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Artificial Intelligence Chemistry is a peer-reviewed journal published by Elsevier, covering Machine Learning in Materials Science, Computational Drug Discovery Methods, Advanced Chemical Physics Studies.
| Publisher | Elsevier |
|---|---|
| Reference style | Numbered (Elsevier) Numbered — [1], [2] in the text [1] A. Smith, B. Jones, C. Lee, A representative article title, Artificial Intelligence Chemistry 12 (2023) 45–58.
Formats any DOI in Artificial Intelligence Chemistry style. No sign-up. |
| Publishes research in | Machine Learning in Materials Science Computational Drug Discovery Methods Advanced Chemical Physics Studies Metabolomics and Mass Spectrometry Studies Protein Structure and Dynamics |
| ISSN | 2949-7477 |
| Citation impact (2-yr) | 8.59 |
| h-index | 18 |
| i10-index | 32 |
| Total citations | 1,277 |
| Open access | Yes |
| Top institutions publishing here | Chinese Academy of Sciences |
| You get | A submission-ready PDF and the editable LaTeX source — ready to submit. |
Citations each year’s papers have accumulated so far — the most recent years are still building up.
Adverse drug reactions (ADRs) and drug-induced toxicity are major challenges in drug discovery, threatening patient safety and dramatically increasing healthcare expenditures. Since ADRs and toxicity are not as visible as infectious diseases, the potential consequences are considerable. Early detection of ADRs and drug-induced toxicity is an essential indicator of a drug's viability and safety profile.…
Artificial intelligence (AI) is revolutionizing the current process of drug design and development, addressing the challenges encountered in its various stages. By utilizing AI, the efficiency of the process is significantly improved through enhanced precision, reduced time and cost, high-performance algorithms and AI-enabled computer-aided drug design (CADD). Effective drug screening techniques are crucial for identifying…
Artificial intelligence (AI) is driving a revolution in chemistry, reshaping the landscape of molecular design. This review explores AI’s pivotal roles in the field of organic synthesis applications. AI accurately predicts reaction outcomes, controls chemical selectivity, simplifies synthesis planning, accelerates catalyst discovery, and fuels material innovation and so on. It seamlessly integrates data-driven algorithms with…
Artificial intelligence has brought crucial changes to the whole field of natural sciences. Myriads of machine learning algorithms have been developed to facilitate the work of experimental scientists. Molecular property prediction and drug synthesis planning become routine tasks. Moreover, inverse design of compounds with tunable properties as well as on-the-fly autonomous process optimization and chemical…
Large Language Models (LLMs), such as GPT-4, are precipitating a new "industrial revolution" by significantly enhancing productivity across various domains. These models encode an extensive corpus of scientific knowledge from vast textual datasets, functioning as near-universal generalists with the ability to engage in natural language communication and exhibit advanced reasoning capabilities. Notably, agents derived from…
