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  6. Toxicity prediction for drug discovery and in silico analysis [Materials provided]

Toxicity prediction for drug discovery and in silico analysis [Materials provided]

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last updated:Mar 31, 2026

シュレーディンガー
シュレーディンガー

Avoiding hERG and CYP risks at the design stage with AI×in silico toxicity prediction.

Are you struggling with failures or additional tests in the late stages of development due to toxicity risks such as hERG, CYP, and PXR? This solution enables early identification of toxicity risks through in silico toxicity predictions that combine AI and physics-based simulations, allowing for avoidance during the design phase. Rather than just a Pass/Fail determination, it analyzes toxicity factors at the atomic level and supports structural optimization while maintaining activity. For known risks, binding hypotheses are provided within a few days, and predictions for binding to anti-targets for unknown risks are delivered within 24 hours. Quickly integrate into the DMTA cycle to improve development efficiency and reduce costs. It transforms toxicity assessment in drug discovery from a "post-process" to a "design process." First, please download the materials to review the specific analytical methods.

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26_113_Predictive

Toxicity prediction for drug discovery and in silico analysis [Materials provided]

26_113_Predictive Toxicology Eval_Flyer_Japanese_ページ_1.jpg
26_113_Predictive Toxicology Eval_Flyer_Japanese_ページ_1.jpg

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Schrödinger Co., Ltd. is the Japanese subsidiary of Schrödinger Inc., headquartered in New York, USA. Schrödinger has a history of about 30 years in developing software that integrates advanced technologies in chemistry and computer science, primarily in the fields of materials science and life sciences, providing advanced solutions for drug discovery, biologics, and materials research and development.

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