Data and Low-Data Tools for Artificial Intelligence in Medicinal Chemistry
Deadline for manuscript submissions: closed (15 August 2022) | Viewed by 14260
Interests: prediction of metabolic reactions and toxicity mechanisms; prediction of the bioactivity profile of new drug candidates; structure-based researches applied to drug design; development of new strategies to improve the performance of predictive algorithms
Interests: drug discovery; virtual screening; QSAR; machine learning; molecular descriptors; de novo design; generative deep learning
In the last few years, the scientific community has witnessed the renaissance of so-called “artificial intelligence” (AI) methods in many scientific domains. Machine and deep learning methods have the potential to transform people’s life in multiple aspects and sectors (healthcare, education, marketing, etc.), possibly translating into a general benefit for society.
This unparalleled emergence of AI can also be observed in medicinal chemistry and toxicology, where machine learning is starting to be routinely applied for several tasks, such as property prediction, retrosynthesis planning, and molecule generation. In medicinal chemistry, unlike in other fields (e.g., image recognition, language translation), however, the potential of “data hungry” machine and deep learning algorithms is often limited by the lack of data, both in terms of numerosity and quality. For this reason, the scientific community is in need of high-quality datasets, open access curation pipelines, and AI tools tailored for low-data regimes.
In this Special Issue, we welcome original research articles and reviews aimed to improve the current status of data and their usage for AI in medicinal chemistry and related fields. The Special Issue will include, but is not limited to, ligand- and structure-based approaches, molecular design, virtual screening, target identification, drug repurposing as well as bioactivity, safety, and ADMET property prediction. We particularly welcome papers focused on the creation and curation of novel datasets or describing the curation and/or usefulness of well-established databases. We also encourage the submission of papers addressing the development/application of AI approaches in low-data regimes. Papers providing accessible code and data are also particularly welcome.
To further improve the impact of the proposed Special Issue, upon acceptance and agreement with the authors, the datasets will be collected in a dedicated repository on Zenodo (Zenodo.org) and assigned a DOI identifier.
We look forward to receiving your submissions!
Dr. Angelica Mazzolari
Dr. Francesca Grisoni
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- artificial intelligence
- machine learning
- medicinal chemistry
- small dataset
- high-quality dataset
- data curation
- drug repurposing
- virtual screening