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Wang B, Guo J, Chen B, Jiao Y, Wan Y, Wu J, Wang Y. Combination of ligand‑based and structure‑based virtual screening for the discovery of novel Janus kinase 2 inhibitors against philadelphia-negative myeloproliferative neoplasms. Mol Divers 2024:10.1007/s11030-024-10938-1. [PMID: 39210217 DOI: 10.1007/s11030-024-10938-1] [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: 08/17/2023] [Accepted: 07/10/2024] [Indexed: 09/04/2024]
Abstract
The activating V617F mutation in Janus kinase 2 (JAK2) has been shown to be the major cause for classic Philadelphia-negative myeloproliferative neoplasms (MPNs). Thus, the development of pharmacologic JAK2 inhibitors is an essential move in combating MPNs. In this study, screening methods examining both ligands and their structures were developed to discover novel JAK2 inhibitors from the ChemDiv database with virtual screening identifying 886 candidate inhibitors. Next, these compounds were further filtered using ADMET, drug likeliness, and PAINS filtering, which reduced the compound number even further. This consolidated list of candidate compounds (n = 49) was then evaluated biologically at molecular level and the highest performing inhibitor with a novel scaffold was selected for further examination. This candidate inhibitor, CD4, was then subjected to molecular dynamics studies, with complex stability, root-mean-square deviation, radius of gyration, binding free energy, and binding properties all examined. The result suggested that CD4 interacts with JAK2 and that the CD4-JAK2 complex is stable. This study was able to identify a candidate inhibitor that warrants further examination and optimization and may potentially serve as a future MPN treatment.
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Affiliation(s)
- Binyou Wang
- School of Basic Medical Sciences, Southwest Medical University, Luzhou, 646000, China
- Zigong Mental Health Center, Zigong Affiliated Hospital of Southwest Medical University, Zigong, 643000, China
- School of Pharmacy, Southwest Medical University, Luzhou, 646000, China
| | - Jianmin Guo
- School of Basic Medical Sciences, Southwest Medical University, Luzhou, 646000, China
| | - Bo Chen
- School of Basic Medical Sciences, Southwest Medical University, Luzhou, 646000, China
| | - Yan Jiao
- State Key Laboratory of Biotherapy and Cancer Center, West China Hospital, Sichuan University, Chengdu, 610041, China
| | - Ying Wan
- School of Basic Medical Sciences, 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.
- 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.
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Lin RH, Lin P, Wang CC, Tung CW. A novel multitask learning algorithm for tasks with distinct chemical space: zebrafish toxicity prediction as an example. J Cheminform 2024; 16:91. [PMID: 39095893 PMCID: PMC11297603 DOI: 10.1186/s13321-024-00891-4] [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: 02/16/2024] [Accepted: 07/27/2024] [Indexed: 08/04/2024] Open
Abstract
Data scarcity is one of the most critical issues impeding the development of prediction models for chemical effects. Multitask learning algorithms leveraging knowledge from relevant tasks showed potential for dealing with tasks with limited data. However, current multitask methods mainly focus on learning from datasets whose task labels are available for most of the training samples. Since datasets were generated for different purposes with distinct chemical spaces, the conventional multitask learning methods may not be suitable. This study presents a novel multitask learning method MTForestNet that can deal with data scarcity problems and learn from tasks with distinct chemical space. The MTForestNet consists of nodes of random forest classifiers organized in the form of a progressive network, where each node represents a random forest model learned from a specific task. To demonstrate the effectiveness of the MTForestNet, 48 zebrafish toxicity datasets were collected and utilized as an example. Among them, two tasks are very different from other tasks with only 1.3% common chemicals shared with other tasks. In an independent test, MTForestNet with a high area under the receiver operating characteristic curve (AUC) value of 0.911 provided superior performance over compared single-task and multitask methods. The overall toxicity derived from the developed models of zebrafish toxicity is well correlated with the experimentally determined overall toxicity. In addition, the outputs from the developed models of zebrafish toxicity can be utilized as features to boost the prediction of developmental toxicity. The developed models are effective for predicting zebrafish toxicity and the proposed MTForestNet is expected to be useful for tasks with distinct chemical space that can be applied in other tasks.Scieific contributionA novel multitask learning algorithm MTForestNet was proposed to address the challenges of developing models using datasets with distinct chemical space that is a common issue of cheminformatics tasks. As an example, zebrafish toxicity prediction models were developed using the proposed MTForestNet which provide superior performance over conventional single-task and multitask learning methods. In addition, the developed zebrafish toxicity prediction models can reduce animal testing.
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Affiliation(s)
- Run-Hsin Lin
- Institute of Biotechnology and Pharmaceutical Research, National Health Research Institutes, Miaoli County, 35053, Taiwan
- Graduate Institute of Data Science, College of Management, Taipei Medical University, Taipei, 10675, Taiwan
| | - Pinpin Lin
- National Institute of Environmental Health Sciences, National Health Research Institutes, Miaoli County, 35053, Taiwan
| | - Chia-Chi Wang
- Department and Graduate Institute of Veterinary Medicine, School of Veterinary Medicine, National Taiwan University, Taipei, 10617, Taiwan
| | - Chun-Wei Tung
- Institute of Biotechnology and Pharmaceutical Research, National Health Research Institutes, Miaoli County, 35053, Taiwan.
- Graduate Institute of Data Science, College of Management, Taipei Medical University, Taipei, 10675, Taiwan.
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Mursalim MKN, Mengko TLER, Hertadi R, Purwarianti A, Susanty M. BiCaps-DBP: Predicting DNA-binding proteins from protein sequences using Bi-LSTM and a 1D-capsule network. Comput Biol Med 2023; 163:107241. [PMID: 37437362 DOI: 10.1016/j.compbiomed.2023.107241] [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: 05/03/2023] [Revised: 06/23/2023] [Accepted: 07/07/2023] [Indexed: 07/14/2023]
Abstract
Predicting DNA-binding proteins (DBPs) based solely on primary sequences is one of the most challenging problems in genome annotation. DBPs play a crucial role in various biological processes, including DNA replication, transcription, repair, and splicing. Some DBPs are essential in pharmaceutical research on various human cancers and autoimmune diseases. Existing experimental methods for identifying DBPs are time-consuming and costly. Therefore, developing a rapid and accurate computational technique is necessary to address the issue. This study introduces BiCaps-DBP, a deep learning-based method that improves DBP prediction performance by combining bidirectional long short-term memory with a 1D-capsule network. This study uses three training and independent datasets to evaluate the proposed model's generalizability and robustness. Based on three independent datasets, BiCaps-DBP achieved 1.05%, 5.79% and 0.40% higher accuracies than an existing predictor for PDB2272, PDB186 and PDB20000, respectively. These outcomes indicate that the proposed method is a promising DBP predictor.
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Affiliation(s)
- Muhammad K N Mursalim
- School of Electrical Engineering and Informatics, Bandung Institute of Technology, Bandung, 40132, Indonesia; Department of Informatics Engineering, Universal University, Batam, Indonesia
| | - Tati L E R Mengko
- School of Electrical Engineering and Informatics, Bandung Institute of Technology, Bandung, 40132, Indonesia.
| | - Rukman Hertadi
- Faculty of Mathematics and Natural Sciences, Bandung Institute of Technology, Bandung, 40132, Indonesia
| | - Ayu Purwarianti
- School of Electrical Engineering and Informatics, Bandung Institute of Technology, Bandung, 40132, Indonesia; Center for Artificial Intelligence (U-CoE AI-VLB), Bandung Institute of Technology, Bandung, 40132, Indonesia
| | - Meredita Susanty
- School of Electrical Engineering and Informatics, Bandung Institute of Technology, Bandung, 40132, Indonesia; Department of Computer Science, Pertamina University, Jakarta, Indonesia
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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.
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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
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