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Latest advances and perspectives of all AI types used in pharmaceutical R&D

Latest advances and perspectives of all AI types used in pharmaceutical R&D

The advances in Artificial intelligence (AI) have successfully propagated into the many areas such as computer vision, speech recognition, and natural language processing. AI is now rapidly propagating into the areas requiring substantial domain expertise such as biology, chemistry promising to speed up, improving the success rates, and lower the cost of drug discovery and drug development.

The laboratories of Jianfeng Pei at Peking University and Alex Zhavoronkov at Insilico Medicine partner with Frontiers in Pharmacology, a leading open science platform on the research topic “AI for drug discovery and development”. This Research Topic will highlight the most recent advances and perspectives of all kinds of artificial intelligence technologies used to accelerate and improve pharmaceutical R&D.

“Artificial intelligence is rapidly propagating into life sciences resulting in a wave of academic publications in the field of AI-powered drug discovery, a plethora of startups developing new strategies and pursuing innovative business models to transform pharmaceutical research and development. The pharmaceutical industry is also strengthening its internal capabilities in this area by centralizing the previously segregated data sources, hiring data scientists and investing in infrastructure,” said Jianfeng Pei, Ph.D., Peking University, Beijing.

“The integration of machine learning with ever-more extensive biological data-sets promises to accelerate advancement in the field of human health in an unprecedented manner, not least to assist in tackling the ‘failure to fail’ in current drug development pipelines, previously highlighted by our Translational Pharmacology section Chief Editor Prof Alastair Stewart. We in Frontiers in Pharmacology are excited to partner with Dr. Zhavoronkov and Dr. Pei, to develop a broad-scope, open-access article collection aiming to reflect and catalyze leading-edge developments in this field globally,” said Brian Boyle, Journal Development Manager, Frontiers in Pharmacology Journal.

The applications of AI in drug discovery are very broad and can be classified into several areas:

The advent of artificial intelligence in the pharmaceutical industry is expected to enable the countries that did not previously engage in early-stage drug discovery and innovative medicine to leapfrog years of pharma R&D and contribute to the global push for better health. With 1.4 billion people and the government push for innovative medicines, China is expected to become the major force in the pharmaceutical industry. The authors hope that trade wars do not impact this important field. Cancer, Alzheimer’s and other diseases do not discriminate by the nation. Until there is a clear set of cures, a trade war in biotechnology R&D is equivalent to a war on all people. Advances in biomedicine require massive international collaborations, diversity, and data sharing initiatives.

“We are very happy to partner with the leading scientist in China and globally, Dr. Jianfeng Pei of the Peking University on this research topic and welcome scientists from all over the world to collaborate and contribute to it”, said Alex Zhavoronkov, Ph.D., Founder, and CEO of Insilico Medicine, Inc.

The Research Topic covers new AI algorithms and (or) applications in a wide range of areas such as drug target identification, systems biology, pharmacogenomics, network pharmacology, chemical property prediction, synthesis planning, molecular design and generation, protein-ligand interaction, drug-target interaction network, drug-related knowledge graphs, big data analysis for drug information, and image recognition. This Research Topic will highlight the most recent advances and perspectives on all kinds of artificial intelligence technologies used in drug design.

In this Research Topic, the authors welcome Perspective and Policy papers proposing the paths for the development of innovative medicines using AI and accelerating pharma R&D in the developed and developing countries.

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