1
|
Amorim AM, Piochi LF, Gaspar AT, Preto A, Rosário-Ferreira N, Moreira IS. Advancing Drug Safety in Drug Development: Bridging Computational Predictions for Enhanced Toxicity Prediction. Chem Res Toxicol 2024; 37:827-849. [PMID: 38758610 PMCID: PMC11187637 DOI: 10.1021/acs.chemrestox.3c00352] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/06/2023] [Revised: 04/29/2024] [Accepted: 05/07/2024] [Indexed: 05/19/2024]
Abstract
The attrition rate of drugs in clinical trials is generally quite high, with estimates suggesting that approximately 90% of drugs fail to make it through the process. The identification of unexpected toxicity issues during preclinical stages is a significant factor contributing to this high rate of failure. These issues can have a major impact on the success of a drug and must be carefully considered throughout the development process. These late-stage rejections or withdrawals of drug candidates significantly increase the costs associated with drug development, particularly when toxicity is detected during clinical trials or after market release. Understanding drug-biological target interactions is essential for evaluating compound toxicity and safety, as well as predicting therapeutic effects and potential off-target effects that could lead to toxicity. This will enable scientists to predict and assess the safety profiles of drug candidates more accurately. Evaluation of toxicity and safety is a critical aspect of drug development, and biomolecules, particularly proteins, play vital roles in complex biological networks and often serve as targets for various chemicals. Therefore, a better understanding of these interactions is crucial for the advancement of drug development. The development of computational methods for evaluating protein-ligand interactions and predicting toxicity is emerging as a promising approach that adheres to the 3Rs principles (replace, reduce, and refine) and has garnered significant attention in recent years. In this review, we present a thorough examination of the latest breakthroughs in drug toxicity prediction, highlighting the significance of drug-target binding affinity in anticipating and mitigating possible adverse effects. In doing so, we aim to contribute to the development of more effective and secure drugs.
Collapse
Affiliation(s)
- Ana M.
B. Amorim
- Department
of Life Sciences, University of Coimbra, Calçada Martim de Freitas, 3000-456 Coimbra, Portugal
- CNC-UC—Center
for Neuroscience and Cell Biology, University
of Coimbra, Calçada Martim de Freitas, 3000-456 Coimbra, Portugal
- CIBB—Centre
for Innovative Biomedicine and Biotechnology, University of Coimbra, Calçada Martim de Freitas, 3000-456 Coimbra, Portugal
- PhD
Programme in Biosciences, Department of Life Sciences, University of Coimbra, Calçada Martim de Freitas, 3000-456 Coimbra, Portugal
- PURR.AI,
Rua Pedro Nunes, IPN Incubadora, Ed C, 3030-199 Coimbra, Portugal
| | - Luiz F. Piochi
- Department
of Life Sciences, University of Coimbra, Calçada Martim de Freitas, 3000-456 Coimbra, Portugal
- CNC-UC—Center
for Neuroscience and Cell Biology, University
of Coimbra, Calçada Martim de Freitas, 3000-456 Coimbra, Portugal
- CIBB—Centre
for Innovative Biomedicine and Biotechnology, University of Coimbra, Calçada Martim de Freitas, 3000-456 Coimbra, Portugal
| | - Ana T. Gaspar
- Department
of Life Sciences, University of Coimbra, Calçada Martim de Freitas, 3000-456 Coimbra, Portugal
- CNC-UC—Center
for Neuroscience and Cell Biology, University
of Coimbra, Calçada Martim de Freitas, 3000-456 Coimbra, Portugal
- CIBB—Centre
for Innovative Biomedicine and Biotechnology, University of Coimbra, Calçada Martim de Freitas, 3000-456 Coimbra, Portugal
| | - António
J. Preto
- CNC-UC—Center
for Neuroscience and Cell Biology, University
of Coimbra, Calçada Martim de Freitas, 3000-456 Coimbra, Portugal
- CIBB—Centre
for Innovative Biomedicine and Biotechnology, University of Coimbra, Calçada Martim de Freitas, 3000-456 Coimbra, Portugal
- PhD Programme
in Experimental Biology and Biomedicine, Institute for Interdisciplinary
Research (IIIUC), University of Coimbra, Casa Costa Alemão, 3030-789 Coimbra, Portugal
| | - Nícia Rosário-Ferreira
- CNC-UC—Center
for Neuroscience and Cell Biology, University
of Coimbra, Calçada Martim de Freitas, 3000-456 Coimbra, Portugal
- CIBB—Centre
for Innovative Biomedicine and Biotechnology, University of Coimbra, Calçada Martim de Freitas, 3000-456 Coimbra, Portugal
| | - Irina S. Moreira
- Department
of Life Sciences, University of Coimbra, Calçada Martim de Freitas, 3000-456 Coimbra, Portugal
- CNC-UC—Center
for Neuroscience and Cell Biology, University
of Coimbra, Calçada Martim de Freitas, 3000-456 Coimbra, Portugal
- CIBB—Centre
for Innovative Biomedicine and Biotechnology, University of Coimbra, Calçada Martim de Freitas, 3000-456 Coimbra, Portugal
| |
Collapse
|
2
|
Sanches IH, Braga RC, Alves VM, Andrade CH. Enhancing hERG Risk Assessment with Interpretable Classificatory and Regression Models. Chem Res Toxicol 2024; 37:910-922. [PMID: 38781421 PMCID: PMC11187631 DOI: 10.1021/acs.chemrestox.3c00400] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/15/2023] [Revised: 04/22/2024] [Accepted: 05/14/2024] [Indexed: 05/25/2024]
Abstract
The human Ether-à-go-go-Related Gene (hERG) is a transmembrane protein that regulates cardiac action potential, and its inhibition can induce a potentially deadly cardiac syndrome. In vitro tests help identify hERG blockers at early stages; however, the high cost motivates searching for alternative, cost-effective methods. The primary goal of this study was to enhance the Pred-hERG tool for predicting hERG blockage. To achieve this, we developed new QSAR models that incorporated additional data, updated existing classificatory and multiclassificatory models, and introduced new regression models. Notably, we integrated SHAP (SHapley Additive exPlanations) values to offer a visual interpretation of these models. Utilizing the latest data from ChEMBL v30, encompassing over 14,364 compounds with hERG data, our binary and multiclassification models outperformed both the previous iteration of Pred-hERG and all publicly available models. Notably, the new version of our tool introduces a regression model for predicting hERG activity (pIC50). The optimal model demonstrated an R2 of 0.61 and an RMSE of 0.48, surpassing the only available regression model in the literature. Pred-hERG 5.0 now offers users a swift, reliable, and user-friendly platform for the early assessment of chemically induced cardiotoxicity through hERG blockage. The tool provides versatile outcomes, including (i) classificatory predictions of hERG blockage with prediction reliability, (ii) multiclassificatory predictions of hERG blockage with reliability, (iii) regression predictions with estimated pIC50 values, and (iv) probability maps illustrating the contribution of chemical fragments for each prediction. Furthermore, we implemented explainable AI analysis (XAI) to visualize SHAP values, providing insights into the contribution of each feature to binary classification predictions. A consensus prediction calculated based on the predictions of the three developed models is also present to assist the user's decision-making process. Pred-hERG 5.0 has been designed to be user-friendly, making it accessible to users without computational or programming expertise. The tool is freely available at http://predherg.labmol.com.br.
Collapse
Affiliation(s)
- Igor H. Sanches
- Laboratory
for Molecular Modeling and Drug Design (LabMol), Faculty of Pharmacy, Universidade Federal de Goiás, Goiânia, GO 74690-900, Brazil
- Center
for Excellence in Artificial Intelligence (CEIA), Institute of Informatics, Universidade Federal de Goiás, Goiânia, GO 74690-900, Brazil
- Center
for the Research and Advancement in Fragments and Molecular Targets
(CRAFT), School of Pharmaceutical Sciences at Ribeirao Preto, University of São Paulo, Ribeirão Preto, SP 05508-220, Brazil
| | | | - Vinicius M. Alves
- University
of North Carolina at Chapel Hill, Chapel Hill, North Carolina 27599, United States
| | - Carolina Horta Andrade
- Laboratory
for Molecular Modeling and Drug Design (LabMol), Faculty of Pharmacy, Universidade Federal de Goiás, Goiânia, GO 74690-900, Brazil
- Center
for Excellence in Artificial Intelligence (CEIA), Institute of Informatics, Universidade Federal de Goiás, Goiânia, GO 74690-900, Brazil
- Center
for the Research and Advancement in Fragments and Molecular Targets
(CRAFT), School of Pharmaceutical Sciences at Ribeirao Preto, University of São Paulo, Ribeirão Preto, SP 05508-220, Brazil
| |
Collapse
|
3
|
Liu H, Chen P, Hu B, Wang S, Wang H, Luan J, Wang J, Lin B, Cheng M. FaissMolLib: An efficient and easy deployable tool for ligand-based virtual screening. Comput Biol Chem 2024; 110:108057. [PMID: 38581840 DOI: 10.1016/j.compbiolchem.2024.108057] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/23/2023] [Revised: 03/06/2024] [Accepted: 03/20/2024] [Indexed: 04/08/2024]
Abstract
Virtual screening-based molecular similarity and fingerprint are crucial in drug design, target prediction, and ADMET prediction, aiding in identifying potential hits and optimizing lead compounds. However, challenges such as lack of comprehensive open-source molecular fingerprint databases and efficient search methods for virtual screening are prevalent. To address these issues, we introduce FaissMolLib, an open-source virtual screening tool that integrates 2.8 million compounds from ChEMBL and ZINC databases. Notably, FaissMolLib employs the highly efficient Faiss search algorithm, outperforming the Tanimoto algorithm in identifying similar molecules with its tighter clustering in scatter plots and lower mean, standard deviation, and variance in key molecular properties. This feature enables FaissMolLib to screen 2.8 million compounds in just 0.05 seconds, offering researchers an efficient, easily deployable solution for virtual screening on laptops and building unique compound databases. This significant advancement holds great potential for accelerating drug discovery efforts and enhancing chemical data analysis. FaissMolLib is freely available at http://liuhaihan.gnway.cc:80. The code and dataset of FaissMolLib are freely available at https://github.com/Superhaihan/FiassMolLib.
Collapse
Affiliation(s)
- Haihan Liu
- Key Laboratory of Structure-Based Drug Design &Discovery of Ministry of Education, Shenyang Pharmaceutical University, Shenyang 110016, People's Republic of China; Key Laboratory of Intelligent Drug Design and New Drug Discovery of Liaoning Province, Shenyang Pharmaceutical University, Shenyang 110016, People's Republic of China; School of Pharmaceutical Engineering, Shenyang Pharmaceutical University, Shenyang 110016, People's Republic of China
| | - Peiying Chen
- Key Laboratory of Structure-Based Drug Design &Discovery of Ministry of Education, Shenyang Pharmaceutical University, Shenyang 110016, People's Republic of China; Key Laboratory of Intelligent Drug Design and New Drug Discovery of Liaoning Province, Shenyang Pharmaceutical University, Shenyang 110016, People's Republic of China; School of Pharmaceutical Engineering, Shenyang Pharmaceutical University, Shenyang 110016, People's Republic of China
| | - Baichun Hu
- Key Laboratory of Structure-Based Drug Design &Discovery of Ministry of Education, Shenyang Pharmaceutical University, Shenyang 110016, People's Republic of China; Key Laboratory of Intelligent Drug Design and New Drug Discovery of Liaoning Province, Shenyang Pharmaceutical University, Shenyang 110016, People's Republic of China; School of Pharmaceutical Engineering, Shenyang Pharmaceutical University, Shenyang 110016, People's Republic of China
| | - Shizun Wang
- Key Laboratory of Structure-Based Drug Design &Discovery of Ministry of Education, Shenyang Pharmaceutical University, Shenyang 110016, People's Republic of China; Key Laboratory of Intelligent Drug Design and New Drug Discovery of Liaoning Province, Shenyang Pharmaceutical University, Shenyang 110016, People's Republic of China; School of Pharmaceutical Engineering, Shenyang Pharmaceutical University, Shenyang 110016, People's Republic of China
| | - Hanxun Wang
- Key Laboratory of Structure-Based Drug Design &Discovery of Ministry of Education, Shenyang Pharmaceutical University, Shenyang 110016, People's Republic of China; Key Laboratory of Intelligent Drug Design and New Drug Discovery of Liaoning Province, Shenyang Pharmaceutical University, Shenyang 110016, People's Republic of China; School of Pharmaceutical Engineering, Shenyang Pharmaceutical University, Shenyang 110016, People's Republic of China
| | - Jiasi Luan
- Key Laboratory of Structure-Based Drug Design &Discovery of Ministry of Education, Shenyang Pharmaceutical University, Shenyang 110016, People's Republic of China; Key Laboratory of Intelligent Drug Design and New Drug Discovery of Liaoning Province, Shenyang Pharmaceutical University, Shenyang 110016, People's Republic of China; School of Medical Devices, Shenyang Pharmaceutical University, Shenyang 110016, People's Republic of China
| | - Jian Wang
- Key Laboratory of Structure-Based Drug Design &Discovery of Ministry of Education, Shenyang Pharmaceutical University, Shenyang 110016, People's Republic of China; Key Laboratory of Intelligent Drug Design and New Drug Discovery of Liaoning Province, Shenyang Pharmaceutical University, Shenyang 110016, People's Republic of China; School of Pharmaceutical Engineering, Shenyang Pharmaceutical University, Shenyang 110016, People's Republic of China.
| | - Bin Lin
- Key Laboratory of Structure-Based Drug Design &Discovery of Ministry of Education, Shenyang Pharmaceutical University, Shenyang 110016, People's Republic of China; Key Laboratory of Intelligent Drug Design and New Drug Discovery of Liaoning Province, Shenyang Pharmaceutical University, Shenyang 110016, People's Republic of China; School of Pharmaceutical Engineering, Shenyang Pharmaceutical University, Shenyang 110016, People's Republic of China.
| | - Maosheng Cheng
- Key Laboratory of Structure-Based Drug Design &Discovery of Ministry of Education, Shenyang Pharmaceutical University, Shenyang 110016, People's Republic of China; Key Laboratory of Intelligent Drug Design and New Drug Discovery of Liaoning Province, Shenyang Pharmaceutical University, Shenyang 110016, People's Republic of China; School of Pharmaceutical Engineering, Shenyang Pharmaceutical University, Shenyang 110016, People's Republic of China.
| |
Collapse
|
4
|
Rozbicki P, Oğuz E, Wolińska E, Türkan F, Cetin A, Branowska D. Synthesis and examination of 1,2,4-triazine-sulfonamide hybrids as potential inhibitory drugs: Inhibition effects on AChE and GST enzymes in silico and in vitro conditions. Arch Pharm (Weinheim) 2024:e2400182. [PMID: 38771105 DOI: 10.1002/ardp.202400182] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/11/2024] [Revised: 04/30/2024] [Accepted: 05/03/2024] [Indexed: 05/22/2024]
Abstract
The crucial functions of acetylcholinesterase (AChE) in neurotransmission and glutathione S-transferase (GST) in detoxification and cellular protection underscore their pivotal roles as key enzymes, essential for maintaining the integrity of neurological and cellular homeostasis. For this purpose, a series of 1,2,4-triazine-sulfonamide hybrids (3a-r) was successfully synthesized, and subsequently evaluated for their inhibitory effects on AChE and GST. The investigation was complemented by molecular docking studies and ADMET (Absorption, Distribution, Metabolism, Excretion, and Toxicity) predictions. The synthesized hybrids demonstrated significant promise in inhibiting both AChE and GST activities. Molecular docking analyses provided insights into the interactions between the compounds and the target enzymes, shedding light on potential binding modes and key amino acid residues involved. Furthermore, the study benefited from ADMET predictions, offering valuable information on the compounds' pharmacokinetic properties and potential toxicity. The promising results obtained from this comprehensive approach highlight the potential of these 1,2,4-triazine-sulfonamide hybrids as effective inhibitors of AChE and GST, paving the way for further development and optimization in the pursuit of novel therapeutic agents.
Collapse
Affiliation(s)
| | - Ercan Oğuz
- Department of Medical Services and Techniques, Health Services Vocational School, Igdır University, Igdır, Turkey
| | - Ewa Wolińska
- Institute of Chemical Sciences, University of Siedlce, Siedlce, Poland
| | - Fikret Türkan
- Department of Basic Sciences, Faculty of Dentistry, Igdır University, Igdır, Turkey
| | - Adnan Cetin
- Department of Chemistry, Faculty of Education, Van Yuzuncu Yil University, Van, Turkey
| | - Danuta Branowska
- Institute of Chemical Sciences, University of Siedlce, Siedlce, Poland
| |
Collapse
|
5
|
Arab I, Egghe K, Laukens K, Chen K, Barakat K, Bittremieux W. Benchmarking of Small Molecule Feature Representations for hERG, Nav1.5, and Cav1.2 Cardiotoxicity Prediction. J Chem Inf Model 2024; 64:2515-2527. [PMID: 37870574 DOI: 10.1021/acs.jcim.3c01301] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/24/2023]
Abstract
In the field of drug discovery, there is a substantial challenge in seeking out chemical structures that possess desirable pharmacological, toxicological, and pharmacokinetic properties. Complications arise when drugs interfere with the functioning of cardiac ion channels, leading to serious cardiovascular consequences. The discontinuation and removal of numerous approved drugs from the market or at late development stages in the pipeline due to such inhibitory effects further highlight the urgency of addressing this issue. Consequently, the early prediction of potential blockers targeting cardiac ion channels during the drug discovery process is of paramount importance. This study introduces a deep learning framework that computationally determines the cardiotoxicity associated with the voltage-gated potassium channel (hERG), the voltage-gated calcium channel (Cav1.2), and the voltage-gated sodium channel (Nav1.5) for drug candidates. The predictive capabilities of three feature representations─molecular fingerprints, descriptors, and graph-based numerical representations─are rigorously benchmarked. Additionally, a novel training and evaluation data set framework is presented, enabling predictive model training of drug off-target cardiotoxicity using a comprehensive and large curated data set covering these three cardiac ion channels. To facilitate these predictions, a robust and comprehensive small molecule cardiotoxicity prediction tool named CToxPred has been developed. It is made available as open source under the permissive MIT license at https://github.com/issararab/CToxPred.
Collapse
Affiliation(s)
- Issar Arab
- Department of Computer Science, University of Antwerp, 2020 Antwerp, Belgium
- Biomedical Informatics Network Antwerpen (Biomina), 2020 Antwerp, Belgium
| | - Kristof Egghe
- Department of Computer Science, University of Antwerp, 2020 Antwerp, Belgium
| | - Kris Laukens
- Department of Computer Science, University of Antwerp, 2020 Antwerp, Belgium
- Biomedical Informatics Network Antwerpen (Biomina), 2020 Antwerp, Belgium
| | - Ke Chen
- Chair for Theoretical Chemistry, Catalysis Research Center, Technische Universität München, Lichtenbergstraße 4, D-85747 Garching, Germany
| | - Khaled Barakat
- Faculty of Pharmacy and Pharmaceutical Sciences, University of Alberta, Edmonton, Alberta 8613, Canada
| | - Wout Bittremieux
- Department of Computer Science, University of Antwerp, 2020 Antwerp, Belgium
- Biomedical Informatics Network Antwerpen (Biomina), 2020 Antwerp, Belgium
| |
Collapse
|
6
|
Chen L, Jiang J, Dou B, Feng H, Liu J, Zhu Y, Zhang B, Zhou T, Wei GW. Machine learning study of the extended drug-target interaction network informed by pain related voltage-gated sodium channels. Pain 2024; 165:908-921. [PMID: 37851391 PMCID: PMC11021136 DOI: 10.1097/j.pain.0000000000003089] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/11/2023] [Accepted: 09/09/2023] [Indexed: 10/19/2023]
Abstract
ABSTRACT Pain is a significant global health issue, and the current treatment options for pain management have limitations in terms of effectiveness, side effects, and potential for addiction. There is a pressing need for improved pain treatments and the development of new drugs. Voltage-gated sodium channels, particularly Nav1.3, Nav1.7, Nav1.8, and Nav1.9, play a crucial role in neuronal excitability and are predominantly expressed in the peripheral nervous system. Targeting these channels may provide a means to treat pain while minimizing central and cardiac adverse effects. In this study, we construct protein-protein interaction (PPI) networks based on pain-related sodium channels and develop a corresponding drug-target interaction network to identify potential lead compounds for pain management. To ensure reliable machine learning predictions, we carefully select 111 inhibitor data sets from a pool of more than 1000 targets in the PPI network. We employ 3 distinct machine learning algorithms combined with advanced natural language processing (NLP)-based embeddings, specifically pretrained transformer and autoencoder representations. Through a systematic screening process, we evaluate the side effects and repurposing potential of more than 150,000 drug candidates targeting Nav1.7 and Nav1.8 sodium channels. In addition, we assess the ADMET (absorption, distribution, metabolism, excretion, and toxicity) properties of these candidates to identify leads with near-optimal characteristics. Our strategy provides an innovative platform for the pharmacological development of pain treatments, offering the potential for improved efficacy and reduced side effects.
Collapse
Affiliation(s)
- Long Chen
- Research Center of Nonlinear Science, School of Mathematical and Physical Sciences, Wuhan Textile University, Wuhan, P R. China
| | - Jian Jiang
- Research Center of Nonlinear Science, School of Mathematical and Physical Sciences, Wuhan Textile University, Wuhan, P R. China
- Department of Mathematics, Michigan State University, East Lansing, MI, United States
| | - Bozheng Dou
- Research Center of Nonlinear Science, School of Mathematical and Physical Sciences, Wuhan Textile University, Wuhan, P R. China
| | - Hongsong Feng
- Department of Mathematics, Michigan State University, East Lansing, MI, United States
| | - Jie Liu
- Research Center of Nonlinear Science, School of Mathematical and Physical Sciences, Wuhan Textile University, Wuhan, P R. China
| | - Yueying Zhu
- Research Center of Nonlinear Science, School of Mathematical and Physical Sciences, Wuhan Textile University, Wuhan, P R. China
| | - Bengong Zhang
- Research Center of Nonlinear Science, School of Mathematical and Physical Sciences, Wuhan Textile University, Wuhan, P R. China
| | - Tianshou Zhou
- Key Laboratory of Computational Mathematics, Guangdong Province, and School of Mathematics, Sun Yat-sen University, Guangzhou, P R. China
| | - Guo-Wei Wei
- Department of Mathematics, Michigan State University, East Lansing, MI, United States
- Department of Electrical and Computer Engineering, Michigan State University, East Lansing, MI, United States
- Department of Biochemistry and Molecular Biology, Michigan State University, East Lansing, MI, United States
| |
Collapse
|
7
|
Lee KH, Won SJ, Oyinloye P, Shi L. Unlocking the Potential of High-Quality Dopamine Transporter Pharmacological Data: Advancing Robust Machine Learning-Based QSAR Modeling. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2024:2024.03.06.583803. [PMID: 38558976 PMCID: PMC10979915 DOI: 10.1101/2024.03.06.583803] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Indexed: 04/04/2024]
Abstract
The dopamine transporter (DAT) plays a critical role in the central nervous system and has been implicated in numerous psychiatric disorders. The ligand-based approaches are instrumental to decipher the structure-activity relationship (SAR) of DAT ligands, especially the quantitative SAR (QSAR) modeling. By gathering and analyzing data from literature and databases, we systematically assemble a diverse range of ligands binding to DAT, aiming to discern the general features of DAT ligands and uncover the chemical space for potential novel DAT ligand scaffolds. The aggregation of DAT pharmacological activity data, particularly from databases like ChEMBL, provides a foundation for constructing robust QSAR models. The compilation and meticulous filtering of these data, establishing high-quality training datasets with specific divisions of pharmacological assays and data types, along with the application of QSAR modeling, prove to be a promising strategy for navigating the pertinent chemical space. Through a systematic comparison of DAT QSAR models using training datasets from various ChEMBL releases, we underscore the positive impact of enhanced data set quality and increased data set size on the predictive power of DAT QSAR models.
Collapse
Affiliation(s)
- Kuo Hao Lee
- Computational Chemistry and Molecular Biophysics Section, Molecular Targets and Medications Discovery Branch, National Institute on Drug Abuse – Intramural Research Program, National Institutes of Health, Baltimore, MD 21224, USA
| | - Sung Joon Won
- Computational Chemistry and Molecular Biophysics Section, Molecular Targets and Medications Discovery Branch, National Institute on Drug Abuse – Intramural Research Program, National Institutes of Health, Baltimore, MD 21224, USA
| | - Precious Oyinloye
- Computational Chemistry and Molecular Biophysics Section, Molecular Targets and Medications Discovery Branch, National Institute on Drug Abuse – Intramural Research Program, National Institutes of Health, Baltimore, MD 21224, USA
| | - Lei Shi
- Computational Chemistry and Molecular Biophysics Section, Molecular Targets and Medications Discovery Branch, National Institute on Drug Abuse – Intramural Research Program, National Institutes of Health, Baltimore, MD 21224, USA
| |
Collapse
|
8
|
Gong Y, Ding W, Wang P, Wu Q, Yao X, Yang Q. Evaluating Machine Learning Methods of Analyzing Multiclass Metabolomics. J Chem Inf Model 2023; 63:7628-7641. [PMID: 38079572 DOI: 10.1021/acs.jcim.3c01525] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/26/2023]
Abstract
Multiclass metabolomic studies have become popular for revealing the differences in multiple stages of complex diseases, various lifestyles, or the effects of specific treatments. In multiclass metabolomics, there are multiple data manipulation steps for analyzing raw data, which consist of data filtering, the imputation of missing values, data normalization, marker identification, sample separation, classification, and so on. In each step, several to dozens of machine learning methods can be chosen for the given data set, with potentially hundreds or thousands of method combinations in the whole data processing chain. Therefore, a clear understanding of these machine learning methods is helpful for selecting an appropriate method combination for obtaining stable and reliable analytical results of specific data. However, there has rarely been an overall introduction or evaluation of these methods based on multiclass metabolomic data. Herein, detailed descriptions of these machine learning methods in multiple data manipulation steps are reviewed. Moreover, an assessment of these methods was performed using a benchmark data set for multiclass metabolomics. First, 12 imputation methods for imputing missing values were evaluated based on the PSS (Procrustes statistical shape analysis) and NRMSE (normalized root-mean-square error) values. Second, 17 normalization methods for processing multiclass metabolomic data were evaluated by applying the PMAD (pooled median absolute deviation) value. Third, different methods of identifying markers of multiclass metabolomics were evaluated based on the CWrel (relative weighted consistency) value. Fourth, nine classification methods for constructing multiclass models were assessed using the AUC (area under the curve) value. Performance evaluations of machine learning methods are highly recommended to select the most appropriate method combination before performing the final analysis of the given data. Overall, detailed descriptions and evaluation of various machine learning methods are expected to improve analyses of multiclass metabolomic data.
Collapse
Affiliation(s)
- Yaguo Gong
- State Key Laboratory of Quality Research in Chinese Medicine, School of Pharmacy, Macau University of Science and Technology, Macau 999078, China
| | - Wei Ding
- State Key Laboratory of Quality Research in Chinese Medicine, School of Pharmacy, Macau University of Science and Technology, Macau 999078, China
| | - Panpan Wang
- College of Chemistry and Pharmaceutical Engineering, Huanghuai University, Zhumadian 463000, China
| | - Qibiao Wu
- State Key Laboratory of Quality Research in Chinese Medicine, School of Pharmacy, Macau University of Science and Technology, Macau 999078, China
| | - Xiaojun Yao
- Centre for Artificial Intelligence Driven Drug Discovery, Faculty of Applied Sciences, Macao Polytechnic University, Macao 999078, China
| | - Qingxia Yang
- Zhejiang Provincial Key Laboratory of Precision Diagnosis and Therapy for Major Gynecological Diseases, Women's Hospital, Zhejiang University School of Medicine, Hangzhou 310058, China
- Department of Bioinformatics, School of Geographic and Biologic Information, Nanjing University of Posts and Telecommunications, Nanjing 210023, China
| |
Collapse
|
9
|
Lv Q, Zhou F, Liu X, Zhi L. Artificial intelligence in small molecule drug discovery from 2018 to 2023: Does it really work? Bioorg Chem 2023; 141:106894. [PMID: 37776682 DOI: 10.1016/j.bioorg.2023.106894] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/15/2023] [Revised: 09/24/2023] [Accepted: 09/25/2023] [Indexed: 10/02/2023]
Abstract
Utilizing artificial intelligence (AI) in drug design represents an advanced approach for identifying targets and developing new drugs. Integrating AI techniques significantly reduces the workload involved in drug development and enhances the efficiency of early-stage drug discovery. This review aims to present a comprehensive overview of the utilization of AI methods in the field of small drug design, with a specific focus on four key areas: protein structure prediction, molecular virtual screening, molecular design, and absorption, distribution, metabolism, excretion, and toxicity (ADMET) prediction. Additionally, the role and limitations of AI in drug development are explored, and the impact of AI on decision-making processes is studied. It is important to note that while AI can bring numerous benefits to the early stage of drug development, the direction and quality of decision-making should still be emphasized, as AI should be considered as a tool rather than a decisive factor.
Collapse
Affiliation(s)
- Qi Lv
- School of Pharmacy, Inflammation and Immune Mediated Diseases Laboratory of Anhui Province, Hefei 230032, PR China
| | - Feilong Zhou
- School of Pharmacy, Inflammation and Immune Mediated Diseases Laboratory of Anhui Province, Hefei 230032, PR China
| | - Xinhua Liu
- School of Pharmacy, Inflammation and Immune Mediated Diseases Laboratory of Anhui Province, Hefei 230032, PR China.
| | - Liping Zhi
- School of Health Management, Anhui Medical University Hefei, 230032, PR China.
| |
Collapse
|
10
|
Hancox JC, Copeland CS, Harmer SC, Henderson G. New synthetic cannabinoids and the potential for cardiac arrhythmia risk. JOURNAL OF MOLECULAR AND CELLULAR CARDIOLOGY PLUS 2023; 6:100049. [PMID: 38143960 PMCID: PMC10739592 DOI: 10.1016/j.jmccpl.2023.100049] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 07/31/2023] [Revised: 09/14/2023] [Accepted: 09/14/2023] [Indexed: 12/26/2023]
Abstract
Synthetic cannabinoid receptor agonists (SCRAs) have been associated with QT interval prolongation. Limited preclinical information on SCRA effects on cardiac electrogenesis results from the rapid emergence of new compounds and restricted research availability. We used two machine-learning-based tools to evaluate seven novel SCRAs' interaction potential with the hERG potassium channel, an important drug antitarget. Five SCRAs were predicted to have the ability to block the hERG channel by both prediction tools; ADB-FUBIATA was predicted to be a strong hERG blocker. ADB-5Br-INACA and ADB-4en-PINACA showed varied predictions. These findings highlight potentially proarrhythmic hERG block by novel SCRAs, necessitating detailed safety evaluations.
Collapse
Affiliation(s)
- Jules C. Hancox
- School of Physiology, Pharmacology and Neuroscience, Biomedical Sciences Building, University Walk, Bristol BS8 1TD, UK
| | - Caroline S. Copeland
- Institute of Pharmaceutical Science, King's College London, UK
- Centre for Pharmaceutical Medicine Research, King's College London, UK
| | - Stephen C. Harmer
- School of Physiology, Pharmacology and Neuroscience, Biomedical Sciences Building, University Walk, Bristol BS8 1TD, UK
| | - Graeme Henderson
- School of Physiology, Pharmacology and Neuroscience, Biomedical Sciences Building, University Walk, Bristol BS8 1TD, UK
| |
Collapse
|
11
|
Haddad S, Oktay L, Erol I, Şahin K, Durdagi S. Utilizing Heteroatom Types and Numbers from Extensive Ligand Libraries to Develop Novel hERG Blocker QSAR Models Using Machine Learning-Based Classifiers. ACS OMEGA 2023; 8:40864-40877. [PMID: 37929100 PMCID: PMC10620895 DOI: 10.1021/acsomega.3c06074] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 08/16/2023] [Accepted: 09/13/2023] [Indexed: 11/07/2023]
Abstract
The human ether-à-go-go-related gene (hERG) channel plays a crucial role in membrane repolarization. Any disruptions in its function can lead to severe cardiovascular disorders such as long QT syndrome (LQTS), which increases the risk of serious cardiovascular problems such as tachyarrhythmia and sudden cardiac death. Drug-induced LQTS is a significant concern and has resulted in drug withdrawals from the market in the past. The main objective of this study is to pinpoint crucial heteroatoms present in ligands that initiate interactions leading to the effective blocking of the hERG channel. To achieve this aim, ligand-based quantitative structure-activity relationships (QSAR) models were constructed using extensive ligand libraries, considering the heteroatom types and numbers, and their associated hERG channel blockage pIC50 values. Machine learning-assisted QSAR models were developed to analyze the key structural components influencing compound activity. Among the various methods, the KPLS method proved to be the most efficient, allowing the construction of models based on eight distinct fingerprints. The study delved into investigating the influence of heteroatoms on the activity of hERG blockers, revealing their significant role. Furthermore, by quantifying the effect of heteroatom types and numbers on ligand activity at the hERG channel, six compound pairs were selected for molecular docking. Subsequent molecular dynamics simulations and per residue MM/GBSA calculations were performed to comprehensively analyze the interactions of the selected pair compounds.
Collapse
Affiliation(s)
- Safa Haddad
- Computational
Biology and Molecular Simulations Laboratory, Department of Biophysics,
School of Medicine, Bahçeşehir
University, Istanbul 34353, Turkey
- Computational
Drug Design Center (HITMER), Bahçeşehir
University, Istanbul 34353, Turkey
| | - Lalehan Oktay
- Computational
Biology and Molecular Simulations Laboratory, Department of Biophysics,
School of Medicine, Bahçeşehir
University, Istanbul 34353, Turkey
- Computational
Drug Design Center (HITMER), Bahçeşehir
University, Istanbul 34353, Turkey
| | - Ismail Erol
- Computational
Biology and Molecular Simulations Laboratory, Department of Biophysics,
School of Medicine, Bahçeşehir
University, Istanbul 34353, Turkey
- Computational
Drug Design Center (HITMER), Bahçeşehir
University, Istanbul 34353, Turkey
| | - Kader Şahin
- Department
of Analytical Chemistry, School of Pharmacy, Bahçeşehir University, Istanbul 34734, Turkey
| | - Serdar Durdagi
- Computational
Biology and Molecular Simulations Laboratory, Department of Biophysics,
School of Medicine, Bahçeşehir
University, Istanbul 34353, Turkey
- Computational
Drug Design Center (HITMER), Bahçeşehir
University, Istanbul 34353, Turkey
- Molecular
Therapy Lab, Department of Pharmaceutical Chemistry, School of Pharmacy, Bahçeşehir University, Istanbul 34353, Turkey
| |
Collapse
|
12
|
Katebi M, Rahgozar S, Kazemi F, Rahmani S, Najafi Dorcheh S. GingerenoneA overcomes dexamethasone resistance by activating apoptosis and inhibiting cell proliferation in pediatric T-ALL cells. Cancer Sci 2023; 114:3984-3995. [PMID: 37619556 PMCID: PMC10551595 DOI: 10.1111/cas.15936] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/28/2023] [Revised: 08/01/2023] [Accepted: 08/10/2023] [Indexed: 08/26/2023] Open
Abstract
Plant-based combination strategies have been widely considered in cancer therapy to attenuate chemotherapeutics side effects. The anti-leukemic effect of the whole ginger extract was previously portrayed by our team, and the current study is centered around the cytotoxicity and mechanism of action of a phenolic subsidiary of ginger, GingerenoneA, on pediatric acute lymphoblastic leukemia. GingernoneA imposed, dose-dependently, inhibitory effects on the viability of T and B leukemia cell lines confirmed by MTT assays. Resistance to Dexamethasone, a mostly used chemotherapeutic in acute lymphoblastic leukemia treatments, was overcome by GingernoneA. A synergistic effect of Dexamethasone and GingrenoneA on T leukemia cell lines and patient primary cells was confirmed. Annexin-V/PI and acridine orange/ethidium bromide staining illustrated dose-dependent apoptosis in CCRF-CEM cells developed by GingerenoneA. The intrinsic and extrinsic apoptosis induction and antiproliferative attribution of GingerenoneA were validated by western blot and qPCR. Despite the supposed loss of function in CCRF-CEM cells, TP53 showed increased expression levels and functional activity upon treatment with GingernoneA. Bioinformatic studies revealed the conceivable impact of GingerenoneA on the reactivity of mutant P53 through its binding to Cys124. Our findings may provide novel strategies for therapeutic intervention to ameliorate pALL outcomes.
Collapse
Affiliation(s)
- Melika Katebi
- Department of Cell and Molecular Biology & Microbiology, Faculty of Biological Science and TechnologyUniversity of IsfahanIran
| | - Soheila Rahgozar
- Department of Cell and Molecular Biology & Microbiology, Faculty of Biological Science and TechnologyUniversity of IsfahanIran
| | - Farnoosh Kazemi
- Department of Cell and Molecular Biology & Microbiology, Faculty of Biological Science and TechnologyUniversity of IsfahanIran
| | - Saeideh Rahmani
- Department of Cell and Molecular Biology & Microbiology, Faculty of Biological Science and TechnologyUniversity of IsfahanIran
| | - Somayeh Najafi Dorcheh
- Department of Cell and Molecular Biology & Microbiology, Faculty of Biological Science and TechnologyUniversity of IsfahanIran
| |
Collapse
|
13
|
Lee M, Min K. AmorProt: Amino Acid Molecular Fingerprints Repurposing-Based Protein Fingerprint. Biochemistry 2023; 62:2700-2709. [PMID: 37622182 DOI: 10.1021/acs.biochem.3c00253] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 08/26/2023]
Abstract
As protein therapeutics play an important role in almost all medical fields, numerous studies have been conducted on proteins using artificial intelligence. Artificial intelligence has enabled data-driven predictions without the need for expensive experiments. Nevertheless, unlike the various molecular fingerprint algorithms that have been developed, protein fingerprint algorithms have rarely been studied. In this study, we proposed the amino acid molecular fingerprints repurposing-based protein (AmorProt) fingerprint, a protein sequence representation method that effectively uses the molecular fingerprints corresponding to 20 amino acids. Subsequently, the performances of the tree-based machine learning and artificial neural network models were compared using (1) amyloid classification and (2) isoelectric point regression. Finally, the applicability and advantages of the developed platform were demonstrated through a case study and the following experiments: (3) comparison of dataset dependence with feature-based methods, (4) feature importance analysis, and (5) protein space analysis. Consequently, the significantly improved model performance and data-set-independent versatility of the AmorProt fingerprint were verified. The results revealed that the current protein representation method can be applied to various fields related to proteins, such as predicting their fundamental properties or interaction with ligands.
Collapse
Affiliation(s)
- Myeonghun Lee
- School of Systems Biomedical Science, Soongsil University, 369 Sangdo-ro, Dongjak-gu, Seoul 06978, Republic of Korea
| | - Kyoungmin Min
- School of Mechanical Engineering, Soongsil University, 369 Sangdo-ro, Dongjak-gu, Seoul 06978, Republic of Korea
| |
Collapse
|
14
|
Wang B, Guo J, Liu X, Yu Y, Wu J, Wang Y. Prediction of the effects of small molecules on the gut microbiome using machine learning method integrating with optimal molecular features. BMC Bioinformatics 2023; 24:338. [PMID: 37697256 PMCID: PMC10496404 DOI: 10.1186/s12859-023-05455-1] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/10/2023] [Accepted: 08/25/2023] [Indexed: 09/13/2023] Open
Abstract
BACKGROUND The human gut microbiome (HGM), consisting of trillions of microorganisms, is crucial to human health. Adverse drug use is one of the most important causes of HGM disorder. Thus, it is necessary to identify drugs or compounds with anti-commensal effects on HGM in the early drug discovery stage. This study proposes a novel anti-commensal effects classification using a machine learning method and optimal molecular features. To improve the prediction performance, we explored combinations of six fingerprints and three descriptors to filter the best characterization as molecular features. RESULTS The final consensus model based on optimal features yielded the F1-score of 0.725 ± 0.014, ACC of 82.9 ± 0.7%, and AUC of 0.791 ± 0.009 for five-fold cross-validation. In addition, this novel model outperformed the prior studies by using the same algorithm. Furthermore, the important chemical descriptors and misclassified anti-commensal compounds are analyzed to better understand and interpret the model. Finally, seven structural alerts responsible for the chemical anti-commensal effect are identified, implying valuable information for drug design. CONCLUSION Our study would be a promising tool for screening anti-commensal compounds in the early stage of drug discovery and assessing the potential risks of these drugs in vivo.
Collapse
Affiliation(s)
- Binyou Wang
- School of Basic Medical Sciences, Southwest Medical University, Luzhou, 646000, China
- School of Pharmacy, Southwest Medical University, Luzhou, 646000, China
| | - Jianmin Guo
- School of Basic Medical Sciences, Southwest Medical University, Luzhou, 646000, China
| | - Xiaofeng Liu
- School of Basic Medical Sciences, Southwest Medical University, Luzhou, 646000, China
| | - Yang Yu
- School of Basic Medical Sciences, Southwest Medical University, Luzhou, 646000, China
- Key Laboratory of Medical Electrophysiology, Ministry of Education and Medical Electrophysiological Key Laboratory of Sichuan Province, Institute of Cardiovascular Research, Southwest Medical University, Luzhou, 646000, China
| | - Jianming Wu
- School of Basic Medical Sciences, Southwest Medical University, Luzhou, 646000, China.
- School of Pharmacy, Southwest Medical University, Luzhou, 646000, China.
- Key Laboratory of Medical Electrophysiology, Ministry of Education and Medical Electrophysiological Key Laboratory of Sichuan Province, Institute of Cardiovascular Research, Southwest Medical University, Luzhou, 646000, China.
- Sichuan Key Medical Laboratory of New Drug Discovery and Druggability Evaluation, Luzhou Key Laboratory of Activity Screening and Druggability Evaluation for Chinese Materia Medica, School of Pharmacy, Southwest Medical University, Luzhou, 646000, China.
| | - Yiwei Wang
- School of Basic Medical Sciences, Southwest Medical University, Luzhou, 646000, China.
- School of Pharmacy, Southwest Medical University, Luzhou, 646000, China.
- Key Laboratory of Medical Electrophysiology, Ministry of Education and Medical Electrophysiological Key Laboratory of Sichuan Province, Institute of Cardiovascular Research, Southwest Medical University, Luzhou, 646000, China.
| |
Collapse
|
15
|
El Harchi A, Hancox JC. hERG agonists pose challenges to web-based machine learning methods for prediction of drug-hERG channel interaction. J Pharmacol Toxicol Methods 2023; 123:107293. [PMID: 37468081 DOI: 10.1016/j.vascn.2023.107293] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/07/2023] [Revised: 05/23/2023] [Accepted: 07/12/2023] [Indexed: 07/21/2023]
Abstract
Pharmacological blockade of the IKr channel (hERG) by diverse drugs in clinical use is associated with the Long QT Syndrome that can lead to life threatening arrhythmia. Various computational tools including machine learning models (MLM) for the prediction of hERG inhibition have been developed to facilitate the throughput screening of drugs in development and optimise thus the prediction of hERG liabilities. The use of MLM relies on large libraries of training compounds for the quantitative structure-activity relationship (QSAR) modelling of hERG inhibition. The focus on inhibition omits potential effects of hERG channel agonist molecules and their associated QT shortening risk. It is instructive, therefore, to consider how known hERG agonists are handled by MLM. Here, two highly developed online computational tools for the prediction of hERG liability, Pred-hERG and HergSPred were probed for their ability to detect hERG activator drug molecules as hERG interactors. In total, 73 hERG blockers were tested with both computational tools giving overall good predictions for hERG blockers with reported IC50s below Pred-hERG and HergSPred cut-off threshold for hERG inhibition. However, for compounds with reported IC50s above this threshold such as disopyramide or sotalol discrepancies were observed. HergSPred identified all 20 hERG agonists selected as interacting with the hERG channel. Further studies are warranted to improve online MLM prediction of hERG related cardiotoxicity, by explicitly taking into account channel agonism as well as inhibition.
Collapse
Affiliation(s)
- Aziza El Harchi
- School of Physiology and Pharmacology and Neuroscience, Biomedical Sciences Building, The University of Bristol, University Walk, Bristol BS8 1TD, UK.
| | - Jules C Hancox
- School of Physiology and Pharmacology and Neuroscience, Biomedical Sciences Building, The University of Bristol, University Walk, Bristol BS8 1TD, UK
| |
Collapse
|
16
|
Venkatraman V. FP-MAP: an extensive library of fingerprint-based molecular activity prediction tools. Front Chem 2023; 11:1239467. [PMID: 37649967 PMCID: PMC10462816 DOI: 10.3389/fchem.2023.1239467] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/13/2023] [Accepted: 07/31/2023] [Indexed: 09/01/2023] Open
Abstract
Discovering new drugs for disease treatment is challenging, requiring a multidisciplinary effort as well as time, and resources. With a view to improving hit discovery and lead compound identification, machine learning (ML) approaches are being increasingly used in the decision-making process. Although a number of ML-based studies have been published, most studies only report fragments of the wider range of bioactivities wherein each model typically focuses on a particular disease. This study introduces FP-MAP, an extensive atlas of fingerprint-based prediction models that covers a diverse range of activities including neglected tropical diseases (caused by viral, bacterial and parasitic pathogens) as well as other targets implicated in diseases such as Alzheimer's. To arrive at the best predictive models, performance of ≈4,000 classification/regression models were evaluated on different bioactivity data sets using 12 different molecular fingerprints. The best performing models that achieved test set AUC values of 0.62-0.99 have been integrated into an easy-to-use graphical user interface that can be downloaded from https://gitlab.com/vishsoft/fpmap.
Collapse
Affiliation(s)
- Vishwesh Venkatraman
- Department of Chemistry, Norwegian University of Science and Technology, Trondheim, Norway
| |
Collapse
|
17
|
Wang H, Zhu G, Izu LT, Chen-Izu Y, Ono N, Altaf-Ul-Amin MD, Kanaya S, Huang M. On QSAR-based cardiotoxicity modeling with the expressiveness-enhanced graph learning model and dual-threshold scheme. Front Physiol 2023; 14:1156286. [PMID: 37228825 PMCID: PMC10203956 DOI: 10.3389/fphys.2023.1156286] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/01/2023] [Accepted: 04/05/2023] [Indexed: 05/27/2023] Open
Abstract
Introduction: Given the direct association with malignant ventricular arrhythmias, cardiotoxicity is a major concern in drug design. In the past decades, computational models based on the quantitative structure-activity relationship have been proposed to screen out cardiotoxic compounds and have shown promising results. The combination of molecular fingerprint and the machine learning model shows stable performance for a wide spectrum of problems; however, not long after the advent of the graph neural network (GNN) deep learning model and its variant (e.g., graph transformer), it has become the principal way of quantitative structure-activity relationship-based modeling for its high flexibility in feature extraction and decision rule generation. Despite all these progresses, the expressiveness (the ability of a program to identify non-isomorphic graph structures) of the GNN model is bounded by the WL isomorphism test, and a suitable thresholding scheme that relates directly to the sensitivity and credibility of a model is still an open question. Methods: In this research, we further improved the expressiveness of the GNN model by introducing the substructure-aware bias by the graph subgraph transformer network model. Moreover, to propose the most appropriate thresholding scheme, a comprehensive comparison of the thresholding schemes was conducted. Results: Based on these improvements, the best model attains performance with 90.4% precision, 90.4% recall, and 90.5% F1-score with a dual-threshold scheme (active: < 1 μ M ; non-active: > 30 μ M ). The improved pipeline (graph subgraph transformer network model and thresholding scheme) also shows its advantages in terms of the activity cliff problem and model interpretability.
Collapse
Affiliation(s)
- Huijia Wang
- Graduate School of Science and Technology, Nara Institute of Science and Technology, Ikoma, Japan
| | - Guangxian Zhu
- Graduate School of Science and Technology, Nara Institute of Science and Technology, Ikoma, Japan
| | - Leighton T. Izu
- Department of Pharmacology, University of California, Davis, CA, United States
| | - Ye Chen-Izu
- Department of Biomedical Engineering, University of California, Davis, CA, United States
| | - Naoaki Ono
- Data Science Center, Nara Institute of Science and Technology, Ikoma, Japan
| | - MD Altaf-Ul-Amin
- Graduate School of Science and Technology, Nara Institute of Science and Technology, Ikoma, Japan
| | - Shigehiko Kanaya
- Graduate School of Science and Technology, Nara Institute of Science and Technology, Ikoma, Japan
| | - Ming Huang
- Graduate School of Science and Technology, Nara Institute of Science and Technology, Ikoma, Japan
| |
Collapse
|
18
|
Feng H, Jiang J, Wei GW. Machine-learning repurposing of DrugBank compounds for opioid use disorder. Comput Biol Med 2023; 160:106921. [PMID: 37178605 DOI: 10.1016/j.compbiomed.2023.106921] [Citation(s) in RCA: 6] [Impact Index Per Article: 6.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/28/2023] [Revised: 03/30/2023] [Accepted: 04/13/2023] [Indexed: 05/15/2023]
Abstract
Opioid use disorder (OUD) is a chronic and relapsing condition that involves the continued and compulsive use of opioids despite harmful consequences. The development of medications with improved efficacy and safety profiles for OUD treatment is urgently needed. Drug repurposing is a promising option for drug discovery due to its reduced cost and expedited approval procedures. Computational approaches based on machine learning enable the rapid screening of DrugBank compounds, identifying those with the potential to be repurposed for OUD treatment. We collected inhibitor data for four major opioid receptors and used advanced machine learning predictors of binding affinity that fuse the gradient boosting decision tree algorithm with two natural language processing (NLP)-based molecular fingerprints and one traditional 2D fingerprint. Using these predictors, we systematically analyzed the binding affinities of DrugBank compounds on four opioid receptors. Based on our machine learning predictions, we were able to discriminate DrugBank compounds with various binding affinity thresholds and selectivities for different receptors. The prediction results were further analyzed for ADMET (absorption, distribution, metabolism, excretion, and toxicity), which provided guidance on repurposing DrugBank compounds for the inhibition of selected opioid receptors. The pharmacological effects of these compounds for OUD treatment need to be tested in further experimental studies and clinical trials. Our machine learning studies provide a valuable platform for drug discovery in the context of OUD treatment.
Collapse
Affiliation(s)
- Hongsong Feng
- Department of Mathematics, Michigan State University, MI 48824, USA
| | - Jian Jiang
- Department of Mathematics, Michigan State University, MI 48824, USA; Research Center of Nonlinear Science, School of Mathematical and Physical Sciences, Wuhan Textile University, Wuhan, 430200, PR China
| | - Guo-Wei Wei
- Department of Mathematics, Michigan State University, MI 48824, USA; Department of Electrical and Computer Engineering, Michigan State University, MI 48824, USA; Department of Biochemistry and Molecular Biology, Michigan State University, MI 48824, USA.
| |
Collapse
|
19
|
Tran TTV, Surya Wibowo A, Tayara H, Chong KT. Artificial Intelligence in Drug Toxicity Prediction: Recent Advances, Challenges, and Future Perspectives. J Chem Inf Model 2023; 63:2628-2643. [PMID: 37125780 DOI: 10.1021/acs.jcim.3c00200] [Citation(s) in RCA: 9] [Impact Index Per Article: 9.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 05/02/2023]
Abstract
Toxicity prediction is a critical step in the drug discovery process that helps identify and prioritize compounds with the greatest potential for safe and effective use in humans, while also reducing the risk of costly late-stage failures. It is estimated that over 30% of drug candidates are discarded owing to toxicity. Recently, artificial intelligence (AI) has been used to improve drug toxicity prediction as it provides more accurate and efficient methods for identifying the potentially toxic effects of new compounds before they are tested in human clinical trials, thus saving time and money. In this review, we present an overview of recent advances in AI-based drug toxicity prediction, including the use of various machine learning algorithms and deep learning architectures, of six major toxicity properties and Tox21 assay end points. Additionally, we provide a list of public data sources and useful toxicity prediction tools for the research community and highlight the challenges that must be addressed to enhance model performance. Finally, we discuss future perspectives for AI-based drug toxicity prediction. This review can aid researchers in understanding toxicity prediction and pave the way for new methods of drug discovery.
Collapse
Affiliation(s)
- Thi Tuyet Van Tran
- Department of Electronics and Information Engineering, Jeonbuk National University, Jeonju 54896, Republic of Korea
- Faculty of Information Technology, An Giang University, Long Xuyen 880000, Vietnam
- Vietnam National University - Ho Chi Minh City, Ho Chi Minh 700000, Vietnam
| | - Agung Surya Wibowo
- Department of Electronics and Information Engineering, Jeonbuk National University, Jeonju 54896, Republic of Korea
- Department of Electrical Engineering, Telkom University, Bandung 40257, Indonesia
| | - Hilal Tayara
- School of International Engineering and Science, Jeonbuk National University, Jeonju 54896, Republic of Korea
| | - Kil To Chong
- Advances Electronics and Information Research Center, Jeonbuk National University, Jeonju 54896, Republic of Korea
| |
Collapse
|
20
|
Application of Chiral Piperidine Scaffolds in Drug Design. PHARMACEUTICAL FRONTS 2023. [DOI: 10.1055/s-0043-1764218] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 03/17/2023] Open
Abstract
Chiral piperidine scaffolds are prevalent as the common cores of a large number of active pharmaceuticals in medical chemistry. This review outlined the diversity of chiral piperidine scaffolds in recently approved drugs, and also covers the scaffolds in leads and drug candidates. The significance of chiral piperidine scaffolds in drug design is also discussed in this article. With the introduction of chiral piperidine scaffolds into small molecules, the exploration of drug-like molecules can be benefitted from the following aspect: (1) modulating the physicochemical properties; (2) enhancing the biological activities and selectivity; (3) improving pharmacokinetic properties; and (4) reducing the cardiac hERG toxicity. Given above, chiral piperidine-based discovery of small molecules will be a promising strategy to enrich our molecules' library to fight against diseases.
Collapse
|
21
|
Li M, Zeng M, Zhang H, Chen H, Guan L. Biological Activity Predictions of Ligands Based on Hybrid Molecular Fingerprinting and Ensemble Learning. ACS OMEGA 2023; 8:5561-5570. [PMID: 36816680 PMCID: PMC9933080 DOI: 10.1021/acsomega.2c06944] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 10/27/2022] [Accepted: 12/23/2022] [Indexed: 06/18/2023]
Abstract
The biological activity predictions of ligands are an important research direction, which can improve the efficiency and success probability of drug screening. However, the traditional prediction method has the disadvantages of complex modeling and low screening efficiency. Machine learning is considered an important research direction to solve these traditional method problems in the near future. This paper proposes a machine learning model with high predictive accuracy and stable prediction ability, namely, the back propagation neural network cross-support vector regression model (BPCSVR). By comparing multiple molecular descriptors, MACCS fingerprint and ECFP6 fingerprint were selected as inputs, and the stable prediction ability of the model was improved by integrating multiple models and correcting similar samples. We used leave-one-out cross-validation on 3038 samples from six data sets. The coefficient of determination, root mean square error, and absolute error were used as the evaluation parameters. After comparing the multiclass models, the results show that the BPCSVR model has stable prediction ability in different data sets, and the prediction accuracy is higher than other comparison models.
Collapse
|
22
|
Feng H, Wei GW. Virtual screening of DrugBank database for hERG blockers using topological Laplacian-assisted AI models. Comput Biol Med 2023; 153:106491. [PMID: 36599209 PMCID: PMC10120853 DOI: 10.1016/j.compbiomed.2022.106491] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/17/2022] [Revised: 11/29/2022] [Accepted: 12/27/2022] [Indexed: 12/29/2022]
Abstract
The human ether-a-go-go (hERG) potassium channel (Kv11.1) plays a critical role in mediating cardiac action potential. The blockade of this ion channel can potentially lead fatal disorder and/or long QT syndrome. Many drugs have been withdrawn because of their serious hERG-cardiotoxicity. It is crucial to assess the hERG blockade activity in the early stage of drug discovery. We are particularly interested in the hERG-cardiotoxicity of compounds collected in the DrugBank database considering that many DrugBank compounds have been approved for therapeutic treatments or have high potential to become drugs. Machine learning-based in silico tools offer a rapid and economical platform to virtually screen DrugBank compounds. We design accurate and robust classifiers for blockers/non-blockers and then build regressors to quantitatively analyze the binding potency of the DrugBank compounds on the hERG channel. Molecular sequences are embedded with two natural language processing (NLP) methods, namely, autoencoder and transformer. Complementary three-dimensional (3D) molecular structures are embedded with two advanced mathematical approaches, i.e., topological Laplacians and algebraic graphs. With our state-of-the-art tools, we reveal that 227 out of the 8641 DrugBank compounds are potential hERG blockers, suggesting serious drug safety problems. Our predictions provide guidance for the further experimental interrogation of DrugBank compounds' hERG-cardiotoxicity.
Collapse
Affiliation(s)
- Hongsong Feng
- Department of Mathematics, Michigan State University, MI 48824, USA
| | - Guo-Wei Wei
- Department of Mathematics, Michigan State University, MI 48824, USA; Department of Electrical and Computer Engineering, Michigan State University, MI 48824, USA; Department of Biochemistry and Molecular Biology, Michigan State University, MI 48824, USA.
| |
Collapse
|
23
|
Vittorio S, Lunghini F, Pedretti A, Vistoli G, Beccari AR. Ensemble of structure and ligand-based classification models for hERG liability profiling. Front Pharmacol 2023; 14:1148670. [PMID: 37033661 PMCID: PMC10076575 DOI: 10.3389/fphar.2023.1148670] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/23/2023] [Accepted: 03/13/2023] [Indexed: 04/11/2023] Open
Abstract
Drug-induced cardiotoxicity represents one of the most critical safety concerns in the early stages of drug development. The blockade of the human ether-à-go-go-related potassium channel (hERG) is the most frequent cause of cardiotoxicity, as it is associated to long QT syndrome which can lead to fatal arrhythmias. Therefore, assessing hERG liability of new drugs candidates is crucial to avoid undesired cardiotoxic effects. In this scenario, computational approaches have emerged as useful tools for the development of predictive models able to identify potential hERG blockers. In the last years, several efforts have been addressed to generate ligand-based (LB) models due to the lack of experimental structural information about hERG channel. However, these methods rely on the structural features of the molecules used to generate the model and often fail in correctly predicting new chemical scaffolds. Recently, the 3D structure of hERG channel has been experimentally solved enabling the use of structure-based (SB) strategies which may overcome the limitations of the LB approaches. In this study, we compared the performances achieved by both LB and SB classifiers for hERG-related cardiotoxicity developed by using Random Forest algorithm and employing a training set containing 12789 hERG binders. The SB models were trained on a set of scoring functions computed by docking and rescoring calculations, while the LB classifiers were built on a set of physicochemical descriptors and fingerprints. Furthermore, models combining the LB and SB features were developed as well. All the generated models were internally validated by ten-fold cross-validation on the TS and further verified on an external test set. The former revealed that the best performance was achieved by the LB model, while the model combining the LB and the SB attributes displayed the best results when applied on the external test set highlighting the usefulness of the integration of LB and SB features in correctly predicting unseen molecules. Overall, our predictive models showed satisfactory performances providing new useful tools to filter out potential cardiotoxic drug candidates in the early phase of drug discovery.
Collapse
Affiliation(s)
- Serena Vittorio
- Dipartimento di Scienze Farmaceutiche, Università Degli Studi di Milano, Milano, Italy
| | | | - Alessandro Pedretti
- Dipartimento di Scienze Farmaceutiche, Università Degli Studi di Milano, Milano, Italy
| | - Giulio Vistoli
- Dipartimento di Scienze Farmaceutiche, Università Degli Studi di Milano, Milano, Italy
| | - Andrea R. Beccari
- EXSCALATE, Dompé Farmaceutici SpA, Napoli, Italy
- *Correspondence: Andrea R. Beccari,
| |
Collapse
|
24
|
Eisenberg B. Setting Boundaries for Statistical Mechanics. MOLECULES (BASEL, SWITZERLAND) 2022; 27:molecules27228017. [PMID: 36432117 PMCID: PMC9696510 DOI: 10.3390/molecules27228017] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 10/03/2022] [Revised: 10/21/2022] [Accepted: 11/08/2022] [Indexed: 11/22/2022]
Abstract
Statistical mechanics has grown without bounds in space. Statistical mechanics of noninteracting point particles in an unbounded perfect gas is widely used to describe liquids like concentrated salt solutions of life and electrochemical technology, including batteries. Liquids are filled with interacting molecules. A perfect gas is a poor model of a liquid. Statistical mechanics without spatial bounds is impossible as well as imperfect, if molecules interact as charged particles, as nearly all atoms do. The behavior of charged particles is not defined until boundary structures and values are defined because charges are governed by Maxwell's partial differential equations. Partial differential equations require boundary structures and conditions. Boundary conditions cannot be defined uniquely 'at infinity' because the limiting process that defines 'infinity' includes such a wide variety of structures and behaviors, from elongated ellipses to circles, from light waves that never decay, to dipolar fields that decay steeply, to Coulomb fields that hardly decay at all. Boundaries and boundary conditions needed to describe matter are not prominent in classical statistical mechanics. Statistical mechanics of bounded systems is described in the EnVarA system of variational mechanics developed by Chun Liu, more than anyone else. EnVarA treatment does not yet include Maxwell equations.
Collapse
Affiliation(s)
- Bob Eisenberg
- Department of Applied Mathematics, Illinois Institute of Technology, Chicago, IL 60616, USA;
- Department of Physiology and Biophysics, Rush University Medical Center, Chicago, IL 60612, USA
| |
Collapse
|
25
|
He H, Zhang Y, Xu J, Li Y, Fang H, Liu Y, Zhang S. Discovery of Orally Bioavailable SOS1 Inhibitors for Suppressing KRAS-Driven Carcinoma. J Med Chem 2022; 65:13158-13171. [PMID: 36173339 DOI: 10.1021/acs.jmedchem.2c00986] [Citation(s) in RCA: 11] [Impact Index Per Article: 5.5] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/15/2023]
Abstract
The interaction between son of sevenless 1 (SOS1) gene and Kirsten rat sarcoma viral oncogene (KRAS) is crucial for activating signals of proliferation and survival in a range of cancers. We previously discovered compound 40a with a tetracyclic quinazoline pharmacophore as a potent orally bioavailable SOS1 inhibitor. Herein, we disclosed the discovery of compound 13c, which substituted the third ring with the seven-membered ring, as a clinical drug candidate for suppressing KRAS-driven tumors. 13c strongly disrupted the protein-protein interaction between SOS1 and KRAS with low IC50 values of 3.9 nM (biochemical) and 21 nM (cellular). 13c showed a favorable pharmacokinetic profile with a bioavailability of 86.8% in beagles and exhibited 83.0% tumor suppression in Mia-paca-2 pancreas xenograft mice tumor models. 13c exhibited a weak time-dependent CY3A4P inhibition than BI-3406, thereby reducing the risk of drug-drug interaction in drug combination. Toxicological investigations revealed that 13c had a lower risk of sudden cardiac death than BI-3406. Overall, 13c has been under evaluation in preclinical trials.
Collapse
Affiliation(s)
- Huan He
- Key Laboratory of Coal Conversion and New Carbon Materials of Hubei Province, College of Chemistry and Chemical Engineering, Institute of Advanced Materials and Nanotechnology, Wuhan University of Science and Technology, Wuhan 430081, P. R. China
- Wuhan Yuxiang Pharmaceutical Technology Co., Ltd., Wuhan 430200, P. R. China
| | - Yu Zhang
- Key Laboratory of Coal Conversion and New Carbon Materials of Hubei Province, College of Chemistry and Chemical Engineering, Institute of Advanced Materials and Nanotechnology, Wuhan University of Science and Technology, Wuhan 430081, P. R. China
| | - Juan Xu
- College of Chemistry and Chemical Engineering, Hubei Polytechnic University, Huangshi 435003, P. R. China
- Wuhan Yuxiang Pharmaceutical Technology Co., Ltd., Wuhan 430200, P. R. China
| | - Yuanyuan Li
- Wuhan Yuxiang Pharmaceutical Technology Co., Ltd., Wuhan 430200, P. R. China
- School of Life Science and Technology & School Chemical and Environmental Engineering, Wuhan Polytechnic University, Wuhan 430023, P. R. China
| | - Huaxiang Fang
- Wuhan Yuxiang Pharmaceutical Technology Co., Ltd., Wuhan 430200, P. R. China
| | - Yi Liu
- Key Laboratory of Coal Conversion and New Carbon Materials of Hubei Province, College of Chemistry and Chemical Engineering, Institute of Advanced Materials and Nanotechnology, Wuhan University of Science and Technology, Wuhan 430081, P. R. China
- School of Life Science and Technology & School Chemical and Environmental Engineering, Wuhan Polytechnic University, Wuhan 430023, P. R. China
- State Key Laboratory of Membrane Separation and Membrane Process & Tianjin Key Laboratory of Green Chemical Technology and Process Engineering, School of Chemistry, Tiangong University, Tianjin 300387, P. R. China
| | - Silong Zhang
- Key Laboratory of Coal Conversion and New Carbon Materials of Hubei Province, College of Chemistry and Chemical Engineering, Institute of Advanced Materials and Nanotechnology, Wuhan University of Science and Technology, Wuhan 430081, P. R. China
- Wuhan Yuxiang Pharmaceutical Technology Co., Ltd., Wuhan 430200, P. R. China
| |
Collapse
|