Professor Pavlo O. Dral joins the Editorial Board of Artificial Intelligence Chemistry

Published Time:  2023-01-12 10:18:36

Artificial Intelligence Chemistry, a new chemical journal launched by Elsevier, with international peer review and online open access, is now officially open to submissions. MLatom founder and XACS co-founder Professor Pavlo O. Dral joins the Editorial Board of this new journal.

We are looking forward to your submission. No article publishing fee (APC) before July 31, 2023!

The journal broadly covers all areas of chemistry where artificial intelligence (AI) and machine learning approaches are used. While studies in more traditional sub-disciplines of chemistry will certainly be considered, we would welcome and encourage submissions of work that could potentially stimulate interdisciplinary interests involving AI. All submissions must provide a significant contribution to our understanding of the contemporary and future AI theory, AI methods, and/or AI applications in chemistry, medicine, biology, and materials science. Studies conducted at all spatial and temporal scales will be considered. The forms of articles to be accepted include reviews, research articles, correspondence letters, news items, etc.

We will be open to submissions on all relevant topics and but particularly interested in the following areas of research:

  • Quantum chemical methods combining artificial intelligence (AI) and big data techniques;

  • AI oriented calculations and data system;

  • Construction of AI models;

  • AI assisted materials design;

  • AI assisted structure-activity relationship;

  • Machine learning protocols and application in chemistry;

  • Multi-scale modelling method development;

  • Intelligent spectrum analysis based on AI;

  • Application of AI in complex chemical systems;

  • Application of AI in organic chemistry;

  • Application of AI in drug discovery;

  • Application of AI in identification of sprout compounds and targets;

  • Development of AI software and hardware;

  • Research on the integrating experimental and theory;

  • AI-based spectroscopic analysis and monitoring.

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