundamental. Data is more important than models, and knowledge is more important than data. Without knowledge, no matter how good the data is, it cannot be accurately interpreted. After that, you need mathematical and statistical skills to determine if the data is really meaningful. After that, you develop your computational abilities to solve problems you want to solve through programming. Once this is in place, you will be an AI engineer in no time. An essential skill of an AI engineer is to define and solve problems. But it’s not just simple computational knowledge. Understanding the field is a priority. Computational knowledge is so easy to learn these days. So, take your time and polish the background knowledge first.”
How far can new drug development using artificial intelligence go?
As AlphaFold, a protein structure prediction artificial intelligence, predicts most protein structures, studies on interactions beyond this are being conducted.
Regarding the trend in the industry, he said, “The focus has already shifted towards interactivity. Structural prediction is just the beginning, and it’s time to look at the next interaction. But it is also growing at a tremendous rate. When artificial intelligence is applied, results are showing that it is more accurate than algorithms made with pure calculations. The future solution is to have multiple types of interactions. There are many types of bonds besides protein-protein, protein-lipid, etc. There are so many different types, so I thought there was a lot to develop.”
Also, when asked how far artificial intelligence can go in this field, he said, “Until now, using computer-designed proteins and RNAs as drugs was a fantasy, and it was simply a matter of research. But now it seems that we are getting closer to the time when it will be used in drugs. At the current pace of development, eventually drugs created by drug generation models will account for the majority of approved drugs.” B