Machine Learning and Artificial Intelligence for Molecular Design, Predictive Modelling and Digital Twins
A Special Issue of Molecules (ISSN 1420-3049) belonging to the section "Computational and Theoretical Chemistry".
Deadline for manuscript submissions: 31 March 2027 | Viewed by 3
Editors
Interests: molecular modelling and simulations; first principle calculations; machine learning tools
Special Issues, Collections and Topics in MDPI journals
Special Issue Information
Dear Colleagues,
Artificial Intelligence (AI) and Machine Learning (ML) are reshaping the field of molecular, chemical, pharmaceutical, and materials systems. These approaches are enabling new forms of data-driven discovery, ranging from predictive modelling to inverse design, automated optimisation and the development of digital twins for complex experimental and industrial processes. However, interpretation and robustness are still difficult issues in AI. As ML is increasingly used to inform decisions, testing the uncertainties that come with them becomes a necessity.
Being mindful of the current sustainability paradigm where standard calculations are limited by time and financial costs, AI/ML can assist in fast exploration of large chemical spaces, developing data-driven models for complex systems. However, most applications encounter limitations due to inadequate or heterogeneous datasets, requiring methods such as transfer learning, active learning, and data augmentation.
The present Special Issue, entitled “Machine Learning and Artificial Intelligence for Molecular Design, Predictive Modelling and Digital Twins”, aims to bring together original research and review articles that advance the use of ML and AI in molecular, pharmaceutical, chemical, and materials sciences. We encourage bold contributions by addressing current AI/ML practical challenges, including inverse design, data scarcity, uncertainty quantification, and cross-domain generalisation. In particular, we welcome contributions that integrate methodological advances with relevant applications, while addressing critical challenges to model explainability.
Dr. M. Natália D. S. Cordeiro
Guest Editor
Dr. José Ferraz-Caetano
Guest Editor Assistant
Manuscript Submission Information
Manuscripts should be submitted online at www.mdpi.com by registering and logging in to this website. Once you are registered, click here to go to the submission form. Manuscripts can be submitted until the deadline. All submissions that pass pre-check are peer-reviewed. Accepted papers will be published continuously in the journal (as soon as accepted) and will be listed together on the special issue website. Research articles, review articles as well as short communications are invited. For planned papers, a title and short abstract (about 250 words) can be sent to the Editorial Office for assessment.
Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-anonymized peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Molecules is an international peer-reviewed open access semimonthly journal published by MDPI.
Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 2700 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.
Keywords
- machine learning
- artificial intelligence
- predictive modelling
- molecular design
- automated optimisation
Benefits of Publishing in a Special Issue
- Ease of navigation: Grouping papers by topic helps scholars navigate broad scope journals more efficiently.
- Greater discoverability: Special Issues support the reach and impact of scientific research. Articles in Special Issues are more discoverable and cited more frequently.
- Expansion of research network: Special Issues facilitate connections among authors, fostering scientific collaborations.
- External promotion: Articles in Special Issues are often promoted through the journal's social media, increasing their visibility.
- Reprint: MDPI Books provides the opportunity to republish successful Special Issues in book format, both online and in print.
Further information on MDPI's Special Issue policies can be found here.
