News

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

SBX collaborate with POLA for their “AI for sensory design”

POLA Chemical Industries Inc, has built a “AI for sensory design” in collaboration with SBX.
“AI for sensory design” (感触づくりAI)can predict the sensory score from the cosmetic formulation information. This AI implementation enables efficient and accurate in-silico prototyping, thus marks a breakthrough in environmental-friendly cosmetics development.

For details, please refer to the POLA press release http://www.pola-rm.co.jp/pdf/release_20210729.pdf (in Japanese)

Thirty-Fifth Annual Conference on Innovative Applications of Artificial Intelligence (IAAI-21)

Scientific discovery has been a driving force of our civilization. Accelerating scientific discovery is one of the most important missions that help shape the future of our society. Systems Biomedicine is one of the areas that shall benefit largely from acceleration of scientific discovery.

RECSYS 2020

Our work with Matching agent presented at Recsys 2020!
RECSYS 2020 Program Details

Paper “Building a reciprocal recommendation system at scale from scratch: Learnings from one of Japan’s prominent dating applications
Presentation Video by R. Ramanathan P9A: Real-World Applications III (Sept. 24, 2020)

COVID-19 Disease Map

Dr. Kitano together with group of scientists announced the COVID-19 Disease Map in the article entitled as “COVID-19 Disease Map, building a computational repository of SARS-CoV-2 virus-host interaction mechanisms” in Scientific Data.

https://www.nature.com/articles/s41597-020-0477-8