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Applied Deep Learning and Machine Learning in Drug Design and Discovery

A special issue of Applied Sciences (ISSN 2076-3417). This special issue belongs to the section "Applied Biosciences and Bioengineering".

Deadline for manuscript submissions: closed (15 March 2024) | Viewed by 1186

Special Issue Editor

Prof. Dr. Junghyun Kim

E-Mail Website
Guest Editor
Department of Artificial Intelligence, Sejong University, Seoul 05006, Republic of Korea
Interests: artificial intelligence; drug discovery; wireless communication; postquantum cryptography
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

This Special Issue focuses on the application of deep learning and machine learning techniques in the field of drug design and discovery. It aims to explore the innovative ways in which these advanced computational approaches are being employed to accelerate drug development, optimize molecular structures, predict drug–target interactions, and enhance our understanding of complex biological systems. The articles featured in this issue will showcase cutting-edge research and developments at the intersection of artificial intelligence, chemistry, and biology, contributing to the advancement of pharmaceutical science and therapeutics.

Prof. Dr. Junghyun Kim
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. Applied Sciences is an international peer-reviewed open access semimonthly 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 2400 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

  • drug design
  • drug discovery
  • artificial intelligence
  • machine learning
  • deep learning
  • drug–target interactions
  • drug–drug interactions

Published Papers (1 paper)

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Research

0 pages, 3349 KiB  
Article
Graph Neural Network and BERT Model for Antimalarial Drug Predictions Using Plasmodium Potential Targets
by Medard Edmund Mswahili, Goodwill Erasmo Ndomba, Kyuri Jo and Young-Seob Jeong
Appl. Sci. 2024, 14(4), 1472; https://doi.org/10.3390/app14041472 - 11 Feb 2024
Viewed by 933
Abstract
Malaria continues to pose a significant global health burden despite concerted efforts to combat it. In 2020, nearly half of the world’s population faced the risk of malaria, underscoring the urgency of innovative strategies to tackle this pervasive threat. One of the major [...] Read more.
Malaria continues to pose a significant global health burden despite concerted efforts to combat it. In 2020, nearly half of the world’s population faced the risk of malaria, underscoring the urgency of innovative strategies to tackle this pervasive threat. One of the major challenges lies in the emergence of the resistance of parasites to existing antimalarial drugs. This challenge necessitates the discovery of new, effective treatments capable of combating the Plasmodium parasite at various stages of its life cycle. Advanced computational approaches have been utilized to accelerate drug development, playing a crucial role in every stage of the drug discovery and development process. We have witnessed impressive and groundbreaking achievements, with GNNs applied to graph data and BERT from transformers across diverse NLP text analysis tasks. In this study, to facilitate a more efficient and effective approach, we proposed the integration of an NLP based model for SMILES (i.e., BERT) and a GNN model (i.e., RGCN) to predict the effect of antimalarial drugs against Plasmodium. The GNN model was trained using designed antimalarial drug and potential target (i.e., PfAcAS, F/GGPPS, and PfMAGL) graph-structured data with nodes representing antimalarial drugs and potential targets, and edges representing relationships between them. The performance of BERT-RGCN was further compared with that of Mordred-RGCN to evaluate its effectiveness. The BERT-RGCN and Mordred-RGCN models performed consistently well across different feature combinations, showcasing high accuracy, sensitivity, specificity, MCC, AUROC, and AUPRC values. These results suggest the effectiveness of the models in predicting antimalarial drugs against Plasmodium falciparum in various scenarios based on different sets of features of drugs and potential antimalarial targets. Full article
(This article belongs to the Special Issue Applied Deep Learning and Machine Learning in Drug Design and Discovery)
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