Collection Overview
Scientific Reports has launched a Guest-Edited Collection on Generative modeling for chemistry discovery.
Generative modeling for chemistry discovery is an emerging field at the intersection of artificial intelligence and molecular science, focused on the use of machine learning architectures to design, predict, and optimize chemical structures and reactions. By learning from vast chemical datasets and exploring chemical space beyond human intuition, generative models can propose novel molecules and materials with targeted properties. The significance of this approach lies in accelerating discovery pipelines, reducing experimental trial-and-error, and unlocking previously inaccessible areas of chemical design, thereby transforming research in pharmaceuticals, energy materials, catalysis, and beyond. Recent progress has been driven by advances in deep generative architectures—including variational autoencoders, generative adversarial networks, reinforcement learning frameworks, and diffusion models—which have been adapted to molecular graphs, reaction templates, and polymer representations.
This Collection supports and amplifies research related to SDG 9 - Industry, Innovation & Infrastructure.
This will be a Collection of original research papers and will be open for submissions from all authors – on the condition that the manuscripts fall within the scope of the Collection and of Scientific Reports more generally. We are welcoming submissions until 21st October 2026.
Why is this Collection important?
"Generative modeling for chemical discovery occupies a strategic position at the interface of artificial intelligence and molecular science, where algorithmic creativity meets physical constraint. Its relevance stems from the possibility of navigating chemical space at scales and speeds unattainable by conventional experimentation, thereby reshaping how molecules and materials are conceived. This Collection is exciting because it captures a moment when deep generative models are moving from theoretical promise toward experimentally grounded practice. Its impact may lie in establishing methodological standards and revealing transferable design principles. Researchers should submit here to engage a focused community addressing both innovation and validation in chemical generation."
- Dr. Chandrakanta Mahanty, Guest Editor
Why submit to a collection?
Collections like this one help promote high-quality science. They are led by Guest Editors, who are experts in their fields, and In-House Editors and are supported by a dedicated team of Commissioning Editors and Managing Editors at Springer Nature. Collection manuscripts typically see higher citations, downloads, and Altmetric scores and provide a one-stop-shop on a cutting-edge topic of interest.
Who is involved?
Guest Editors:
- Roger H. French, Case Western Reserve University, United States
- James Guevara-Pulido, Universidad El Bosque, Colombia
- Chandrakanta Mahanty, GITAM University, India
- Quynh D. Tran, Case Western Reserve University, United States
Internal Team:
- In-House Editor: Chenyu Wang, Scientific Reports, USA
- Commissioning Editor: Quintina Dawson, Fully OA Brands, Springer Nature, UK
- Managing Editor: Eleanor Smith, Fully OA Brands, Springer Nature, UK
How can I submit my paper?
Visit the Collection page for more information on the Collection, and how to submit your article.