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Machine Learning in Small-Molecule Drug Discovery 2.0

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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 (15 March 2023) | Viewed by 6827

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Special Issue Editors

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

Dr. Ya Chen

E-Mail Website
Guest Editor

Department of Pharmaceutical Chemistry, University of Vienna, Althanstrasse 14, 1090 Vienna, Austria
Interests: cheminformatics; computer-aided drug design; machine learning; bioactive natural products; target prediction

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 desirable properties, including favorable bioactivity spectra, beneficial ADME properties, low toxicity, synthetic accessibility, and novelty in molecular structures. 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 that focus 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 desirable 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
Dr. Ya Chen
Guest Editors

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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.

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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

Machine Learning in Small-Molecule Drug Discovery in International Journal of Molecular Sciences (3 articles)

Published Papers (6 papers)

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Research

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23 pages, 5835 KiB

Open AccessArticle

DeepsmirUD: Prediction of Regulatory Effects on microRNA Expression Mediated by Small Molecules Using Deep Learning

by Jianfeng Sun, Jinlong Ru, Lorenzo Ramos-Mucci, Fei Qi, Zihao Chen, Suyuan Chen, Adam P. Cribbs, Li Deng and Xia Wang

Int. J. Mol. Sci. 2023, 24(3), 1878; https://0-doi-org.brum.beds.ac.uk/10.3390/ijms24031878 - 18 Jan 2023

Cited by 5 | Viewed by 1885

Abstract

Aberrant miRNA expression has been associated with a large number of human diseases. Therefore, targeting miRNAs to regulate their expression levels has become an important therapy against diseases that stem from the dysfunction of pathways regulated by miRNAs. In recent years, small molecules have demonstrated enormous potential as drugs to regulate miRNA expression (i.e., SM-miR). A clear understanding of the mechanism of action of small molecules on the upregulation and downregulation of miRNA expression allows precise diagnosis and treatment of oncogenic pathways. However, outside of a slow and costly process of experimental determination, computational strategies to assist this on an ad hoc basis have yet to be formulated. In this work, we developed, to the best of our knowledge, the first cross-platform prediction tool, DeepsmirUD, to infer small-molecule-mediated regulatory effects on miRNA expression (i.e., upregulation or downregulation). This method is powered by 12 cutting-edge deep-learning frameworks and achieved AUC values of 0.843/0.984 and AUCPR values of 0.866/0.992 on two independent test datasets. With a complementarily constructed network inference approach based on similarity, we report a significantly improved accuracy of 0.813 in determining the regulatory effects of nearly 650 associated SM-miR relations, each formed with either novel small molecule or novel miRNA. By further integrating miRNA–cancer relationships, we established a database of potential pharmaceutical drugs from 1343 small molecules for 107 cancer diseases to understand the drug mechanisms of action and offer novel insight into drug repositioning. Furthermore, we have employed DeepsmirUD to predict the regulatory effects of a large number of high-confidence associated SM-miR relations. Taken together, our method shows promise to accelerate the development of potential miRNA targets and small molecule drugs. Full article

(This article belongs to the Special Issue Machine Learning in Small-Molecule Drug Discovery 2.0)

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22 pages, 4403 KiB

Open AccessArticle

Interpretable Machine Learning Models for Molecular Design of Tyrosine Kinase Inhibitors Using Variational Autoencoders and Perturbation-Based Approach of Chemical Space Exploration

by Keerthi Krishnan, Ryan Kassab, Steve Agajanian and Gennady Verkhivker

Int. J. Mol. Sci. 2022, 23(19), 11262; https://0-doi-org.brum.beds.ac.uk/10.3390/ijms231911262 - 24 Sep 2022

Cited by 5 | Viewed by 2181

Abstract

In the current study, we introduce an integrative machine learning strategy for the autonomous molecular design of protein kinase inhibitors using variational autoencoders and a novel cluster-based perturbation approach for exploration of the chemical latent space. The proposed strategy combines autoencoder-based embedding of small molecules with a cluster-based perturbation approach for efficient navigation of the latent space and a feature-based kinase inhibition likelihood classifier that guides optimization of the molecular properties and targeted molecular design. In the proposed generative approach, molecules sharing similar structures tend to cluster in the latent space, and interpolating between two molecules in the latent space enables smooth changes in the molecular structures and properties. The results demonstrated that the proposed strategy can efficiently explore the latent space of small molecules and kinase inhibitors along interpretable directions to guide the generation of novel family-specific kinase molecules that display a significant scaffold diversity and optimal biochemical properties. Through assessment of the latent-based and chemical feature-based binary and multiclass classifiers, we developed a robust probabilistic evaluator of kinase inhibition likelihood that is specifically tailored to guide the molecular design of novel SRC kinase molecules. The generated molecules originating from LCK and ABL1 kinase inhibitors yielded ~40% of novel and valid SRC kinase compounds with high kinase inhibition likelihood probability values (p > 0.75) and high similarity (Tanimoto coefficient > 0.6) to the known SRC inhibitors. By combining the molecular perturbation design with the kinase inhibition likelihood analysis and similarity assessments, we showed that the proposed molecular design strategy can produce novel valid molecules and transform known inhibitors of different kinase families into potential chemical probes of the SRC kinase with excellent physicochemical profiles and high similarity to the known SRC kinase drugs. The results of our study suggest that task-specific manipulation of a biased latent space may be an important direction for more effective task-oriented and target-specific autonomous chemical design models. Full article

(This article belongs to the Special Issue Machine Learning in Small-Molecule Drug Discovery 2.0)

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12 pages, 2395 KiB

Open AccessArticle

Deep Probabilistic Learning Model for Prediction of Ionic Liquids Toxicity

by Mapopa Chipofya, Hilal Tayara and Kil To Chong

Int. J. Mol. Sci. 2022, 23(9), 5258; https://0-doi-org.brum.beds.ac.uk/10.3390/ijms23095258 - 09 May 2022

Cited by 8 | Viewed by 2001

Abstract

Identification of ionic liquids with low toxicity is paramount for applications in various domains. Traditional approaches used for determining the toxicity of ionic liquids are often expensive, and can be labor intensive and time consuming. In order to mitigate these limitations, researchers have resorted to using computational models. This work presents a probabilistic model built from deep kernel learning with the aim of predicting the toxicity of ionic liquids in the leukemia rat cell line (IPC-81). Only open source tools, namely, RDKit and Mol2vec, are required to generate predictors for this model; as such, its predictions are solely based on chemical structure of the ionic liquids and no manual extraction of features is needed. The model recorded an RMSE of 0.228 and

R^{2}

of 0.943. These results indicate that the model is both reliable and accurate. Furthermore, this model provides an accompanying uncertainty level for every prediction it makes. This is important because discrepancies in experimental measurements that generated the dataset used herein are inevitable, and ought to be modeled. A user-friendly web server was developed as well, enabling researchers and practitioners ti make predictions using this model. Full article

(This article belongs to the Special Issue Machine Learning in Small-Molecule Drug Discovery 2.0)

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16 pages, 43960 KiB

Open AccessArticle

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 3637

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 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

Open AccessArticle

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 3167

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 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

Jump to: Research

34 pages, 2678 KiB

Open AccessReview

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 10438

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 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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