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Computational Intelligence (CI) Tools in Drug Discovery and Design

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A special issue of Pharmaceutics (ISSN 1999-4923). This special issue belongs to the section "Pharmaceutical Technology, Manufacturing and Devices".

Deadline for manuscript submissions: closed (31 July 2022) | Viewed by 33553

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Special Issue Editors

Dr. Jakub Szlęk

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Chair and Department of Pharmaceutical Technology and Biopharmaceutics, Faculty of Pharmacy, Jagiellonian University-Medical College, Medyczna 9 St, 30-688 Kraków, Poland
Interests: pharmaceutical technology; machine learning; solid dosage forms; drug dissolution; biopharmaceutics
Special Issues, Collections and Topics in MDPI journals

Dr. Adam Pacławski

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Chair and Department of Pharmaceutical Technology and Biopharmaceutics, Faculty of Pharmacy, Jagiellonian University-Medical College, Medyczna 9 St, 30-688 Kraków, Poland
Interests: artificial intelligence; machine learning; pulmonary drug delivery; particle technology; spray drying; biopharmaceutics; image processing and analysis
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

The demand of new drugs has increased in the last decades. Therefore, the discovery and development of new drugs and their pharmaceutical forms need to be fast and efficient, while maintaining a high quality. This may require the use of computational intelligence (CI) tools. CI usually refers to a program which is able to solve complex problems without prior knowledge of a phenomenon, by learning from data or experimental observations. Computers currently surpass the human brain in terms of data processing, and, if properly designed, computer programs could significantly accelerate the development of new drugs. Moreover, CI tools could help to discover complex and sometimes unobvious interactions between drugs and biological targets.

The purpose of this Special Issue of Pharmaceutics is to gather novel and interesting scientific research regarding the applications of computational intelligence tools in drug discovery and development. The focus will be on research articles and reviews on drug dosage forms and novel substances whose development was motivated by computational intelligence tools. Studies on other technological and pharmaceutical aspects of computer-aided drug design will also be welcome.

Dr. Jakub Szlęk
Dr. Adam Pacławski
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. Pharmaceutics 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

machine learning in drug design and development
artificial intelligence
data science
heuristic modeling of pharmaceutical processes
QSPR models

Published Papers (11 papers)

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Research

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17 pages, 25431 KiB

Open AccessArticle

DoubleSG-DTA: Deep Learning for Drug Discovery: Case Study on the Non-Small Cell Lung Cancer with EGFR^T790M Mutation

by Yongtao Qian, Wanxing Ni, Xingxing Xianyu, Liang Tao and Qin Wang

Pharmaceutics 2023, 15(2), 675; https://0-doi-org.brum.beds.ac.uk/10.3390/pharmaceutics15020675 - 16 Feb 2023

Cited by 5 | Viewed by 2029

Abstract

Drug–targeted therapies are promising approaches to treating tumors, and research on receptor–ligand interactions for discovering high-affinity targeted drugs has been accelerating drug development. This study presents a mechanism-driven deep learning-based computational model to learn double drug sequences, protein sequences, and drug graphs to project drug–target affinities (DTAs), which was termed the DoubleSG-DTA. We deployed lightweight graph isomorphism networks to aggregate drug graph representations and discriminate between molecular structures, and stacked multilayer squeeze-and-excitation networks to selectively enhance spatial features of drug and protein sequences. What is more, cross-multi-head attentions were constructed to further model the non-covalent molecular docking behavior. The multiple cross-validation experimental evaluations on various datasets indicated that DoubleSG-DTA consistently outperformed all previously reported works. To showcase the value of DoubleSG-DTA, we applied it to generate promising hit compounds of Non-Small Cell Lung Cancer harboring

E G F R^{T 790 M}

mutation from natural products, which were consistent with reported laboratory studies. Afterward, we further investigated the interpretability of the graph-based “black box” model and highlighted the active structures that contributed the most. DoubleSG-DTA thus provides a powerful and interpretable framework that extrapolates for potential chemicals to modulate the systemic response to disease. Full article

(This article belongs to the Special Issue Computational Intelligence (CI) Tools in Drug Discovery and Design)

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15 pages, 2982 KiB

Open AccessArticle

Fine-tuning of BERT Model to Accurately Predict Drug–Target Interactions

by Hyeunseok Kang, Sungwoo Goo, Hyunjung Lee, Jung-woo Chae, Hwi-yeol Yun and Sangkeun Jung

Pharmaceutics 2022, 14(8), 1710; https://0-doi-org.brum.beds.ac.uk/10.3390/pharmaceutics14081710 - 16 Aug 2022

Cited by 9 | Viewed by 3551

Abstract

The identification of optimal drug candidates is very important in drug discovery. Researchers in biology and computational sciences have sought to use machine learning (ML) to efficiently predict drug–target interactions (DTIs). In recent years, according to the emerging usefulness of pretrained models in natural language process (NLPs), pretrained models are being developed for chemical compounds and target proteins. This study sought to improve DTI predictive models using a Bidirectional Encoder Representations from the Transformers (BERT)-pretrained model, ChemBERTa, for chemical compounds. Pretraining features the use of a simplified molecular-input line-entry system (SMILES). We also employ the pretrained ProBERT for target proteins (pretraining employed the amino acid sequences). The BIOSNAP, DAVIS, and BindingDB databases (DBs) were used (alone or together) for learning. The final model, taught by both ChemBERTa and ProtBert and the integrated DBs, afforded the best DTI predictive performance to date based on the receiver operating characteristic area under the curve (AUC) and precision-recall-AUC values compared with previous models. The performance of the final model was verified using a specific case study on 13 pairs of subtrates and the metabolic enzyme cytochrome P450 (CYP). The final model afforded excellent DTI prediction. As the real-world interactions between drugs and target proteins are expected to exhibit specific patterns, pretraining with ChemBERTa and ProtBert could teach such patterns. Learning the patterns of such interactions would enhance DTI accuracy if learning employs large, well-balanced datasets that cover all relationships between drugs and target proteins. Full article

(This article belongs to the Special Issue Computational Intelligence (CI) Tools in Drug Discovery and Design)

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18 pages, 2866 KiB

Open AccessArticle

Do AutoML-Based QSAR Models Fulfill OECD Principles for Regulatory Assessment? A 5-HT_1A Receptor Case

by Natalia Czub, Adam Pacławski, Jakub Szlęk and Aleksander Mendyk

Pharmaceutics 2022, 14(7), 1415; https://0-doi-org.brum.beds.ac.uk/10.3390/pharmaceutics14071415 - 06 Jul 2022

Cited by 7 | Viewed by 2020

Abstract

The drug discovery and development process requires a lot of time, financial, and workforce resources. Any reduction in these burdens might benefit all stakeholders in the healthcare domain, including patients, government, and companies. One of the critical stages in drug discovery is a selection of molecular structures with a strong affinity to a particular molecular target. The possible solution is the development of predictive models and their application in the screening process, but due to the complexity of the problem, simple and statistical models might not be sufficient for practical application. The manuscript presents the best-in-class predictive model for the serotonin 1A receptor affinity and its validation according to the Organization for Economic Co-operation and Development guidelines for regulatory purposes. The model was developed based on a database with close to 9500 molecules by using an automatic machine learning tool (AutoML). The model selection was conducted based on the Akaike information criterion value and 10-fold cross-validation routine, and later good predictive ability was confirmed with an additional external validation dataset with over 700 molecules. Moreover, the multi-start technique was applied to test if an automatic model development procedure results in reliable results. Full article

(This article belongs to the Special Issue Computational Intelligence (CI) Tools in Drug Discovery and Design)

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16 pages, 5370 KiB

Open AccessArticle

Drug-Induced Immune Thrombocytopenia Toxicity Prediction Based on Machine Learning

by Binyou Wang, Xiaoqiu Tan, Jianmin Guo, Ting Xiao, Yan Jiao, Junlin Zhao, Jianming Wu and Yiwei Wang

Pharmaceutics 2022, 14(5), 943; https://0-doi-org.brum.beds.ac.uk/10.3390/pharmaceutics14050943 - 26 Apr 2022

Cited by 4 | Viewed by 2315

Abstract

Drug-induced immune thrombocytopenia (DITP) often occurs in patients receiving many drug treatments simultaneously. However, clinicians usually fail to accurately distinguish which drugs can be plausible culprits. Despite significant advances in laboratory-based DITP testing, in vitro experimental assays have been expensive and, in certain cases, cannot provide a timely diagnosis to patients. To address these shortcomings, this paper proposes an efficient machine learning-based method for DITP toxicity prediction. A small dataset consisting of 225 molecules was constructed. The molecules were represented by six fingerprints, three descriptors, and their combinations. Seven classical machine learning-based models were examined to determine an optimal model. The results show that the RDMD + PubChem-k-NN model provides the best prediction performance among all the models, achieving an area under the curve of 76.9% and overall accuracy of 75.6% on the external validation set. The application domain (AD) analysis demonstrates the prediction reliability of the RDMD + PubChem-k-NN model. Five structural fragments related to the DITP toxicity are identified through information gain (IG) method along with fragment frequency analysis. Overall, as far as known, it is the first machine learning-based classification model for recognizing chemicals with DITP toxicity and can be used as an efficient tool in drug design and clinical therapy. Full article

(This article belongs to the Special Issue Computational Intelligence (CI) Tools in Drug Discovery and Design)

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23 pages, 4536 KiB

Open AccessEditor’s ChoiceArticle

Puzzle out Machine Learning Model-Explaining Disintegration Process in ODTs

by Jakub Szlęk, Mohammad Hassan Khalid, Adam Pacławski, Natalia Czub and Aleksander Mendyk

Pharmaceutics 2022, 14(4), 859; https://0-doi-org.brum.beds.ac.uk/10.3390/pharmaceutics14040859 - 13 Apr 2022

Cited by 7 | Viewed by 2537

Abstract

Tablets are the most common dosage form of pharmaceutical products. While tablets represent the majority of marketed pharmaceutical products, there remain a significant number of patients who find it difficult to swallow conventional tablets. Such difficulties lead to reduced patient compliance. Orally disintegrating tablets (ODT), sometimes called oral dispersible tablets, are the dosage form of choice for patients with swallowing difficulties. ODTs are defined as a solid dosage form for rapid disintegration prior to swallowing. The disintegration time, therefore, is one of the most important and optimizable critical quality attributes (CQAs) for ODTs. Current strategies to optimize ODT disintegration times are based on a conventional trial-and-error method whereby a small number of samples are used as proxies for the compliance of whole batches. We present an alternative machine learning approach to optimize the disintegration time based on a wide variety of machine learning (ML) models through the H2O AutoML platform. ML models are presented with inputs from a database originally presented by Han et al., which was enhanced and curated to include chemical descriptors representing active pharmaceutical ingredient (API) characteristics. A deep learning model with a 10-fold cross-validation NRMSE of 8.1% and an R² of 0.84 was obtained. The critical parameters influencing the disintegration of the directly compressed ODTs were ascertained using the SHAP method to explain ML model predictions. A reusable, open-source tool, the ODT calculator, is now available at Heroku platform. Full article

(This article belongs to the Special Issue Computational Intelligence (CI) Tools in Drug Discovery and Design)

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19 pages, 15490 KiB

Open AccessArticle

An Explainable Supervised Machine Learning Model for Predicting Respiratory Toxicity of Chemicals Using Optimal Molecular Descriptors

by Keerthana Jaganathan, Hilal Tayara and Kil To Chong

Pharmaceutics 2022, 14(4), 832; https://0-doi-org.brum.beds.ac.uk/10.3390/pharmaceutics14040832 - 11 Apr 2022

Cited by 15 | Viewed by 3418

Abstract

Respiratory toxicity is a serious public health concern caused by the adverse effects of drugs or chemicals, so the pharmaceutical and chemical industries demand reliable and precise computational tools to assess the respiratory toxicity of compounds. The purpose of this study is to develop quantitative structure-activity relationship models for a large dataset of chemical compounds associated with respiratory system toxicity. First, several feature selection techniques are explored to find the optimal subset of molecular descriptors for efficient modeling. Then, eight different machine learning algorithms are utilized to construct respiratory toxicity prediction models. The support vector machine classifier outperforms all other optimized models in 10-fold cross-validation. Additionally, it outperforms the prior study by 2% in prediction accuracy and 4% in MCC. The best SVM model achieves a prediction accuracy of 86.2% and a MCC of 0.722 on the test set. The proposed SVM model predictions are explained using the SHapley Additive exPlanations approach, which prioritizes the relevance of key modeling descriptors influencing the prediction of respiratory toxicity. Thus, our proposed model would be incredibly beneficial in the early stages of drug development for predicting and understanding potential respiratory toxic compounds. Full article

(This article belongs to the Special Issue Computational Intelligence (CI) Tools in Drug Discovery and Design)

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Graphical abstract

19 pages, 871 KiB

Open AccessArticle

A Novel Deep Neural Network Technique for Drug–Target Interaction

by Jackson G. de Souza, Marcelo A. C. Fernandes and Raquel de Melo Barbosa

Pharmaceutics 2022, 14(3), 625; https://0-doi-org.brum.beds.ac.uk/10.3390/pharmaceutics14030625 - 11 Mar 2022

Cited by 6 | Viewed by 2997

Abstract

Drug discovery (DD) is a time-consuming and expensive process. Thus, the industry employs strategies such as drug repositioning and drug repurposing, which allows the application of already approved drugs to treat a different disease, as occurred in the first months of 2020, during the COVID-19 pandemic. The prediction of drug–target interactions is an essential part of the DD process because it can accelerate it and reduce the required costs. DTI prediction performed in silico have used approaches based on molecular docking simulations, including similarity-based and network- and graph-based ones. This paper presents MPS2IT-DTI, a DTI prediction model obtained from research conducted in the following steps: the definition of a new method for encoding molecule and protein sequences onto images; the definition of a deep-learning approach based on a convolutional neural network in order to create a new method for DTI prediction. Training results conducted with the Davis and KIBA datasets show that MPS2IT-DTI is viable compared to other state-of-the-art (SOTA) approaches in terms of performance and complexity of the neural network model. With the Davis dataset, we obtained 0.876 for the concordance index and 0.276 for the MSE; with the KIBA dataset, we obtained 0.836 and 0.226 for the concordance index and the MSE, respectively. Moreover, the MPS2IT-DTI model represents molecule and protein sequences as images, instead of treating them as an NLP task, and as such, does not employ an embedding layer, which is present in other models. Full article

(This article belongs to the Special Issue Computational Intelligence (CI) Tools in Drug Discovery and Design)

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11 pages, 859 KiB

Open AccessArticle

Drug Properties Prediction Based on Deep Learning

by Soyoung Yoo, Junghyun Kim and Guang J. Choi

Pharmaceutics 2022, 14(2), 467; https://0-doi-org.brum.beds.ac.uk/10.3390/pharmaceutics14020467 - 21 Feb 2022

Cited by 5 | Viewed by 2085

Abstract

In recent research on the formulation prediction of oral dissolving drugs, deep learning models with significantly improved performance compared to machine learning models were proposed. However, the performance degradation due to limitations of an imbalanced dataset with a small size and inefficient neural network structure has still not been resolved. Therefore, we propose new deep learning-based prediction models that maximize the prediction performance for disintegration time of oral fast disintegrating films (OFDF) and cumulative dissolution profiles of sustained-release matrix tablets (SRMT). In the case of OFDF, we use principal component analysis (PCA) to reduce the dimensionality of the dataset, thereby improving the prediction performance and reducing the training time. In the case of SRMT, the Wasserstein generative adversarial network (WGAN), a neural network-based generative model, is used to overcome the limitation of performance improvement due to the lack of experimental data. To the best of our knowledge, this is the first work that utilizes WGAN for pharmaceutical formulation prediction. Experimental results show that the proposed methods have superior performance than the existing methods for all the performance metrics considered. Full article

(This article belongs to the Special Issue Computational Intelligence (CI) Tools in Drug Discovery and Design)

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17 pages, 3299 KiB

Open AccessArticle

SCMTHP: A New Approach for Identifying and Characterizing of Tumor-Homing Peptides Using Estimated Propensity Scores of Amino Acids

by Phasit Charoenkwan, Wararat Chiangjong, Chanin Nantasenamat, Mohammad Ali Moni, Pietro Lio’, Balachandran Manavalan and Watshara Shoombuatong

Pharmaceutics 2022, 14(1), 122; https://0-doi-org.brum.beds.ac.uk/10.3390/pharmaceutics14010122 - 04 Jan 2022

Cited by 12 | Viewed by 2258

Abstract

Tumor-homing peptides (THPs) are small peptides that can recognize and bind cancer cells specifically. To gain a better understanding of THPs’ functional mechanisms, the accurate identification and characterization of THPs is required. Although some computational methods for in silico THP identification have been proposed, a major drawback is their lack of model interpretability. In this study, we propose a new, simple and easily interpretable computational approach (called SCMTHP) for identifying and analyzing tumor-homing activities of peptides via the use of a scoring card method (SCM). To improve the predictability and interpretability of our predictor, we generated propensity scores of 20 amino acids as THPs. Finally, informative physicochemical properties were used for providing insights on characteristics giving rise to the bioactivity of THPs via the use of SCMTHP-derived propensity scores. Benchmarking experiments from independent test indicated that SCMTHP could achieve comparable performance to state-of-the-art method with accuracies of 0.827 and 0.798, respectively, when evaluated on two benchmark datasets consisting of Main and Small datasets. Furthermore, SCMTHP was found to outperform several well-known machine learning-based classifiers (e.g., decision tree, k-nearest neighbor, multi-layer perceptron, naive Bayes and partial least squares regression) as indicated by both 10-fold cross-validation and independent tests. Finally, the SCMTHP web server was established and made freely available online. SCMTHP is expected to be a useful tool for rapid and accurate identification of THPs and for providing better understanding on THP biophysical and biochemical properties. Full article

(This article belongs to the Special Issue Computational Intelligence (CI) Tools in Drug Discovery and Design)

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22 pages, 2761 KiB

Open AccessArticle

Biomedical Text Link Prediction for Drug Discovery: A Case Study with COVID-19

by Kevin McCoy, Sateesh Gudapati, Lawrence He, Elaina Horlander, David Kartchner, Soham Kulkarni, Nidhi Mehra, Jayant Prakash, Helena Thenot, Sri Vivek Vanga, Abigail Wagner, Brandon White and Cassie S. Mitchell

Pharmaceutics 2021, 13(6), 794; https://0-doi-org.brum.beds.ac.uk/10.3390/pharmaceutics13060794 - 26 May 2021

Cited by 19 | Viewed by 5226

Abstract

Link prediction in artificial intelligence is used to identify missing links or derive future relationships that can occur in complex networks. A link prediction model was developed using the complex heterogeneous biomedical knowledge graph, SemNet, to predict missing links in biomedical literature for drug discovery. A web application visualized knowledge graph embeddings and link prediction results using TransE, CompleX, and RotatE based methods. The link prediction model achieved up to 0.44 hits@10 on the entity prediction tasks. The recent outbreak of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), also known as COVID-19, served as a case study to demonstrate the efficacy of link prediction modeling for drug discovery. The link prediction algorithm guided identification and ranking of repurposed drug candidates for SARS-CoV-2 primarily by text mining biomedical literature from previous coronaviruses, including SARS and middle east respiratory syndrome (MERS). Repurposed drugs included potential primary SARS-CoV-2 treatment, adjunctive therapies, or therapeutics to treat side effects. The link prediction accuracy for nodes ranked highly for SARS coronavirus was 0.875 as calculated by human in the loop validation on existing COVID-19 specific data sets. Drug classes predicted as highly ranked include anti-inflammatory, nucleoside analogs, protease inhibitors, antimalarials, envelope proteins, and glycoproteins. Examples of highly ranked predicted links to SARS-CoV-2: human leukocyte interferon, recombinant interferon-gamma, cyclosporine, antiviral therapy, zidovudine, chloroquine, vaccination, methotrexate, artemisinin, alkaloids, glycyrrhizic acid, quinine, flavonoids, amprenavir, suramin, complement system proteins, fluoroquinolones, bone marrow transplantation, albuterol, ciprofloxacin, quinolone antibacterial agents, and hydroxymethylglutaryl-CoA reductase inhibitors. Approximately 40% of identified drugs were not previously connected to SARS, such as edetic acid or biotin. In summary, link prediction can effectively suggest repurposed drugs for emergent diseases. Full article

(This article belongs to the Special Issue Computational Intelligence (CI) Tools in Drug Discovery and Design)

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Review

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19 pages, 5291 KiB

Open AccessReview

The Finite Element Analysis Research on Microneedle Design Strategy and Transdermal Drug Delivery System

by Qinying Yan, Shulin Shen, Yan Wang, Jiaqi Weng, Aiqun Wan, Gensheng Yang and Lili Feng

Pharmaceutics 2022, 14(8), 1625; https://0-doi-org.brum.beds.ac.uk/10.3390/pharmaceutics14081625 - 03 Aug 2022

Cited by 10 | Viewed by 3245

Abstract

Microneedles (MNs) as a novel transdermal drug delivery system have shown great potential for therapeutic and disease diagnosis applications by continually providing minimally invasive, portable, cost-effective, high bioavailability, and easy-to-use tools compared to traditional parenteral administrations. However, microneedle transdermal drug delivery is still in its infancy. Many research studies need further in-depth exploration, such as safety, structural characteristics, and drug loading performance evaluation. Finite element analysis (FEA) uses mathematical approximations to simulate real physical systems (geometry and load conditions). It can simplify complex engineering problems to guide the precise preparation and potential industrialization of microneedles, which has attracted extensive attention. This article introduces FEA research for microneedle transdermal drug delivery systems, focusing on microneedle design strategy, skin mechanics models, skin permeability, and the FEA research on drug delivery by MNs. Full article

(This article belongs to the Special Issue Computational Intelligence (CI) Tools in Drug Discovery and Design)

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