The importance of Artificial Intelligence and machine learning is becoming ever more obvious in the field of drug discovery. This is evident though the increase in quality submissions of articles related to this topic. To respond to this increased demand and interest, we at Elsevier announce the launch of a new journal, “Artificial Intelligence in the Life Sciences” for rapid, open access publication with a streamlined editorial review process to enhance author satisfaction. The journal is intended to provide a scientific forum for practical applications and theoretical advances of Artificial Intelligence in the life sciences and related disciplines which include (but are not restricted to) general, molecular and systems biology; population and disease genetics; medicinal chemistry and chemical biology; pharmacology and drug discovery and epidemiology and clinical investigations. Come and join the launch event on May 5th 2021 at 3pm UK time - meet the Editor-in-Chief Juergen Bajorath, hear about the aims and scopes of papers for the journal and ask whatever questions you may have. Sign up for the event please email Parveen Dhillon.
To whet your appetite, this month’s Editor’s Choice features some excellent examples of how Artificial intelligence can be applied to the drug discovery industry. First features an article from Linlin Zhao, Heather L. Ciallella, Lauren M. Aleksunes and Hao Zhu of Rutgers University, Camden, New Jersey, USA, entitled: “Advancing computer-aided drug discovery (CADD) by big data and data-driven machine learning modeling” which outlines recent progress of machine learning to deep learning and the development of new algorithms to address big data challenges.
Next is an article from Andreas Bender and Isidro Cortés-Ciriano of Cambridge, UK entitled: “Artificial intelligence in drug discovery: what is realistic, what are illusions? Part 1: Ways to make an impact, and why we are not there yet”. The article highlights that although AI has transformed many fields, it has not achieved this as yet in drug discovery and outlines a road map to move from what “can be done” to what “should be done” to produce safe, efficacious drugs at a low price.
Finally, is the article “Artificial intelligence in drug discovery and development” from Debleena Paul, Gaura Sanap, Snehal Shenoy, Dnyaneshwar Kalyane, Kiran Kalia and Rakesh K. Tekade, which expands upon the themes from the other two articles and explains how AI can extend the life cycle of products, and support pharma endeavours as disparate as drug discovery and product management. The article further expands upon those challenges that still face the pharma industry still faces and how AI may help to address them.
Steve Carney was born in Liverpool, England and studied Biochemistry at Liverpool University, obtaining a BSc.(Hons) and then read for a PhD on the Biochemistry and Pathology of Connective Tissue Diseases in Manchester University, in the Departments of Medical Biochemistry and Histopathology. On completion of his PhD he moved to the Kennedy Institute of Rheumatology, London, where he worked with Professor Helen Muir FRS and Professor Tim Hardingham, on the biochemistry of experimental Osteoarthritis. He joined Eli Lilly and Co. and held a number of positions in Biology R&D, initially in the Connective Tissue Department, but latterly in the Neuroscience Department. He left Lilly to take up his present position as Managing Editor, Drug Discovery Today, at Elsevier. Currently, he also holds an honorary lectureship in Drug Discovery at the University of Surrey, UK. He has authored over 50 articles in peer-reviewed journals, written several book chapters and has held a number of patents.