Springer Nature advances discovery by publishing insights on how AI impacts drug discovery
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Springer Nature advances discovery by publishing trusted research, supporting the development of new ideas and championing open science. We are committed to playing our part in accelerating solutions to address the world’s urgent challenges. We are a global publisher dedicated to providing the best possible service to the whole research community. We help authors to share their discoveries and enable researchers to find, access and understand the work of others.
Springer Nature publishes peer-reviewed papers that share insights on utilizing AI tools to deliver solutions for healthier lives. The scope and applications are far-reaching. Here we provide a small selection of papers of many that investigate how AI offers new opportunities to the advancement of healthcare and in particular, how AI impacts drug discovery.
Jayatunga et al. review AI in small molecule drug discovery and Nagra et al. outline how AI-based approaches are being applied in large-molecule drug discovery, analyse the landscape of companies developing these approaches and their pipelines, and provide a perspective on what is required for the biopharma industry to implement these approaches successfully.
Advances with deep learning, the growth of databases of molecules for virtual screening and improvements in computational power have supported the emergence of a new field of quantitative structure-activity relationship (QSAR) modelling application that Tropsha et al. term ‘deep QSAR’. This review discusses key advances in the field, including deep generative and reinforcement learning approaches in molecular design, deep learning models for synthetic planning, and the use of deep QSAR models in structure-based virtual screening.
Several AI technologies have also been developed to accelerate the discovery of natural products. Advances in computational omics technologies are enabling access to the hidden diversity of natural products, and AI approaches are facilitating key steps in harnessing the therapeutic potential of such compounds, including biological activity prediction. Mullowney et al. discuss the synergies between these fields to effectively identify drug candidates from the plethora of molecules produced by nature, and how to address the challenges in realizing the potential of these synergies.
And a recent news article offers insight to the debate with AI in drug discovery booming–but who owns the patents?
Thanks for taking the time to read the highlighted Nature content. Our Springer imprint also has content relevant to the impact of AI and machine-learning on medicine. If you’d like to explore the additional article content, please contact Demetrai Tate at [email protected].
Submitted by Springer Nature