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Pharmaceuticals
Special Issues
Artificial Intelligence Applied to Medicinal Chemistry and Structural Biology
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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: closed (1 December 2023) | Viewed by 26761

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 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 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. 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 2900 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

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Published Papers (5 papers)

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Research

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22 pages, 4387 KiB  
Article
Using Artificial Intelligence for Drug Discovery: A Bibliometric Study and Future Research Agenda
by Erik Karger and Marko Kureljusic
Pharmaceuticals 2022, 15(12), 1492; https://doi.org/10.3390/ph15121492 - 30 Nov 2022
Cited by 9 | Viewed by 4867
Abstract
Drug discovery is usually a rule-based process that is carefully carried out by pharmacists. However, a new trend is emerging in research and practice where artificial intelligence is being used for drug discovery to increase efficiency or to develop new drugs for previously [...] Read more.
Drug discovery is usually a rule-based process that is carefully carried out by pharmacists. However, a new trend is emerging in research and practice where artificial intelligence is being used for drug discovery to increase efficiency or to develop new drugs for previously untreatable diseases. Nevertheless, so far, no study takes a holistic view of AI-based drug discovery research. Given the importance and potential of AI for drug discovery, this lack of research is surprising. This study aimed to close this research gap by conducting a bibliometric analysis to identify all relevant studies and to analyze interrelationships among algorithms, institutions, countries, and funding sponsors. For this purpose, a sample of 3884 articles was examined bibliometrically, including studies from 1991 to 2022. We utilized various qualitative and quantitative methods, such as performance analysis, science mapping, and thematic analysis. Based on these findings, we furthermore developed a research agenda that aims to serve as a foundation for future researchers. Full article
(This article belongs to the Special Issue Artificial Intelligence Applied to Medicinal Chemistry and Structural Biology)
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10 pages, 2310 KiB  
Article
Peptide-Based Drug Predictions for Cancer Therapy Using Deep Learning
by Yih-Yun Sun, Tzu-Tang Lin, Wen-Chih Cheng, I-Hsuan Lu, Chung-Yen Lin and Shu-Hwa Chen
Pharmaceuticals 2022, 15(4), 422; https://doi.org/10.3390/ph15040422 - 30 Mar 2022
Cited by 9 | Viewed by 3515
Abstract
Anticancer peptides (ACPs) are selective and toxic to cancer cells as new anticancer drugs. Identifying new ACPs is time-consuming and expensive to evaluate all candidates’ anticancer abilities. To reduce the cost of ACP drug development, we collected the most updated ACP data to [...] Read more.
Anticancer peptides (ACPs) are selective and toxic to cancer cells as new anticancer drugs. Identifying new ACPs is time-consuming and expensive to evaluate all candidates’ anticancer abilities. To reduce the cost of ACP drug development, we collected the most updated ACP data to train a convolutional neural network (CNN) with a peptide sequence encoding method for initial in silico evaluation. Here we introduced PC6, a novel protein-encoding method, to convert a peptide sequence into a computational matrix, representing six physicochemical properties of each amino acid. By integrating data, encoding method, and deep learning model, we developed AI4ACP, a user-friendly web-based ACP distinguisher that can predict the anticancer property of query peptides and promote the discovery of peptides with anticancer activity. The experimental results demonstrate that AI4ACP in CNN, trained using the new ACP collection, outperforms the existing ACP predictors. The 5-fold cross-validation of AI4ACP with the new collection also showed that the model could perform at a stable level on high accuracy around 0.89 without overfitting. Using AI4ACP, users can easily accomplish an early-stage evaluation of unknown peptides and select potential candidates to test their anticancer activities quickly. Full article
(This article belongs to the Special Issue Artificial Intelligence Applied to Medicinal Chemistry and Structural Biology)
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18 pages, 3846 KiB  
Article
Predicting Anticancer Drug Resistance Mediated by Mutations
by Yu-Feng Lin, Jia-Jun Liu, Yu-Jen Chang, Chin-Sheng Yu, Wei Yi, Hsien-Yuan Lane and Chih-Hao Lu
Pharmaceuticals 2022, 15(2), 136; https://doi.org/10.3390/ph15020136 - 24 Jan 2022
Cited by 7 | Viewed by 3636
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
(This article belongs to the Special Issue Artificial Intelligence Applied to Medicinal Chemistry and Structural Biology)
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Review

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34 pages, 2622 KiB  
Review
Revolutionizing Medicinal Chemistry: The Application of Artificial Intelligence (AI) in Early Drug Discovery
by Ri Han, Hongryul Yoon, Gahee Kim, Hyundo Lee and Yoonji Lee
Pharmaceuticals 2023, 16(9), 1259; https://doi.org/10.3390/ph16091259 - 6 Sep 2023
Cited by 21 | Viewed by 9088
Abstract
Artificial intelligence (AI) has permeated various sectors, including the pharmaceutical industry and research, where it has been utilized to efficiently identify new chemical entities with desirable properties. The application of AI algorithms to drug discovery presents both remarkable opportunities and challenges. This review [...] Read more.
Artificial intelligence (AI) has permeated various sectors, including the pharmaceutical industry and research, where it has been utilized to efficiently identify new chemical entities with desirable properties. The application of AI algorithms to drug discovery presents both remarkable opportunities and challenges. This review article focuses on the transformative role of AI in medicinal chemistry. We delve into the applications of machine learning and deep learning techniques in drug screening and design, discussing their potential to expedite the early drug discovery process. In particular, we provide a comprehensive overview of the use of AI algorithms in predicting protein structures, drug–target interactions, and molecular properties such as drug toxicity. While AI has accelerated the drug discovery process, data quality issues and technological constraints remain challenges. Nonetheless, new relationships and methods have been unveiled, demonstrating AI’s expanding potential in predicting and understanding drug interactions and properties. For its full potential to be realized, interdisciplinary collaboration is essential. This review underscores AI’s growing influence on the future trajectory of medicinal chemistry and stresses the importance of ongoing synergies between computational and domain experts. Full article
(This article belongs to the Special Issue Artificial Intelligence Applied to Medicinal Chemistry and Structural Biology)
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Other

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12 pages, 1382 KiB  
Perspective
Artificial Intelligence, Machine Learning, and Big Data for Ebola Virus Drug Discovery
by Samuel K. Kwofie, Joseph Adams, Emmanuel Broni, Kweku S. Enninful, Clement Agoni, Mahmoud E. S. Soliman and Michael D. Wilson
Pharmaceuticals 2023, 16(3), 332; https://doi.org/10.3390/ph16030332 - 21 Feb 2023
Cited by 8 | Viewed by 3000
Abstract
The effect of Ebola virus disease (EVD) is fatal and devastating, necessitating several efforts to identify potent biotherapeutic molecules. This review seeks to provide perspectives on complementing existing work on Ebola virus (EBOV) by discussing the role of machine learning (ML) techniques in [...] Read more.
The effect of Ebola virus disease (EVD) is fatal and devastating, necessitating several efforts to identify potent biotherapeutic molecules. This review seeks to provide perspectives on complementing existing work on Ebola virus (EBOV) by discussing the role of machine learning (ML) techniques in the prediction of small molecule inhibitors of EBOV. Different ML algorithms have been used to predict anti-EBOV compounds, including Bayesian, support vector machine, and random forest algorithms, which present strong models with credible outcomes. The use of deep learning models for predicting anti-EBOV molecules is underutilized; therefore, we discuss how such models could be leveraged to develop fast, efficient, robust, and novel algorithms to aid in the discovery of anti-EBOV drugs. We further discuss the deep neural network as a plausible ML algorithm for predicting anti-EBOV compounds. We also summarize the plethora of data sources necessary for ML predictions in the form of systematic and comprehensive high-dimensional data. With ongoing efforts to eradicate EVD, the application of artificial intelligence-based ML to EBOV drug discovery research can promote data-driven decision making and may help to reduce the high attrition rates of compounds in the drug development pipeline. Full article
(This article belongs to the Special Issue Artificial Intelligence Applied to Medicinal Chemistry and Structural Biology)
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