Computers Outperform Lab Rats in Detecting Toxic Chemicals: UL and John Hopkins University Discovery Released at AAAS
UL, the science safety company, and Johns Hopkins University have embarked on joint research that has resulted in findings that Artificial Intelligence (AI) is superior in finding toxic substances to traditional animal testing.
Beyond being more effective, UL’s Cheminformatics REACHAcross™ software computer processing can be performed in a matter of seconds and at a fraction of the cost to traditional testing methods.
Working with researchers at the Bloomberg School of Public Health, UL has developed an innovative suite of tools including REACHAcross™ software to predict chemical toxicity that can be used wherever such data is needed.
AI development, like predictive cheminformatics, has been slowly progressing over the last two decades. It was in 1996 that the supercomputer Deep Blue beat chess world champion Gary Kasparov for the first time by using the data from over 700,000 games to learn more than 200 million possible moves. In the infancy of AI, it was estimated that Deep Blue cost more than $12 million.
Today, similar computing power is accessible with far more affordable systems, allowing the use of AI for real-world problems, like predicting toxicity.
There are more than 100,000 chemicals in medications and consumer products; for the vast majority; there is very little information about their toxicity.
Dr. Thomas Hartung, Chair for Evidence-based Toxicology at Johns Hopkins University, wants “to help end toxicological ignorance”, asserting that “we will not succeed if we continue using antiquated methods like animal testing, which has been in use since the beginning of biomedical research.” To that end, his group at Johns Hopkins University, led by Tom Luechtefeld, created a database — not of chess moves – but, of toxicity studies.
In 2016, when revealed at The American Association for the Advancement of Science (AAAS), the groundbreaking breadth of the REACHAcross™ software database (with 800,000 toxicological studies for over 10,000 chemicals) created the basis for using Big Data in safety assessments.
2018-02-21
컴퓨터가 실험 쥐보다 독성 검사 수행이 우월함(AAAS 발췌-영문)
AI가 독성화학물질을 검사하는 데 있어서 전통적인 동물실험보다 훨씬 우수하다는 내용으로
UL(과학안전회사)와 존스홉킨스대학이 AAAS(미국과학진흥협회)에서 발표한 연구 결과에 대한 기사를 공유합니다.