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Datachemical LAB Case Study Interview
From the Front Lines of Research and Education
Prof.
Tatsuya Oshima
Professor,Department of Engineering,
Applied Chemistry Program,
University of Miyazaki
×
Shogo Yoshimaru
CEO & Representative Director, Datachemical, Inc.
Background
"No-Code × Materials Informatics" Expanding into Research and Education
— Embracing AI in Education: Miyazaki University's Challenge to Cultivate Students' Ability to "Think with Data" —
Materials Informatics (MI), an essential driver of accelerated materials development, is now making its way into educational settings — not just industry. However, introducing MI into curricula is no easy feat, given the high barriers of specialized knowledge and programming skills (such as Python) required.
In this interview, Professor Tatsuya Oshima of the Faculty of Engineering at Miyazaki University shares how he has integrated Datachemical LAB — a no-code MI tool — into his laboratory research and university lectures. We explore how students took on the challenge of "defining their own problems and verifying hypotheses with AI."
What does it take to cultivate the ability to "think with data"? What real-world results has this approach delivered, and what does the future hold? Discover the frontlines of educational digital transformation.
Speakers
Guest
Prof. Tatsuya Oshima —
Professor, Chemical Life Program, Faculty of Engineering, University of Miyazaki
Interviewer
Shogo Yoshimaru —
CEO, Datachemical Inc.
At the Intersection of Experimental Science and Data Science: A Conversation on Research and Education Today
Table of Contents
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What sparked the use of machine learning in research
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Student-led machine learning enabled by Datachemical LAB
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Real results and the reality of data utilization in education
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Experimental science and machine learning: confronting the challenge of limited data
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How to engage with AI and machine learning: the mindset tomorrow's students need
1. What Sparked the Use of Machine Learning in Research
Yoshimaru (Moderator):
Thank you for your time today. It has been about three years since your university adopted Datachemical LAB. To start, could you tell us how you have been using it in your own research?
Prof. Oshima:
My research focuses on solvent extraction of metal chloride complexes. Conventionally, attention in this field tends to center on the design of metal complexes themselves. However, I realized that the physical properties of the solvents themselves are critical parameters in metal extraction, which led me to start collecting and organizing solvent data.
I initially used solubility parameters to qualitatively organize extraction capacity, but I thought there might be room to go further — and began exploring whether I could quantitatively predict extraction rates.
Yoshimaru:
And that's when you turned to machine learning?
Prof. Oshima:
Exactly. At the time, the assumption was that you had to write code in Python. Since our lab — myself included — doesn't have a background in computer science, sustaining that approach was genuinely difficult. Then Datachemical LAB was released, and I thought, "This we can work with." From there, machine learning adoption in our lab expanded rapidly.
2. Student-Led Machine Learning Enabled by Datachemical LAB
Yoshimaru:
How did things change after you introduced Datachemical LAB?
Prof. Oshima:
The biggest difference was simply that no programming is required. That removed an enormous barrier for students. For chemistry students, learning Python from scratch just to build a model is a very high hurdle. But with Datachemical LAB, no prior background is needed.
As a result, going back to Python is no longer even an option for us. Our policy is: if we're doing machine learning, we use Datachemical LAB.
Yoshimaru:
Has this translated into tangible results?
Prof. Oshima:
We started with an extraction rate prediction model that achieved an R² of around 0.94. Since then, the volume of data students handle has grown and our validation methods have evolved. We are now building models with R² values of 0.98 to 0.99 — quite high accuracy.
3. Real Results and the Reality of Data Utilization in Education
Yoshimaru:
Your university has signed a partnership agreement with us with the aim of collaborating on education. As part of that, this year you actually used Datachemical LAB in the classroom.
Prof. Oshima:
Yes. The Faculty of Engineering at Miyazaki University has been advancing DX education initiatives, and we incorporated Datachemical LAB into one of our laboratory exercises. Previously, there had been plans to have students write Python code, but the time and technical constraints made that impractical.
Yoshimaru:
And Datachemical LAB was able to bridge that gap.
Prof. Oshima:
Exactly. This time, we had students work through the full process — from model construction to validation — using "prediction of aqueous solubility of organic compounds" as the theme. For many students, it was a completely different experience from conventional lab work.In particular, being able to retrieve data instantly from a CSV file, and the workflow of extracting features from molecular structures to make predictions — all of that seemed fresh to them. You could see students genuinely surprised, thinking, "We can actually do this?"
【Exercise Overview】
Prediction of logS (aqueous solubility) of organic compounds
Using structural data (SMILES notation) and aqueous solubility data for 800 existing organic compounds as training data, students experienced machine learning prediction through the following steps:
①Descriptor calculation: Generated 208 molecular descriptors from organic compound structures in the training data using RDKit
②Preprocessing: Performed feature selection to remove unnecessary descriptors and organize data for analysis
③Data visualization: Used correlation heatmaps, scatter plots, and other tools to understand the characteristics of the feature set
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④ Model construction & prediction: Built a predictive model using the training data and predicted the logS values of compounds each student selected as their target
⑤ Validation: Compared prediction results with literature values and examined the relationship with molecular structure
Laboratory exercise session using Datachemical LAB
4. Experimental Science and Machine Learning: Confronting the Challenge of Limited Data
Yoshimaru:
What do you see as the key challenges in applying machine learning to research and education?
Prof. Oshima:
Data volume is a real challenge. Experimental research is inherently limited in the number of data points available. Compared to research that generates tens of thousands of data points through high-throughput methods, the conditions are inevitably more restrictive.
The solvent screening work we do in metal extraction happened to be a theme where relatively large datasets could be collected, which made it well-suited to machine learning. So now, I'm always thinking about what kinds of research themes can realistically generate enough data and are amenable to modeling.
Yoshimaru:
It really comes down to having the perspective to assess, based on the nature of the research and its practical realities, how much data can actually be obtained. And it is precisely in those settings — where data is limited — that we believe Datachemical LAB can make a real contribution. We have continually refined our features to enable accurate predictions even with small datasets, and we look forward to continuing to expand the possibilities in this field through close collaboration with research and education communities.
5. How to Engage with AI and Machine Learning: The Mindset Tomorrow's Students Need
Yoshimaru:
Finally, do you have any words for students who are beginning to study data science?
Prof. Oshima:
First and foremost: don't over-rely on AI or machine learning. Even in our student exercise, the prediction accuracy was good — but the model was far from perfect. And that's fine. In fact, experiencing firsthand that "things don't always work out" is precisely the point.
Yoshimaru:
I see — that's what it really means to master these tools.
Prof. Oshima:
I think so. Machine learning is an extension of statistics, so results aren't everything — what matters is how you think from there. As AI advances and the importance of data science grows, an exclusive reliance on conventional theoretical and scientific approaches will no longer suffice. Actively incorporating statistical methods is becoming indispensable. So I want students to carry a genuine awareness of "mastering AI." Used well, it is a powerful tool — but it should never be something that controls you. I want them to engage proactively, with a healthy sense of distance.
Looking ahead, I also hope to develop more practical educational package materials with a view toward broader use in academic settings.
Yoshimaru:
That sounds like a wonderful initiative. We at Datachemical would love to provide more opportunities for Datachemical LAB to be used broadly in university education — and we would be very glad to support your efforts in developing those materials.
Prof. Oshima:
I would greatly appreciate that.
Yoshimaru:
Thank you so much. Your sincere dedication to both research and education has been a great source of inspiration for all of us.
Prof. Oshima:
Thank you as well. It has been a pleasure.
Company Overview
Company Name: Datachemical Inc.
Address: Kuwano Building 2F, 6-23-4 Jingumae, Shibuya-ku, Tokyo 150-0001, Japan
Phone: +81-3-6778-2045 CEO: Shogo Yoshimaru
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