Special Issue "Artificial Intelligence Applied to Medicinal Chemistry and Structural Biology"

A special issue of Pharmaceuticals (ISSN 1424-8247). This special issue belongs to the section "Medicinal Chemistry".

Deadline for manuscript submissions: 31 December 2022.

Special Issue Editor

Prof. Dr. Osvaldo Andrade Santos-Filho
E-Mail Website
Guest Editor
Center of Health Sciences, Laboratory of Molecular Modeling and Computational Structural Biology, Federal University of Rio de Janeiro, IPPN, Av. Carlos Chagas Filho 373, Bloco H, Rio de Janeiro RJ-21941-599, Brazil
Interests: molecular modeling; computational and medicinal chemistry; molecular simulations; structural biology
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

The usage of artificial intelligence (AI) has been increasing in several sectors of society. Particularly in drug design endeavors, it has been successfully applied in several areas, including clinical trials, medicinal chemistry, drug repurposing, and marketing and sales analytics, among others. Moreover, in structural biology research, AI has been used in fields such as molecular simulations, protein engineering, and quantum enzymology. It is well known that in medicinal chemistry and structural biology, an important task is the analysis and processing of the multivariable chemical and biological spaces that are organized in huge databases. Those are time-consuming tasks that require a lot of economic resources and, since AI technology can process a vast amount of data, it is becoming an essential tool for accelerating research and development steps and reducing costs, with a good degree of accuracy. To celebrate the success story and the advances on the applications of AI in Medicinal Chemistry and Structural Biology, I invite fellow scientists to submit original papers or reviews, which will be published as a Special Issue on “Artificial Intelligence Applied to Medicinal Chemistry and Structural Biology”.

We look forward to your contribution.

Prof. Dr. Osvaldo Santos-Filho
Guest Editor

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 papers will be 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 100 words) can be sent to the Editorial Office for announcement on this website.

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-blind peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Pharmaceuticals is an international peer-reviewed open access monthly 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 2000 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

  • artificial intelligence
  • machine learning
  • deep learning
  • artificial neural network
  • decision tree
  • instance-based algorithm
  • medicinal chemistry
  • structural biology
  • molecular simulation

Published Papers (1 paper)

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Research

Article
Predicting Anticancer Drug Resistance Mediated by Mutations
Pharmaceuticals 2022, 15(2), 136; https://0-doi-org.brum.beds.ac.uk/10.3390/ph15020136 (registering DOI) - 24 Jan 2022
Abstract
Cancer drug resistance presents a challenge for precision medicine. Drug-resistant mutations are always emerging. In this study, we explored the relationship between drug-resistant mutations and drug resistance from the perspective of protein structure. By combining data from previously identified drug-resistant mutations and information [...] Read more.
Cancer drug resistance presents a challenge for precision medicine. Drug-resistant mutations are always emerging. In this study, we explored the relationship between drug-resistant mutations and drug resistance from the perspective of protein structure. By combining data from previously identified drug-resistant mutations and information of protein structure and function, we used machine learning-based methods to build models to predict cancer drug resistance mutations. The performance of our combined model achieved an accuracy of 86%, a Matthews correlation coefficient score of 0.57, and an F1 score of 0.66. We have constructed a fast, reliable method that predicts and investigates cancer drug resistance in a protein structure. Nonetheless, more information is needed concerning drug resistance and, in particular, clarification is needed about the relationships between the drug and the drug resistance mutations in proteins. Highly accurate predictions regarding drug resistance mutations can be helpful for developing new strategies with personalized cancer treatments. Our novel concept, which combines protein structure information, has the potential to elucidate physiological mechanisms of cancer drug resistance. Full article
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