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AI
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AI in Drug Design
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AI in Drug Design

A special issue of AI (ISSN 2673-2688). This special issue belongs to the section "Chemical Artificial Intelligence".

Deadline for manuscript submissions: closed (28 February 2021) | Viewed by 5047

Special Issue Editors

Prof. Dr. George Mihai Nitulescu

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Guest Editor
Faculty of Pharmacy, “Carol Davila” University of Medicine and Pharmacy, 6 Traian Vuia Street, 020956 Bucharest, Romania
Interests: the design and synthesis of new anticancer agents; the design and synthesis of new antimicrobial compounds; studies and structural analysis; the isolation and analysis of natural compounds with anticancer effects; computer-assisted drug design studies
Special Issues, Collections and Topics in MDPI journals
Prof. Dr. Pier Luigi Gentili

E-Mail Website
Guest Editor
Department of Chemistry, Biology, and Biotechnology, University of Perugia, 06123 Perugia, Italy
Interests: complexity; artificial intelligence; fuzzy logic; photophysics; photochemistry; oscillatory reactions; complex systems; nonlinear dynamics; chaos
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

Complex methods are employed to identify new chemical compounds that may be developed to be marketed as drugs. Despite all the technologic progress, the process is very long, with an estimated average of 9 to 12 years, and the success rate is low, considerably increasing the cost. A large variety of computer-aided methods and tools were developed to improve the drug development process. There has also been a remarkable advancement in computational power coupled with big data analysis that enables artificial intelligence to revolutionize the drug discovery and development process. The top pharmaceutical companies have already implemented artificial intelligence methods to improve their development success rates. The research paradigm is clearly changing, presenting great challenges and opportunities for scientists.

This Special Issue will focus on research studies highlighting the development of artificial intelligence focused on preclinical or clinical drug development. This issue aims to bring together multi- and interdisciplinary approaches in computer-aided drug development.

Dr. George Mihai Nitulescu
Dr. Pier Luigi Gentili
Guest Editors

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 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. AI is an international peer-reviewed open access quarterly 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 1600 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

  • molecular modeling
  • machine learning
  • big data analysis
  • CADD

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Published Papers (1 paper)

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Research

10 pages, 1286 KiB  
Article
Artificial Intelligence Algorithms for Discovering New Active Compounds Targeting TRPA1 Pain Receptors
by Dragos Paul Mihai, Cosmin Trif, Gheorghe Stancov, Denise Radulescu and George Mihai Nitulescu
AI 2020, 1(2), 276-285; https://doi.org/10.3390/ai1020018 - 11 Jun 2020
Cited by 6 | Viewed by 3960
Abstract
Transient receptor potential ankyrin 1 (TRPA1) is a ligand-gated calcium channel activated by cold temperatures and by a plethora of electrophilic environmental irritants (allicin, acrolein, mustard-oil) and endogenously oxidized lipids (15-deoxy-∆12, 14-prostaglandin J2 and 5, 6-eposyeicosatrienoic acid). These oxidized lipids work as agonists, [...] Read more.
Transient receptor potential ankyrin 1 (TRPA1) is a ligand-gated calcium channel activated by cold temperatures and by a plethora of electrophilic environmental irritants (allicin, acrolein, mustard-oil) and endogenously oxidized lipids (15-deoxy-∆12, 14-prostaglandin J2 and 5, 6-eposyeicosatrienoic acid). These oxidized lipids work as agonists, making TRPA1 a key player in inflammatory and neuropathic pain. TRPA1 antagonists acting as non-central pain blockers are a promising choice for future treatment of pain-related conditions having advantages over current therapeutic choices A large variety of in silico methods have been used in drug design to speed up the development of new active compounds such as molecular docking, quantitative structure-activity relationship models (QSAR), and machine learning classification algorithms. Artificial intelligence methods can significantly improve the drug discovery process and it is an attractive field that can bring together computer scientists and experts in drug development. In our paper, we aimed to develop three machine learning algorithms frequently used in drug discovery research: feedforward neural networks (FFNN), random forests (RF), and support vector machines (SVM), for discovering novel TRPA1 antagonists. All three machine learning methods used the same class of independent variables (multilevel neighborhoods of atoms descriptors) as prediction of activity spectra for substances (PASS) software. The model with the highest accuracy and most optimal performance metrics was the random forest algorithm, showing 99% accuracy and 0.9936 ROC AUC. Thus, our study emphasized that simpler and robust machine learning algorithms such as random forests perform better in correctly classifying TRPA1 antagonists since the dimension of the dependent variables dataset is relatively modest. Full article
(This article belongs to the Special Issue AI in Drug Design)
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