LPIXEL aims to solve the challenges lying ahead for the drug discovery research
with our top-tier image analysis technology powered by Artificial Intelligence.
We accelerate joint researches with our partners,deliver innovative solutions and
create new standards.
Why you can rely on LPIXEL
tailored to each customer
Create whole new standards by proposing best ways tailored to customers to gain and collect data in an experimental design and AI learning and by setting an appropriate subject .
Specialists in Life Science
and Artificial Intelligence
Mainly in partneship with academia and research institutions in and outside of Japan, LPIXEL conducts numerous joint researches with pharmaceutical companies. Our seasoned and experienced specialists support you in implementating AI.
More than 100 joint researches
Mainly in partneship with academia and research institutions in and outside of Japan, LPIXEL conducts
numerous joint researches with pharmaceutical companies.
Our seasoned and experienced specialists support you in implementating AI.
※ In alphabetical order
How to make
the most of AI in a drug discovery process
AI utilization make it possible to reach the performance of automation the same as both recognition and quantification of researcher.
Furthermore, by executing huge amount of tasks that researcher cannot, AI can find essential difference in targets.
Very few candidate compounds
NON CLINICAL TEST
Reproducibility and safety
Increase in test cost
Decline in success probability
Quality of regenerative medicine
Quality of cell preparation
Drug price reduction
Depending on your needs, LPIXEL gives you a conprehensive support, from plan, PoC to a product
implementation to achieve open innovation making use of AI technologies with a variety of companies.
Whether an image needs to be analyzed
1 - 2 months
Number of images
About 20 - 100
Evaluation of performance for implementation
3 months ~
Number of images
About 200 - 500
Evaluation analysis report
6 months ~
Number of images
About 500 - 1000
*1：Price differs depending on requirement, degree of difficulty, and duration.
*2：While property rights and intellectual property rights of the model and the know-how gained through each process belong to LPIXEL, user rights belong to customers as a precondition.
Support measures examples
Based on morphology of cells, evaluation of influence of compound
Recognition of each cell and quantification of cellular morphologies
automation of recognition of micronucleus
automation of test of genotoxisity by automatic recognition of micronucleus
recognition of cells, nucleus, and micronucleus on whole slide image
Takeda Pharmaceutical Company Limited
Oral presentation at mammalian mutagenesis study group on 2018
Collaboration of Life intelligence Consortium
Safety test for side effect of drug candidate on organs. Automatic finding region of side effect on organs
comparing normal structure with abnormal structure on organs
Daiichi-sankyo, Otsuka, Fujitsu, Sysmex
Oral presentation at 35th annual meeting of the japanese society of Toxicologic pathology
Dr. Shimahara completed his graduate studies and Ph.D. at the Graduate School of Frontier Sciences at the University of Tokyo. Dr. Shimahara started his career at GREE Inc., where he took part in business strategy and human resources management. At the second IT firm, he gained experience in global business development. Dr. Shimahara later established LPIXEL Inc. in March 2014 along with his two colleagues studying in the same university laboratory. Dr. Shimahara was selected to join the Next Innovator 2015 Project in Silicon Valley, which was hosted by the Japan METI. Dr. Shimahara was also featured in Forbes Asia 30 under 30.
Dr. Kutsuna completed his graduate studies and Ph.D. at the Graduate School of Frontier Sciences at the University of Tokyo. Dr. Kutsuna also serves as an Associate Professor at the University of Tokyo. In 2001, Dr. Kutsuna participated in a study which was adopted by Exploratory IT Human Resources Project (IPA). Dr. Kutsuna’s works have also received honorable mentions in business competitions in Japan. Aside from academia, Dr. Kutsuna has won in Japanese computer Shogi competitions.
Dr. Hakamada received his Ph. D. from the Graduate School of Systems Life Sciences at Kyushu University. Dr. Hakamada served as assistant professor at University of Tokyo and Osaka University. At both universities, he researched the relationship between gene expression and cell division by using time lapse imagings of cells. Prior to joining LPIXEL, Dr. Hakamada served as senior researcher at IVD company and researched recognition of pathological images by deep learning. Dr. Hakamada was hornored with the excellent paper award at the Society for Biotechnology Japan in 2011.
Dr. Kawai completed his graduate studies and Ph.D. at the Graduate School of Frontier Sciences at the University of Tokyo, where Dr. Kawai engaged in a research of homeostatic regulation of neural stem cells. Dr. Kawai performed as a research fellow at the Japan Society for the Promotion of Science (DC1), as well as a researcher at the University of Tokyo conducting research on neural stem cells. Dr. Kawai currently serves as a visiting researcher at the University of Tokyo.
As a Ph.D. holder, as well as a pharmacist, from the University of Tokyo in Pharmaceutical Sciences, Dr. Sugawara currently works as a researcher in the field of biophysics. His research revolves around the nanoscale localization and dynamics analysis of intracellular mRNA using single-molecule fluorescence microscopy.
Dr. Han obtained his Ph.D. degree in Computer Science from The University of Tokyo in 2020. Changhee has expertise in Machine Learning, especially Deep Learning for Medical Imaging and Bioinformatics, where he is working on 2D/3D MR/CT image augmentation/classification/detection/segmentation, and operon optimization, as a project leader of several international projects in collaboration with various institutes.
We provide optimal solutions by applying image analysis and artificial intelligence technologies in all life science fields such as medicine, pharmaceuticals, agriculture, chemistry, and food.