Create an Engine of
Scientific Discovery

Co-Creating the Future of Beauty: SBX and POLA Chemical Industries Unveil AIM POLAR

Read the full press release from POLA CHEMICAL INDUSTRIES. here (in Japanese).

SBX is proud to announce our role in the launch of AIM POLAR, a revolutionary R&D platform unveiled by our partner, the cosmetics leader POLA CHEMICAL INDUSTRIES. The two companies jointly built this platform, combining POLA CHEMICAL INDUSTRIES deep industry knowledge with SBX’s technical expertise. In a true co-creation effort, SBX was responsible for the complete technical development of the system, from the powerful AI engine at its core to the intuitive user interface that brings its capabilities to life.

POLA CHEMICAL INDUSTRIES entrusted SBX with their vision for a transformative tool that could digitize their deep formulation expertise. Our team custom-built the backend AI engines to translate POLA CHEMICAL INDUSTRIES’s vast historical data into precise predictions for product texture, safety, and stability. In parallel, we designed and developed the intuitive touchpoint for POLA CHEMICAL INDUSTRIES’s researchers to have a seamless and powerful way to interact with the AI’s insights.

Agentic AI in Healthcare Workshop in Singapore

Seminar/workshop in Singapore, August 29th, 2025

IMDA Hosts Seminar on Agentic AI in Healthcare; SBX to Present

The Infocomm Media Development Authority (IMDA) will host a full-day seminar on Agentic AI in Healthcare, on August 29th, 2025, exploring how these technologies can transform healthcare operations and decision-making. The SBX team is proud to be a key presenter, sharing insights from our real-world experiences.

New Publication on mutagenenicity prediction…

A new paper is now published by our researchers from our research collaboration project.

Acharya, S., Shinada, N.K., Koyama, N. et al. Asking the right questions for mutagenicity prediction from BioMedical text. npj Syst Biol Appl 9, 63 (2023). https://doi.org/10.1038/s41540-023-00324-2

We are excited to share our latest publication in Nature Systems Biology and Applications! This work is a part of the incredible journey we've been on for the past many years, striving to harness the power of AI for risk assessment in Toxicology.

New Publication on Mutagenesis “Optimizing machine-learning models for mutagenicity prediction through better feature selection”

A new paper is now published by our researchers from our research collaboration project.

Nicolas K Shinada, Naoki Koyama, Megumi Ikemori, Tomoki Nishioka, Seiji Hitaoka, Atsushi Hakura, Shoji Asakura, Yukiko Matsuoka, Sucheendra K Palaniappan, Optimizing machine-learning models for mutagenicity prediction through better feature selection, Mutagenesis, 2022;, geac010, https://doi.org/10.1093/mutage/geac010

Abstract
Assessing a compound’s mutagenicity using machine learning is an important activity in the drug discovery and development process. Traditional methods of mutagenicity detection, such as Ames test, are expensive and time and labor intensive. In this context, in silico methods that predict a compound mutagenicity with high accuracy are important. Recently, machine-learning (ML) models are increasingly being proposed to improve the accuracy of mutagenicity prediction. While these models are used in practice, there is further scope to improve the accuracy of these models. We hypothesize that choosing the right features to train the model can further lead to better accuracy. We systematically consider and evaluate a combination of novel structural and molecular features which have the maximal impact on the accuracy of models. We rigorously evaluate these features against multiple classification models (from classical ML models to deep neural network models). The performance of the models was assessed using 5- and 10-fold cross-validation and we show that our approach using the molecule structure, molecular properties, and structural alerts as feature sets successfully outperform the state-of-the-art methods for mutagenicity prediction for the Hansen et al. benchmark dataset with an area under the receiver operating characteristic curve of 0.93. More importantly, our framework shows how combining features could benefit model accuracy improvements.

JSOT 2021

Our collaboration work with Eisai was presented at JSOT 2021 (The 48th Annual Meeting of the Japanese Society of Toxicology) on 2021/07/08

Development of AI mutagenicity prediction system incorporating the knowledge of expert review
○ Naoki KOYAMA1 , Megumi IKEMORI2 , Tomoki NISHIOKA3 , Seiji HITAOKA3 , Atsushi HAKURA1 , Chihiro NAKAZAWA1 , Chakravarti K SUMAN4 , Saiakhov D ROUSTEM4 , Shinada K NICOLAS5 , Palaniappan K SUCHEENDRA5 , Yukiko MATSUOKA5 , Shoji ASAKURA1 11 Global Drug Safety, Eisai Co., Ltd., 2 Planning & Operation. hhc Data Creation Center, Eisai Co., Ltd., 3 5D Integration Unit, hhc Data Creation Center, Eisai Co., Ltd., 4 MultiCASE Inc., 5 SBX Corporation