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Machine Learning in Small-Molecule Drug Discovery
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Machine Learning in Small-Molecule Drug Discovery

A special issue of International Journal of Molecular Sciences (ISSN 1422-0067). This special issue belongs to the section "Molecular Informatics".

Deadline for manuscript submissions: closed (31 March 2021) | Viewed by 18370

Special Issue Editor

Prof. Dr. Johannes Kirchmair

E-Mail Website
Guest Editor
Department of Pharmaceutical Chemistry, University of Vienna, Althanstrasse 14, 1090 Vienna, Austria
Interests: cheminformatics; computer-aided molecular design; machine learning; ADME prediction; target prediction; toxicity prediction
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

Machine learning has evolved as a key technology in small-molecule drug discovery. A rich body of literature documents the capacity of machine learning approaches to design and identify compounds with desired properties, including favorable bioactivity spectra, beneficial ADME properties, low toxicity, synthetic accessibility, and novelty in molecular structure. However, the significant advances in machine learning and artificial intelligence seen in recent years come with a new set of challenges, such as the limited interpretability of models.

On these grounds, this Special Issue seeks original research articles and reviews focusing on all aspects of machine learning relevant to small-molecule drug discovery. Scientists are particularly encouraged to submit contributions on the development and application of machine learning methods for the design of bioactive small molecules with desired chemical and pharmacological properties, the prediction of the macromolecular target(s) of compounds, and the assessment and optimization of ADME properties, safety profiles, and synthetic accessibility. Further topics of high interest include automation in drug design, integrated models, machine learning in drug repurposing and natural products research, virtual screening, the performance of methods as a function of the quantity and quality of the training data, concepts of the applicability domain, and benchmarking.

Prof. Dr. Johannes Kirchmair
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. International Journal of Molecular Sciences is an international peer-reviewed open access semimonthly journal published by MDPI.

Please visit the Instructions for Authors page before submitting a manuscript. There is an Article Processing Charge (APC) for publication in this open access journal. For details about the APC please see here. 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

  • small-molecule drug discovery
  • machine learning
  • artificial intelligence
  • automation
  • integrated models
  • explainable, chemistry-aware methods
  • molecular representations
  • intelligent compound design
  • virtual screening
  • synthesis prediction
  • drug repurposing
  • natural products
  • datasets for model development
  • applicability domain
  • benchmarking

Related Special Issue

Published Papers (3 papers)

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Research

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16 pages, 43960 KiB  
Article
Target Prediction Model for Natural Products Using Transfer Learning
by Bo Qiang, Junyong Lai, Hongwei Jin, Liangren Zhang and Zhenming Liu
Int. J. Mol. Sci. 2021, 22(9), 4632; https://0-doi-org.brum.beds.ac.uk/10.3390/ijms22094632 - 28 Apr 2021
Cited by 9 | Viewed by 3639
Abstract
A large proportion of lead compounds are derived from natural products. However, most natural products have not been fully tested for their targets. To help resolve this problem, a model using transfer learning was built to predict targets for natural products. The model [...] Read more.
A large proportion of lead compounds are derived from natural products. However, most natural products have not been fully tested for their targets. To help resolve this problem, a model using transfer learning was built to predict targets for natural products. The model was pre-trained on a processed ChEMBL dataset and then fine-tuned on a natural product dataset. Benefitting from transfer learning and the data balancing technique, the model achieved a highly promising area under the receiver operating characteristic curve (AUROC) score of 0.910, with limited task-related training samples. Since the embedding distribution difference is reduced, embedding space analysis demonstrates that the model’s outputs of natural products are reliable. Case studies have proved our model’s performance in drug datasets. The fine-tuned model can successfully output all the targets of 62 drugs. Compared with a previous study, our model achieved better results in terms of both AUROC validation and its success rate for obtaining active targets among the top ones. The target prediction model using transfer learning can be applied in the field of natural product-based drug discovery and has the potential to find more lead compounds or to assist researchers in drug repurposing. Full article
(This article belongs to the Special Issue Machine Learning in Small-Molecule Drug Discovery)
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20 pages, 5330 KiB  
Article
A Novel Computational Approach for the Discovery of Drug Delivery System Candidates for COVID-19
by Taeheum Cho, Hyo-Sang Han, Junhyuk Jeong, Eun-Mi Park and Kyu-Sik Shim
Int. J. Mol. Sci. 2021, 22(6), 2815; https://0-doi-org.brum.beds.ac.uk/10.3390/ijms22062815 - 10 Mar 2021
Cited by 8 | Viewed by 3169
Abstract
In order to treat Coronavirus Disease 2019 (COVID-19), we predicted and implemented a drug delivery system (DDS) that can provide stable drug delivery through a computational approach including a clustering algorithm and the Schrödinger software. Six carrier candidates were derived by the proposed [...] Read more.
In order to treat Coronavirus Disease 2019 (COVID-19), we predicted and implemented a drug delivery system (DDS) that can provide stable drug delivery through a computational approach including a clustering algorithm and the Schrödinger software. Six carrier candidates were derived by the proposed method that could find molecules meeting the predefined conditions using the molecular structure and its functional group positional information. Then, just one compound named glycyrrhizin was selected as a candidate for drug delivery through the Schrödinger software. Using glycyrrhizin, nafamostat mesilate (NM), which is known for its efficacy, was converted into micelle nanoparticles (NPs) to improve drug stability and to effectively treat COVID-19. The spherical particle morphology was confirmed by transmission electron microscopy (TEM), and the particle size and stability of 300–400 nm were evaluated by measuring DLSand the zeta potential. The loading of NM was confirmed to be more than 90% efficient using the UV spectrum. Full article
(This article belongs to the Special Issue Machine Learning in Small-Molecule Drug Discovery)
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Review

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34 pages, 2678 KiB  
Review
Deep Learning in Virtual Screening: Recent Applications and Developments
by Talia B. Kimber, Yonghui Chen and Andrea Volkamer
Int. J. Mol. Sci. 2021, 22(9), 4435; https://0-doi-org.brum.beds.ac.uk/10.3390/ijms22094435 - 23 Apr 2021
Cited by 81 | Viewed by 10453
Abstract
Drug discovery is a cost and time-intensive process that is often assisted by computational methods, such as virtual screening, to speed up and guide the design of new compounds. For many years, machine learning methods have been successfully applied in the context of [...] Read more.
Drug discovery is a cost and time-intensive process that is often assisted by computational methods, such as virtual screening, to speed up and guide the design of new compounds. For many years, machine learning methods have been successfully applied in the context of computer-aided drug discovery. Recently, thanks to the rise of novel technologies as well as the increasing amount of available chemical and bioactivity data, deep learning has gained a tremendous impact in rational active compound discovery. Herein, recent applications and developments of machine learning, with a focus on deep learning, in virtual screening for active compound design are reviewed. This includes introducing different compound and protein encodings, deep learning techniques as well as frequently used bioactivity and benchmark data sets for model training and testing. Finally, the present state-of-the-art, including the current challenges and emerging problems, are examined and discussed. Full article
(This article belongs to the Special Issue Machine Learning in Small-Molecule Drug Discovery)
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