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Artificial Intelligence and Machine Learning in Drug Development
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Artificial Intelligence and Machine Learning in Drug Development

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

Deadline for manuscript submissions: closed (30 April 2022) | Viewed by 12592

Special Issue Editor

Dr. Irina Moreira

E-Mail Website
Guest Editor
1. Department of Life Sciences, University of Coimbra, Calçada Martim de Freitas, 3000-456 Coimbra, Portugal
2. Center for Innovative Biomedicine and Biotechnology, University of Coimbra, 3004-535 Coimbra, Portugal
Interests: data science; drug discovery; deep learning; computational chemistry; structural biology; protein–protein complexes; modeling; GPCRs; functional selectivity
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

High-throughput screening technologies used in the development of data in genomics, proteomics, and metabolomics continue to produce large amounts of data from different populations, cell types, and diseases. The analysis of such data has produced promising results in genomic biomedicine, but encounters difficulties due to the heterogeneity of disease, with multiple causal pathways leading to similar symptoms but requiring different therapeutic approaches. This is further exacerbated by human biological complexity and genomic variability, which lead to different responses to therapeutic approaches at both the individual and population levels. Molecular profiling is now able to stratify diseases into their distinct molecular subtypes for matching with appropriate drugs, thus beginning to shape a translational systems medicine for better tailored predictive and pharmacotherapeutic guidance. This new research paradigm, powered by state-of-the-art artificial intelligence (AI)/machine learning (ML)-based prediction algorithms, presents great challenges and opportunities for researchers in the field.

This Special Issue welcomes original research, short communications, and review papers. Potential topics include, but are not limited to, the application of AI/ML to: target identification and characterization; protein networks/pathways prediction; mechanism of disease; drug–target complex formation and characterization; drug identification; drug repurposing; generation of novel drug candidates; drug efficacy metrics; and toxicology, biopharmaceutical properties prediction, etc. Wet-lab and clinical-data-based submissions with biomolecular experiments are welcomed.

Dr. Irina Moreira
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

  • artificial intelligence
  • big data
  • in silico drug design and discovery
  • biophysics
  • omics
  • clinical data
  • near-real-time prediction algorithms
  • personalized medicine

Published Papers (4 papers)

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Research

18 pages, 4775 KiB  
Article
Quantum Artificial Neural Network Approach to Derive a Highly Predictive 3D-QSAR Model for Blood–Brain Barrier Passage
by Taeho Kim, Byoung Hoon You, Songhee Han, Ho Chul Shin, Kee-Choo Chung and Hwangseo Park
Int. J. Mol. Sci. 2021, 22(20), 10995; https://0-doi-org.brum.beds.ac.uk/10.3390/ijms222010995 - 12 Oct 2021
Cited by 10 | Viewed by 2105
Abstract
A successful passage of the blood–brain barrier (BBB) is an essential prerequisite for the drug molecules designed to act on the central nervous system. The logarithm of blood–brain partitioning (LogBB) has served as an effective index of molecular BBB permeability. Using the three-dimensional [...] Read more.
A successful passage of the blood–brain barrier (BBB) is an essential prerequisite for the drug molecules designed to act on the central nervous system. The logarithm of blood–brain partitioning (LogBB) has served as an effective index of molecular BBB permeability. Using the three-dimensional (3D) distribution of the molecular electrostatic potential (ESP) as the numerical descriptor, a quantitative structure-activity relationship (QSAR) model termed AlphaQ was derived to predict the molecular LogBB values. To obtain the optimal atomic coordinates of the molecules under investigation, the pairwise 3D structural alignments were conducted in such a way to maximize the quantum mechanical cross correlation between the template and a target molecule. This alignment method has the advantage over the conventional atom-by-atom matching protocol in that the structurally diverse molecules can be analyzed as rigorously as the chemical derivatives with the same scaffold. The inaccuracy problem in the 3D structural alignment was alleviated in a large part by categorizing the molecules into the eight subsets according to the molecular weight. By applying the artificial neural network algorithm to associate the fully quantum mechanical ESP descriptors with the extensive experimental LogBB data, a highly predictive 3D-QSAR model was derived for each molecular subset with a squared correlation coefficient larger than 0.8. Due to the simplicity in model building and the high predictability, AlphaQ is anticipated to serve as an effective computational screening tool for molecular BBB permeability. Full article
(This article belongs to the Special Issue Artificial Intelligence and Machine Learning in Drug Development)
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13 pages, 15099 KiB  
Article
Predicting Drug–Target Interactions Based on the Ensemble Models of Multiple Feature Pairs
by Cheng Wang, Jun Zhang, Peng Chen and Bing Wang
Int. J. Mol. Sci. 2021, 22(12), 6598; https://0-doi-org.brum.beds.ac.uk/10.3390/ijms22126598 - 20 Jun 2021
Cited by 2 | Viewed by 1648
Abstract
Backgroud: The prediction of drug–target interactions (DTIs) is of great significance in drug development. It is time-consuming and expensive in traditional experimental methods. Machine learning can reduce the cost of prediction and is limited by the characteristics of imbalanced datasets and problems of [...] Read more.
Backgroud: The prediction of drug–target interactions (DTIs) is of great significance in drug development. It is time-consuming and expensive in traditional experimental methods. Machine learning can reduce the cost of prediction and is limited by the characteristics of imbalanced datasets and problems of essential feature selection. Methods: The prediction method based on the Ensemble model of Multiple Feature Pairs (Ensemble-MFP) is introduced. Firstly, three negative sets are generated according to the Euclidean distance of three feature pairs. Then, the negative samples of the validation set/test set are randomly selected from the union set of the three negative sets in the validation set/test set. At the same time, the ensemble model with weight is optimized and applied to the test set. Results: The area under the receiver operating characteristic curve (area under ROC, AUC) in three out of four sub-datasets in gold standard datasets was more than 94.0% in the prediction of new drugs. The effectiveness of the proposed method is also shown with the comparison of state-of-the-art methods and demonstration of predicted drug–target pairs. Conclusion: The Ensemble-MFP can weigh the existing feature pairs and has a good prediction effect for general prediction on new drugs. Full article
(This article belongs to the Special Issue Artificial Intelligence and Machine Learning in Drug Development)
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16 pages, 44299 KiB  
Article
Ligand-Receptor Interactions and Machine Learning in GCGR and GLP-1R Drug Discovery
by Mikołaj Mizera and Dorota Latek
Int. J. Mol. Sci. 2021, 22(8), 4060; https://0-doi-org.brum.beds.ac.uk/10.3390/ijms22084060 - 14 Apr 2021
Cited by 7 | Viewed by 3537
Abstract
The large amount of data that has been collected so far for G protein-coupled receptors requires machine learning (ML) approaches to fully exploit its potential. Our previous ML model based on gradient boosting used for prediction of drug affinity and selectivity for a [...] Read more.
The large amount of data that has been collected so far for G protein-coupled receptors requires machine learning (ML) approaches to fully exploit its potential. Our previous ML model based on gradient boosting used for prediction of drug affinity and selectivity for a receptor subtype was compared with explicit information on ligand-receptor interactions from induced-fit docking. Both methods have proved their usefulness in drug response predictions. Yet, their successful combination still requires allosteric/orthosteric assignment of ligands from datasets. Our ligand datasets included activities of two members of the secretin receptor family: GCGR and GLP-1R. Simultaneous activation of two or three receptors of this family by dual or triple agonists is not a typical kind of information included in compound databases. A precise allosteric/orthosteric ligand assignment requires a continuous update based on new structural and biological data. This data incompleteness remains the main obstacle for current ML methods applied to class B GPCR drug discovery. Even so, for these two class B receptors, our ligand-based ML model demonstrated high accuracy (5-fold cross-validation Q2 > 0.63 and Q2 > 0.67 for GLP-1R and GCGR, respectively). In addition, we performed a ligand annotation using recent cryogenic-electron microscopy (cryo-EM) and X-ray crystallographic data on small-molecule complexes of GCGR and GLP-1R. As a result, we assigned GLP-1R and GCGR actives deposited in ChEMBL to four small-molecule binding sites occupied by positive and negative allosteric modulators and a full agonist. Annotated compounds were added to our recently released repository of GPCR data. Full article
(This article belongs to the Special Issue Artificial Intelligence and Machine Learning in Drug Development)
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19 pages, 4926 KiB  
Article
SPOTONE: Hot Spots on Protein Complexes with Extremely Randomized Trees via Sequence-Only Features
by A. J. Preto and Irina S. Moreira
Int. J. Mol. Sci. 2020, 21(19), 7281; https://0-doi-org.brum.beds.ac.uk/10.3390/ijms21197281 - 01 Oct 2020
Cited by 10 | Viewed by 3305
Abstract
Protein Hot-Spots (HS) are experimentally determined amino acids, key to small ligand binding and tend to be structural landmarks on protein–protein interactions. As such, they were extensively approached by structure-based Machine Learning (ML) prediction methods. However, the availability of a much larger array [...] Read more.
Protein Hot-Spots (HS) are experimentally determined amino acids, key to small ligand binding and tend to be structural landmarks on protein–protein interactions. As such, they were extensively approached by structure-based Machine Learning (ML) prediction methods. However, the availability of a much larger array of protein sequences in comparison to determined tree-dimensional structures indicates that a sequence-based HS predictor has the potential to be more useful for the scientific community. Herein, we present SPOTONE, a new ML predictor able to accurately classify protein HS via sequence-only features. This algorithm shows accuracy, AUROC, precision, recall and F1-score of 0.82, 0.83, 0.91, 0.82 and 0.85, respectively, on an independent testing set. The algorithm is deployed within a free-to-use webserver, only requiring the user to submit a FASTA file with one or more protein sequences. Full article
(This article belongs to the Special Issue Artificial Intelligence and Machine Learning in Drug Development)
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