Friday, November 30 - Insilico Medicine, an artificial intelligence company developing an end-to-end drug discovery pipeline for age-related diseases, announced an open research collaboration. Researchers are invited to contribute to the new platform MOSES (Molecular Sets), described in the paper titled "Molecular Sets (MOSES): A Benchmarking Platform for Molecular Generation Models". The code and the paper are available at the GitHub repository https:/
The original benchmarking platform is a result of collaboration between Insilico Medicine, Neuromation, and Alán Aspuru-Guzik's laboratory. The researchers and teams are invited to contribute their datasets and models to extend the benchmarking platform.
The paper introduces Molecular Sets (MOSES) -- a benchmarking platform that encompasses various machine learning techniques, in order to compare them on a standard dataset. MOSES implements several popular molecular generation models and ranks them, according to a predefined set of metrics. MOSES aims to increase the pace of drug discovery and facilitate sharing and comparison of new models. MOSES is supposed to boost AI-powered drug discovery, just as ImageNet boosted deep learning for imaging data.
The ongoing research in machine learning, in particular, deep learning, brings up the issues of reproducibility and fair comparison of different approaches. While there are multiple methods for generating novel molecular structures with machine learning models, there is no conventional way to run and evaluate the performance of these generative models. The MOSES platform provides a standardized benchmarking dataset, a set of open-sourced models with unified implementation, and metrics to evaluate and assess the results of generation.
"When we started the journey in generative chemistry using GANs in 2015, it was rather exotic and unproven technology. Today it is experimentally-validated and the field is exploding with many groups joining and making meaningful contributions. We believe that it is important to develop a set of standards and benchmarks to help the community, to accelerate the delivery of AI-generated drugs to the patients", said Alex Zhavoronkov, Ph.D., founder, and CEO of Insilico Medicine.
"At Insilico Medicine, we take reproducibility and fair evaluation of machine learning models very seriously. Growing popularity of generative AI applications in drug discovery dictates a need for a standardized benchmarking platform supported and maintained by the research community. With MOSES, we come one step closer to the ultimate goal of disrupting the industry with better drug compounds produced by advanced computational and machine learning methods", said Alexander Zhebrak, CTO of Insilico Medicine.
"I am happy to announce this research collaboration on behalf of Neuromation. Here at Neuromation, we are trying to bring cutting edge results from deep learning to many fields, with healthcare being among our top priorities. MOSES is a benchmarking platform that has the potential to become an industry standard for generative models in biochemistry; developing it has been an exciting road for us and a wonderful opportunity to work together with Insilico Medicine," said Sergey Nikolenko, Ph.D., Chief Research Officer of Neuromation.
The platform, described in the paper, will be presented at the NIPS Expo in Montreal on December 2, by Daniil Polykovskiy, Alexander Zhebrak, and Alex Zhavoronkov, co-authors of the paper.
Insilico Medicine is regularly publishing research papers in peer-reviewed journals. The company was first to apply the generative adversarial networks (GANs) to the generation of the new molecular structures with the specified parameters and published a seminal peer-reviewed paper submitted in June 2016. The concept was further extended and augmented with advanced memory and reinforcement learning. One of the latest papers published in the Journals of Gerontology demonstrated the application of the deep neural networks to assessing the biological age of the patients. The latest special issue in Molecular Pharmaceutics featured several research papers by Insilico Medicine. Insilico published an overview of its results in aging research including the development of AI aging biomarkers, target identification, cross-species comparison and geroprotector discovery in Aging Research Reviews, one of the highest-impact journals in the field.
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About Insilico Medicine, Inc
Insilico Medicine is an artificial intelligence company headquartered in Rockville, with R&D and management resources in Belgium, Russia, UK, Taiwan, and Korea sourced through hackathons and competitions. The company and its scientists are dedicated to extending human productive longevity and transforming every step of the drug discovery and drug development process through excellence in biomarker discovery, drug development, digital medicine, and aging research. Insilico pioneered the applications of the generative adversarial networks (GANs) and reinforcement learning for generation of novel molecular structures for the diseases with a known target and with no known targets. In addition to working collaborations with the large pharmaceutical companies, the company is pursuing internal drug discovery programs in cancer, dermatological diseases, fibrosis, Parkinson's Disease, Alzheimer's Disease, ALS, diabetes, sarcopenia, and aging. Through a partnership with LifeExtension.com, the company launched a range of nutraceutical products compounded using the advanced bioinformatics techniques and deep learning approaches. It also provides a range of consumer-facing applications including Young.AI. Insilico Medicine is one of the founding companies of the Alliance of Artificial Intelligence in Healthcare (AAIH).
In 2017, NVIDIA selected Insilico Medicine as one of the Top 5 AI companies in its potential for social impact. In 2018, the company was named one of the global top 100 AI companies by CB Insights. In 2018 it received the Frost & Sullivan 2018 North American Artificial Intelligence for Aging Research and Drug Development Award accompanied with the industry brief. Brief company video: https:/