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Zhu Y, Zhang Y, Li X, Wang L. 3MTox: A motif-level graph-based multi-view chemical language model for toxicity identification with deep interpretation. JOURNAL OF HAZARDOUS MATERIALS 2024; 476:135114. [PMID: 38986414 DOI: 10.1016/j.jhazmat.2024.135114] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 04/27/2024] [Revised: 06/24/2024] [Accepted: 07/04/2024] [Indexed: 07/12/2024]
Abstract
Toxicity identification plays a key role in maintaining human health, as it can alert humans to the potential hazards caused by long-term exposure to a wide variety of chemical compounds. Experimental methods for determining toxicity are time-consuming, and costly, while computational methods offer an alternative for the early identification of toxicity. For example, some classical ML and DL methods, which demonstrate excellent performance in toxicity prediction. However, these methods also have some defects, such as over-reliance on artificial features and easy overfitting, etc. Proposing novel models with superior prediction performance is still an urgent task. In this study, we propose a motifs-level graph-based multi-view pretraining language model, called 3MTox, for toxicity identification. The 3MTox model uses Bidirectional Encoder Representations from Transformers (BERT) as the backbone framework, and a motif graph as input. The results of extensive experiments showed that our 3MTox model achieved state-of-the-art performance on toxicity benchmark datasets and outperformed the baseline models considered. In addition, the interpretability of the model ensures that the it can quickly and accurately identify toxicity sites in a given molecule, thereby contributing to the determination of the status of toxicity and associated analyses. We think that the 3MTox model is among the most promising tools that are currently available for toxicity identification.
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Affiliation(s)
- Yingying Zhu
- Guangdong Provincial Key Laboratory of Fermentation and Enzyme Engineering, Joint International Research Laboratory of Synthetic Biology and Medicine, Ministry of Education, Guangdong Provincial Engineering and Technology Research Center of Biopharmaceuticals, School of Biology and Biological Engineering, South China University of Technology, Guangzhou 510006, China
| | - Yanhong Zhang
- Guangdong Provincial Key Laboratory of Fermentation and Enzyme Engineering, Joint International Research Laboratory of Synthetic Biology and Medicine, Ministry of Education, Guangdong Provincial Engineering and Technology Research Center of Biopharmaceuticals, School of Biology and Biological Engineering, South China University of Technology, Guangzhou 510006, China
| | - Xinze Li
- Guangdong Provincial Key Laboratory of Fermentation and Enzyme Engineering, Joint International Research Laboratory of Synthetic Biology and Medicine, Ministry of Education, Guangdong Provincial Engineering and Technology Research Center of Biopharmaceuticals, School of Biology and Biological Engineering, South China University of Technology, Guangzhou 510006, China
| | - Ling Wang
- Guangdong Provincial Key Laboratory of Fermentation and Enzyme Engineering, Joint International Research Laboratory of Synthetic Biology and Medicine, Ministry of Education, Guangdong Provincial Engineering and Technology Research Center of Biopharmaceuticals, School of Biology and Biological Engineering, South China University of Technology, Guangzhou 510006, China.
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2
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Li B, Lin M, Chen T, Wang L. FG-BERT: a generalized and self-supervised functional group-based molecular representation learning framework for properties prediction. Brief Bioinform 2023; 24:bbad398. [PMID: 37930026 DOI: 10.1093/bib/bbad398] [Citation(s) in RCA: 4] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/30/2023] [Revised: 09/25/2023] [Accepted: 10/14/2023] [Indexed: 11/07/2023] Open
Abstract
Artificial intelligence-based molecular property prediction plays a key role in molecular design such as bioactive molecules and functional materials. In this study, we propose a self-supervised pretraining deep learning (DL) framework, called functional group bidirectional encoder representations from transformers (FG-BERT), pertained based on ~1.45 million unlabeled drug-like molecules, to learn meaningful representation of molecules from function groups. The pretrained FG-BERT framework can be fine-tuned to predict molecular properties. Compared to state-of-the-art (SOTA) machine learning and DL methods, we demonstrate the high performance of FG-BERT in evaluating molecular properties in tasks involving physical chemistry, biophysics and physiology across 44 benchmark datasets. In addition, FG-BERT utilizes attention mechanisms to focus on FG features that are critical to the target properties, thereby providing excellent interpretability for downstream training tasks. Collectively, FG-BERT does not require any artificially crafted features as input and has excellent interpretability, providing an out-of-the-box framework for developing SOTA models for a variety of molecule (especially for drug) discovery tasks.
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Affiliation(s)
- Biaoshun Li
- Guangdong Provincial Key Laboratory of Fermentation and Enzyme Engineering, Joint International Research Laboratory of Synthetic Biology and Medicine, Ministry of Education, Guangdong Provincial Engineering and Technology Research Center of Biopharmaceuticals, School of Biology and Biological Engineering, South China University of Technology, Guangzhou 510006, China
| | - Mujie Lin
- Guangdong Provincial Key Laboratory of Fermentation and Enzyme Engineering, Joint International Research Laboratory of Synthetic Biology and Medicine, Ministry of Education, Guangdong Provincial Engineering and Technology Research Center of Biopharmaceuticals, School of Biology and Biological Engineering, South China University of Technology, Guangzhou 510006, China
| | - Tiegen Chen
- Zhongshan Institute for Drug Discovery, Shanghai Institute of Materia Medica, Chinese Academy of Sciences, Room 109, Building C, SSIP Healthcare and Medicine Demonstration Zone, Zhongshan Tsuihang New District, Zhongshan, Guangdong, 528400, China
| | - Ling Wang
- Guangdong Provincial Key Laboratory of Fermentation and Enzyme Engineering, Joint International Research Laboratory of Synthetic Biology and Medicine, Ministry of Education, Guangdong Provincial Engineering and Technology Research Center of Biopharmaceuticals, School of Biology and Biological Engineering, South China University of Technology, Guangzhou 510006, China
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3
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Wu Q, Han J, Huang J, Zhang H, Ren M, Zhang X, Fu Z. Asymmetric synthesis of chiral pyrazolo[3,4- b]pyridin-6-ones under carbene catalysis. Org Biomol Chem 2023; 21:6898-6902. [PMID: 37581413 DOI: 10.1039/d3ob01057k] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 08/16/2023]
Abstract
A structurally diverse set of chiral pyrazolo[3,4-b]pyridin-6-ones was efficiently prepared in excellent yields with excellent enantioselectivities via N-heterocyclic carbene-catalyzed oxidative [3 + 3] annulation of enals with pyrazol-5-amines. The reaction features mild reaction conditions, a broad substrate scope, and easy scale-up.
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Affiliation(s)
- Qianqian Wu
- Institute of Advanced Materials, Nanjing Tech University, 30 South Puzhu Road, Nanjing 211816, China.
| | - Jinna Han
- Institute of Advanced Materials, Nanjing Tech University, 30 South Puzhu Road, Nanjing 211816, China.
| | - Jie Huang
- Institute of Advanced Materials, Nanjing Tech University, 30 South Puzhu Road, Nanjing 211816, China.
| | - Hailong Zhang
- Institute of Advanced Materials, Nanjing Tech University, 30 South Puzhu Road, Nanjing 211816, China.
| | - Min Ren
- Institute of Advanced Materials, Nanjing Tech University, 30 South Puzhu Road, Nanjing 211816, China.
| | - Xiaoxiang Zhang
- Jiangsu Co-Innovation Center of Efficient Processing and Utilization of Forest Resources, College of Chemical Engineering, Nanjing Forestry University, Nanjing 210037, China
| | - Zhenqian Fu
- Institute of Advanced Materials, Nanjing Tech University, 30 South Puzhu Road, Nanjing 211816, China.
- Ningbo Institute, Chongqing Technology Innovation Center, Frontiers Science Center for Flexible Electronics (FSCFE), Northwestern Polytechnical University, Xi'an 710072, China
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4
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Wu J, Xiao Y, Lin M, Cai H, Zhao D, Li Y, Luo H, Tang C, Wang L. DeepCancerMap: A versatile deep learning platform for target- and cell-based anticancer drug discovery. Eur J Med Chem 2023; 255:115401. [PMID: 37116265 DOI: 10.1016/j.ejmech.2023.115401] [Citation(s) in RCA: 3] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/07/2022] [Revised: 03/29/2023] [Accepted: 04/18/2023] [Indexed: 04/30/2023]
Abstract
Discovering new anticancer drugs has been widely concerned and remains an open challenge. Target- and phenotypic-based experimental screening represent two mainstream anticancer drug discovery methods, which suffer from time-consuming, labor-intensive, and high experimental costs. In this study, we collected 485,900 compounds involving in 3,919,974 bioactivity records against 426 anticancer targets and 346 cancer cell lines from academic literature, as well as 60 tumor cell lines from NCI-60 panel. A total of 832 classification models (426 target- and 406 cell-based predictive models) were then constructed to predict the inhibitory activity of compounds against targets and tumor cell lines using FP-GNN deep learning method. Compared to the classical machine learning and deep learning methods, the FP-GNN models achieve considerable overall predictive performance, with the highest AUC values of 0.91, 0.88, 0.91 for the test sets of targets, academia-sourced and NCI-60 cancer cell lines, respectively. A user-friendly webserver called DeepCancerMap and its local version were developed based on these high-quality models, enabling users to perform anticancer drug discovery-related tasks including large-scale virtual screening, profiling prediction of anticancer agents, target fishing, and drug repositioning. We anticipate this platform to accelerate the discovery of anticancer drugs in the field. DeepCancerMap is freely available at https://deepcancermap.idruglab.cn.
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Affiliation(s)
- Jingxing Wu
- School of Biology and Biological Engineering, South China University of Technology, Guangzhou, 510006, China
| | - Yi Xiao
- School of Biology and Biological Engineering, South China University of Technology, Guangzhou, 510006, China
| | - Mujie Lin
- School of Biology and Biological Engineering, South China University of Technology, Guangzhou, 510006, China
| | - Hanxuan Cai
- School of Biology and Biological Engineering, South China University of Technology, Guangzhou, 510006, China
| | - Duancheng Zhao
- School of Biology and Biological Engineering, South China University of Technology, Guangzhou, 510006, China
| | - Yirui Li
- School of Software Engineering, South China University of Technology, Guangzhou, 510006, China
| | - Hailin Luo
- School of Software Engineering, South China University of Technology, Guangzhou, 510006, China
| | - Chuanqi Tang
- School of Design, South China University of Technology, Guangzhou, 510006, China
| | - Ling Wang
- School of Biology and Biological Engineering, South China University of Technology, Guangzhou, 510006, China.
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5
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Computational Approaches to the Rational Design of Tubulin-Targeting Agents. Biomolecules 2023; 13:biom13020285. [PMID: 36830654 PMCID: PMC9952983 DOI: 10.3390/biom13020285] [Citation(s) in RCA: 4] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/15/2022] [Revised: 01/27/2023] [Accepted: 01/31/2023] [Indexed: 02/05/2023] Open
Abstract
Microtubules are highly dynamic polymers of α,β-tubulin dimers which play an essential role in numerous cellular processes such as cell proliferation and intracellular transport, making them an attractive target for cancer and neurodegeneration research. To date, a large number of known tubulin binders were derived from natural products, while only one was developed by rational structure-based drug design. Several of these tubulin binders show promising in vitro profiles while presenting unacceptable off-target effects when tested in patients. Therefore, there is a continuing demand for the discovery of safer and more efficient tubulin-targeting agents. Since tubulin structural data is readily available, the employment of computer-aided design techniques can be a key element to focus on the relevant chemical space and guide the design process. Due to the high diversity and quantity of structural data available, we compiled here a guide to the accessible tubulin-ligand structures. Furthermore, we review different ligand and structure-based methods recently used for the successful selection and design of new tubulin-targeting agents.
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Cai H, Zhang H, Zhao D, Wu J, Wang L. FP-GNN: a versatile deep learning architecture for enhanced molecular property prediction. Brief Bioinform 2022; 23:6702671. [PMID: 36124766 DOI: 10.1093/bib/bbac408] [Citation(s) in RCA: 23] [Impact Index Per Article: 11.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/05/2022] [Revised: 07/28/2022] [Accepted: 08/22/2022] [Indexed: 12/14/2022] Open
Abstract
Accurate prediction of molecular properties, such as physicochemical and bioactive properties, as well as ADME/T (absorption, distribution, metabolism, excretion and toxicity) properties, remains a fundamental challenge for molecular design, especially for drug design and discovery. In this study, we advanced a novel deep learning architecture, termed FP-GNN (fingerprints and graph neural networks), which combined and simultaneously learned information from molecular graphs and fingerprints for molecular property prediction. To evaluate the FP-GNN model, we conducted experiments on 13 public datasets, an unbiased LIT-PCBA dataset and 14 phenotypic screening datasets for breast cell lines. Extensive evaluation results showed that compared to advanced deep learning and conventional machine learning algorithms, the FP-GNN algorithm achieved state-of-the-art performance on these datasets. In addition, we analyzed the influence of different molecular fingerprints, and the effects of molecular graphs and molecular fingerprints on the performance of the FP-GNN model. Analysis of the anti-noise ability and interpretation ability also indicated that FP-GNN was competitive in real-world situations. Collectively, FP-GNN algorithm can assist chemists, biologists and pharmacists in predicting and discovering better molecules with desired functions or properties.
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Affiliation(s)
- Hanxuan Cai
- Guangdong Provincial Key Laboratory of Fermentation and Enzyme Engineering, Joint International Research Laboratory of Synthetic Biology and Medicine, Guangdong Provincial Engineering and Technology Research Center of Biopharmaceuticals, School of Biology and Biological Engineering, South China University of Technology, Guangzhou 510006, China
| | - Huimin Zhang
- Guangdong Provincial Key Laboratory of Fermentation and Enzyme Engineering, Joint International Research Laboratory of Synthetic Biology and Medicine, Guangdong Provincial Engineering and Technology Research Center of Biopharmaceuticals, School of Biology and Biological Engineering, South China University of Technology, Guangzhou 510006, China
| | - Duancheng Zhao
- Guangdong Provincial Key Laboratory of Fermentation and Enzyme Engineering, Joint International Research Laboratory of Synthetic Biology and Medicine, Guangdong Provincial Engineering and Technology Research Center of Biopharmaceuticals, School of Biology and Biological Engineering, South China University of Technology, Guangzhou 510006, China
| | - Jingxing Wu
- Guangdong Provincial Key Laboratory of Fermentation and Enzyme Engineering, Joint International Research Laboratory of Synthetic Biology and Medicine, Guangdong Provincial Engineering and Technology Research Center of Biopharmaceuticals, School of Biology and Biological Engineering, South China University of Technology, Guangzhou 510006, China
| | - Ling Wang
- Guangdong Provincial Key Laboratory of Fermentation and Enzyme Engineering, Joint International Research Laboratory of Synthetic Biology and Medicine, Guangdong Provincial Engineering and Technology Research Center of Biopharmaceuticals, School of Biology and Biological Engineering, South China University of Technology, Guangzhou 510006, China
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7
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Zhang H, Huang J, Chen R, Cai H, Chen Y, He S, Xu J, Zhang J, Wang L. Ligand- and structure-based identification of novel CDK9 inhibitors for the potential treatment of leukemia. Bioorg Med Chem 2022; 72:116994. [PMID: 36087428 DOI: 10.1016/j.bmc.2022.116994] [Citation(s) in RCA: 7] [Impact Index Per Article: 3.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/05/2022] [Revised: 08/21/2022] [Accepted: 08/29/2022] [Indexed: 11/02/2022]
Abstract
Cyclin-dependent kinase 9 (CDK9) plays a vital role in controlling cell transcription and has been an attractive target for cancer treatment. Herein, ten predictive models derived from 1330 unique molecules against CDK9 were constructed based on molecular fingerprints and graphs using two conventional machine learning and four deep learning methods. The evaluation results showed that FP-GNN deep learning architecture performed best for CDK9 inhibitors prediction with the highest BA and F1 values of 0.681 and 0.912 for testing set. We then performed virtual screening to identify new CDK9 inhibitors by incorporating the optimal established predictive model and molecular docking. Five compounds were identified to show broad anticancer activity against various cancer cell lines through bioassays. For example, C9 exhibited antiproliferative activities against HeLa, MOLM-13 and MDA-MB-231 with IC50 values of 2.53, 3.92 and 11.65 μM. Kinase inhibition assay results demonstrated that these compounds displayed submicromolar (214 ∼ 504 nM) inhibitory activities against CDK9. Further cellular mechanism evaluation revealed that C9 suppressed the activity of CDK9 and interfered with the expression of Mcl-1 and cleaved PARP in MOLM-13 cells, resulting in the induction of cellular apoptosis. In addition, C9 displayed a good stability in rat liver microsomes, artificial gastrointestinal fluid and plasm. An online platform (called DEEPCDK9Pred) was developed based on the FP-GNN models to predict or design new CDK9 inhibitors. Collectively, our findings demonstrated that FP-GNN algorithm can achieve accurate prediction of CDK9 inhibitors and the subsequent discovery of C9 as a new potential CDK9 inhibitor deserves further structural modification for the treatment of leukemia.
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Affiliation(s)
- Huimin Zhang
- Guangdong Provincial Key Laboratory of Fermentation and Enzyme Engineering, Joint International Research Laboratory of Synthetic Biology and Medicine, Guangdong Provincial Engineering and Technology Research Center of Biopharmaceuticals, School of Biology and Biological Engineering, South China University of Technology, Guangzhou 510006, China
| | - Jindi Huang
- Guangdong Provincial Key Laboratory of Fermentation and Enzyme Engineering, Joint International Research Laboratory of Synthetic Biology and Medicine, Guangdong Provincial Engineering and Technology Research Center of Biopharmaceuticals, School of Biology and Biological Engineering, South China University of Technology, Guangzhou 510006, China
| | - Rui Chen
- State Key Laboratory of Functions and Applications of Medicinal Plants & College of Pharmacy, Guizhou Provincial Engineering Technology Research Center for Chemical Drug R&D, Guizhou Medical University, Guiyang 550004, China
| | - Hanxuan Cai
- Guangdong Provincial Key Laboratory of Fermentation and Enzyme Engineering, Joint International Research Laboratory of Synthetic Biology and Medicine, Guangdong Provincial Engineering and Technology Research Center of Biopharmaceuticals, School of Biology and Biological Engineering, South China University of Technology, Guangzhou 510006, China
| | - Yihao Chen
- Guangdong Provincial Key Laboratory of Fermentation and Enzyme Engineering, Joint International Research Laboratory of Synthetic Biology and Medicine, Guangdong Provincial Engineering and Technology Research Center of Biopharmaceuticals, School of Biology and Biological Engineering, South China University of Technology, Guangzhou 510006, China
| | - Shuyun He
- Guangdong Provincial Key Laboratory of Fermentation and Enzyme Engineering, Joint International Research Laboratory of Synthetic Biology and Medicine, Guangdong Provincial Engineering and Technology Research Center of Biopharmaceuticals, School of Biology and Biological Engineering, South China University of Technology, Guangzhou 510006, China
| | - Jianrong Xu
- Department of Pharmacology and Chemical Biology, Shanghai Jiao Tong University School of Medicine, Shanghai 200025, China; Academy of Integrative Medicine, Shanghai University of Traditional Chinese Medicine, Shanghai 201203, China
| | - Jiquan Zhang
- State Key Laboratory of Functions and Applications of Medicinal Plants & College of Pharmacy, Guizhou Provincial Engineering Technology Research Center for Chemical Drug R&D, Guizhou Medical University, Guiyang 550004, China
| | - Ling Wang
- Guangdong Provincial Key Laboratory of Fermentation and Enzyme Engineering, Joint International Research Laboratory of Synthetic Biology and Medicine, Guangdong Provincial Engineering and Technology Research Center of Biopharmaceuticals, School of Biology and Biological Engineering, South China University of Technology, Guangzhou 510006, China.
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8
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Gorostiola González M, Janssen APA, IJzerman AP, Heitman LH, van Westen GJP. Oncological drug discovery: AI meets structure-based computational research. Drug Discov Today 2022; 27:1661-1670. [PMID: 35301149 DOI: 10.1016/j.drudis.2022.03.005] [Citation(s) in RCA: 5] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/25/2021] [Revised: 01/22/2022] [Accepted: 03/09/2022] [Indexed: 02/08/2023]
Abstract
The integration of machine learning and structure-based methods has proven valuable in the past as a way to prioritize targets and compounds in early drug discovery. In oncological research, these methods can be highly beneficial in addressing the diversity of neoplastic diseases portrayed by the different hallmarks of cancer. Here, we review six use case scenarios for integrated computational methods, namely driver prediction, computational mutagenesis, (off)-target prediction, binding site prediction, virtual screening, and allosteric modulation analysis. We address the heterogeneity of integration approaches and individual methods, while acknowledging their current limitations and highlighting their potential to bring drugs for personalized oncological therapies to the market faster.
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Affiliation(s)
- Marina Gorostiola González
- Division of Drug Discovery and Safety, Leiden Academic Centre for Drug Research, Leiden University, The Netherlands; Oncode Institute, Utrecht, The Netherlands
| | - Antonius P A Janssen
- Oncode Institute, Utrecht, The Netherlands; Molecular Physiology, Leiden Institute of Chemistry, Leiden University, The Netherlands
| | - Adriaan P IJzerman
- Division of Drug Discovery and Safety, Leiden Academic Centre for Drug Research, Leiden University, The Netherlands
| | - Laura H Heitman
- Division of Drug Discovery and Safety, Leiden Academic Centre for Drug Research, Leiden University, The Netherlands; Oncode Institute, Utrecht, The Netherlands
| | - Gerard J P van Westen
- Division of Drug Discovery and Safety, Leiden Academic Centre for Drug Research, Leiden University, The Netherlands.
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9
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He S, Zhao D, Ling Y, Cai H, Cai Y, Zhang J, Wang L. Machine Learning Enables Accurate and Rapid Prediction of Active Molecules Against Breast Cancer Cells. Front Pharmacol 2022; 12:796534. [PMID: 34975493 PMCID: PMC8719637 DOI: 10.3389/fphar.2021.796534] [Citation(s) in RCA: 4] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/17/2021] [Accepted: 12/02/2021] [Indexed: 12/22/2022] Open
Abstract
Breast cancer (BC) has surpassed lung cancer as the most frequently occurring cancer, and it is the leading cause of cancer-related death in women. Therefore, there is an urgent need to discover or design new drug candidates for BC treatment. In this study, we first collected a series of structurally diverse datasets consisting of 33,757 active and 21,152 inactive compounds for 13 breast cancer cell lines and one normal breast cell line commonly used in in vitro antiproliferative assays. Predictive models were then developed using five conventional machine learning algorithms, including naïve Bayesian, support vector machine, k-Nearest Neighbors, random forest, and extreme gradient boosting, as well as five deep learning algorithms, including deep neural networks, graph convolutional networks, graph attention network, message passing neural networks, and Attentive FP. A total of 476 single models and 112 fusion models were constructed based on three types of molecular representations including molecular descriptors, fingerprints, and graphs. The evaluation results demonstrate that the best model for each BC cell subtype can achieve high predictive accuracy for the test sets with AUC values of 0.689–0.993. Moreover, important structural fragments related to BC cell inhibition were identified and interpreted. To facilitate the use of the model, an online webserver called ChemBC (http://chembc.idruglab.cn/) and its local version software (https://github.com/idruglab/ChemBC) were developed to predict whether compounds have potential inhibitory activity against BC cells.
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Affiliation(s)
- Shuyun He
- Guangdong Provincial Key Laboratory of Fermentation and Enzyme Engineering, Guangdong Provincial Engineering and Technology Research Center of Biopharmaceuticals, School of Biology and Biological Engineering, South China University of Technology, Guangzhou, China.,Joint International Research Laboratory of Synthetic Biology and Medicine, Guangdong Provincial Engineering and Technology Research Center of Biopharmaceuticals, School of Biology and Biological Engineering, South China University of Technology, Guangzhou, China
| | - Duancheng Zhao
- Guangdong Provincial Key Laboratory of Fermentation and Enzyme Engineering, Guangdong Provincial Engineering and Technology Research Center of Biopharmaceuticals, School of Biology and Biological Engineering, South China University of Technology, Guangzhou, China.,Joint International Research Laboratory of Synthetic Biology and Medicine, Guangdong Provincial Engineering and Technology Research Center of Biopharmaceuticals, School of Biology and Biological Engineering, South China University of Technology, Guangzhou, China
| | - Yanle Ling
- Guangdong Provincial Key Laboratory of Fermentation and Enzyme Engineering, Guangdong Provincial Engineering and Technology Research Center of Biopharmaceuticals, School of Biology and Biological Engineering, South China University of Technology, Guangzhou, China.,Joint International Research Laboratory of Synthetic Biology and Medicine, Guangdong Provincial Engineering and Technology Research Center of Biopharmaceuticals, School of Biology and Biological Engineering, South China University of Technology, Guangzhou, China
| | - Hanxuan Cai
- Guangdong Provincial Key Laboratory of Fermentation and Enzyme Engineering, Guangdong Provincial Engineering and Technology Research Center of Biopharmaceuticals, School of Biology and Biological Engineering, South China University of Technology, Guangzhou, China.,Joint International Research Laboratory of Synthetic Biology and Medicine, Guangdong Provincial Engineering and Technology Research Center of Biopharmaceuticals, School of Biology and Biological Engineering, South China University of Technology, Guangzhou, China
| | - Yike Cai
- Center for Certification and Evaluation, Guangdong Drug Administration, Guangzhou, China
| | - Jiquan Zhang
- State Key Laboratory of Functions and Applications of Medicinal Plants, College of Pharmacy, Guizhou Provincial Engineering Technology Research Center for Chemical Drug R&D, Guizhou Medical University, Guiyang, China
| | - Ling Wang
- Guangdong Provincial Key Laboratory of Fermentation and Enzyme Engineering, Guangdong Provincial Engineering and Technology Research Center of Biopharmaceuticals, School of Biology and Biological Engineering, South China University of Technology, Guangzhou, China.,Joint International Research Laboratory of Synthetic Biology and Medicine, Guangdong Provincial Engineering and Technology Research Center of Biopharmaceuticals, School of Biology and Biological Engineering, South China University of Technology, Guangzhou, China
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10
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Zhai S, Zhang H, Chen R, Wu J, Ai D, Tao S, Cai Y, Zhang JQ, Wang L. Design, synthesis and biological evaluation of novel hybrids targeting mTOR and HDACs for potential treatment of hepatocellular carcinoma. Eur J Med Chem 2021; 225:113824. [PMID: 34509167 DOI: 10.1016/j.ejmech.2021.113824] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/18/2021] [Revised: 08/19/2021] [Accepted: 08/28/2021] [Indexed: 01/02/2023]
Abstract
Hepatocellular carcinoma (HCC) is a major contributor to global cancer incidence and mortality. Many pathways are involved in the development of HCC and various proteins including mTOR and HDACs have been identified as potential drug targets for HCC treatment. In the present study, two series of novel hybrid molecules targeting mTOR and HDACs were designed and synthesized based on parent inhibitors (MLN0128 and PP121 for mTOR, SAHA for HDACs) by using a fusion-type molecular hybridization strategy. In vitro antiproliferative assays demonstrated that these novel hybrids with suitable linker lengths exhibited broad cytotoxicity against various cancer cell lines, with significant activity against HepG2 cells. Notably, DI06, an MLN0128-based hybrid, exhibited antiproliferative activity against HepG2 cells with an IC50 value of 1.61 μM, which was comparable to those of both parent drugs (MLN0128, IC50 = 2.13 μM and SAHA, IC50 = 2.26 μM). In vitro enzyme inhibition assays indicated that DI06, DI07 and DI17 (PP121-based hybrid) exhibited nanomolar inhibitory activity against mTOR kinase and HDACs (e.g., HDAC1, HDAC2, HDAC3, HADC6 and HADC8). Cellular studies and western blot analyses uncovered that in HepG2 cells, DI06 and DI17 induced cell apoptosis by targeting mTOR and HDACs, blocked the cell cycle at the G0/G1 phase and suppressed cell migration. The potential binding modes of the hybrids (DI06 and DI17) with mTOR and HDACs were investigated by molecular docking. DI06 displayed better stability in rat liver microsomes than DI07 and DI17. Collectively, DI06 as a novel mTOR and HDACs inhibitor presented here warrants further investigation as a potential treatment of HCC.
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Affiliation(s)
- Shiyang Zhai
- Guangdong Provincial Key Laboratory of Fermentation and Enzyme Engineering, Joint International Research Laboratory of Synthetic Biology and Medicine, Guangdong Provincial Engineering and Technology Research Center of Biopharmaceuticals, School of Biology and Biological Engineering, South China University of Technology, Guangzhou, 510006, China
| | - Huimin Zhang
- Guangdong Provincial Key Laboratory of Fermentation and Enzyme Engineering, Joint International Research Laboratory of Synthetic Biology and Medicine, Guangdong Provincial Engineering and Technology Research Center of Biopharmaceuticals, School of Biology and Biological Engineering, South China University of Technology, Guangzhou, 510006, China
| | - Rui Chen
- State Key Laboratory of Functions and Applications of Medicinal Plants & College of Pharmacy, Guizhou Provincial Engineering Technology Research Center for Chemical Drug R&D, Guizhou Medical University, Guiyang, 550004, China
| | - Jiangxia Wu
- Guangdong Provincial Key Laboratory of Fermentation and Enzyme Engineering, Joint International Research Laboratory of Synthetic Biology and Medicine, Guangdong Provincial Engineering and Technology Research Center of Biopharmaceuticals, School of Biology and Biological Engineering, South China University of Technology, Guangzhou, 510006, China
| | - Daiqiao Ai
- Guangdong Provincial Key Laboratory of Fermentation and Enzyme Engineering, Joint International Research Laboratory of Synthetic Biology and Medicine, Guangdong Provincial Engineering and Technology Research Center of Biopharmaceuticals, School of Biology and Biological Engineering, South China University of Technology, Guangzhou, 510006, China
| | - Shunming Tao
- Guangdong Provincial Key Laboratory of Fermentation and Enzyme Engineering, Joint International Research Laboratory of Synthetic Biology and Medicine, Guangdong Provincial Engineering and Technology Research Center of Biopharmaceuticals, School of Biology and Biological Engineering, South China University of Technology, Guangzhou, 510006, China
| | - Yike Cai
- Center for Certification and Evaluation, Guangdong Drug Administration, Guangzhou, 510080, China
| | - Ji-Quan Zhang
- State Key Laboratory of Functions and Applications of Medicinal Plants & College of Pharmacy, Guizhou Provincial Engineering Technology Research Center for Chemical Drug R&D, Guizhou Medical University, Guiyang, 550004, China
| | - Ling Wang
- Guangdong Provincial Key Laboratory of Fermentation and Enzyme Engineering, Joint International Research Laboratory of Synthetic Biology and Medicine, Guangdong Provincial Engineering and Technology Research Center of Biopharmaceuticals, School of Biology and Biological Engineering, South China University of Technology, Guangzhou, 510006, China.
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Carracedo-Reboredo P, Liñares-Blanco J, Rodríguez-Fernández N, Cedrón F, Novoa FJ, Carballal A, Maojo V, Pazos A, Fernandez-Lozano C. A review on machine learning approaches and trends in drug discovery. Comput Struct Biotechnol J 2021; 19:4538-4558. [PMID: 34471498 PMCID: PMC8387781 DOI: 10.1016/j.csbj.2021.08.011] [Citation(s) in RCA: 116] [Impact Index Per Article: 38.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/01/2021] [Revised: 08/06/2021] [Accepted: 08/06/2021] [Indexed: 12/30/2022] Open
Abstract
Drug discovery aims at finding new compounds with specific chemical properties for the treatment of diseases. In the last years, the approach used in this search presents an important component in computer science with the skyrocketing of machine learning techniques due to its democratization. With the objectives set by the Precision Medicine initiative and the new challenges generated, it is necessary to establish robust, standard and reproducible computational methodologies to achieve the objectives set. Currently, predictive models based on Machine Learning have gained great importance in the step prior to preclinical studies. This stage manages to drastically reduce costs and research times in the discovery of new drugs. This review article focuses on how these new methodologies are being used in recent years of research. Analyzing the state of the art in this field will give us an idea of where cheminformatics will be developed in the short term, the limitations it presents and the positive results it has achieved. This review will focus mainly on the methods used to model the molecular data, as well as the biological problems addressed and the Machine Learning algorithms used for drug discovery in recent years.
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Key Words
- ADMET, Absorption, distribution, metabolism, elimination and toxicity
- ADR, Adverse Drug Reaction
- AI, Artificial Intelligence
- ANN, Artificial Neural Networks
- APFP, Atom Pairs 2d FingerPrint
- AUC, Area under the Curve
- BBB, Blood–Brain barrier
- CDK, Chemical Development Kit
- CNN, Convolutional Neural Networks
- CNS, Central Nervous System
- CPI, Compound-protein interaction
- CV, Cross Validation
- Cheminformatics
- DL, Deep Learning
- DNA, Deoxyribonucleic acid
- Deep Learning
- Drug Discovery
- ECFP, Extended Connectivity Fingerprints
- FDA, Food and Drug Administration
- FNN, Fully Connected Neural Networks
- FP, Fringerprints
- FS, Feature Selection
- GCN, Graph Convolutional Networks
- GEO, Gene Expression Omnibus
- GNN, Graph Neural Networks
- GO, Gene Ontology
- KEGG, Kyoto Encyclopedia of Genes and Genomes
- MACCS, Molecular ACCess System
- MCC, Matthews correlation coefficient
- MD, Molecular Descriptors
- MKL, Multiple Kernel Learning
- ML, Machine Learning
- Machine Learning
- Molecular Descriptors
- NB, Naive Bayes
- OOB, Out of Bag
- PCA, Principal Component Analyisis
- QSAR
- QSAR, Quantitative structure–activity relationship
- RF, Random Forest
- RNA, Ribonucleic Acid
- SMILES, simplified molecular-input line-entry system
- SVM, Support Vector Machines
- TCGA, The Cancer Genome Atlas
- WHO, World Health Organization
- t-SNE, t-Distributed Stochastic Neighbor Embedding
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Affiliation(s)
- Paula Carracedo-Reboredo
- Department of Computer Science and Information Technologies, Faculty of Computer Science, Universidade da Coruna, Campus Elviña s/n, A Coruña 15071, Spain
| | - Jose Liñares-Blanco
- Department of Computer Science and Information Technologies, Faculty of Computer Science, Universidade da Coruna, Campus Elviña s/n, A Coruña 15071, Spain
- CITIC-Research Center of Information and Communication Technologies, Universidade da Coruna, A Coruña 15071, Spain
| | - Nereida Rodríguez-Fernández
- CITIC-Research Center of Information and Communication Technologies, Universidade da Coruna, A Coruña 15071, Spain
- Department of Computer Science and Information Technologies, Faculty of Communication Science, Universidade da Coruna, Campus Elviña s/n, A Coruña 15071, Spain
| | - Francisco Cedrón
- Department of Computer Science and Information Technologies, Faculty of Computer Science, Universidade da Coruna, Campus Elviña s/n, A Coruña 15071, Spain
| | - Francisco J. Novoa
- Department of Computer Science and Information Technologies, Faculty of Computer Science, Universidade da Coruna, Campus Elviña s/n, A Coruña 15071, Spain
| | - Adrian Carballal
- Department of Computer Science and Information Technologies, Faculty of Computer Science, Universidade da Coruna, Campus Elviña s/n, A Coruña 15071, Spain
- CITIC-Research Center of Information and Communication Technologies, Universidade da Coruna, A Coruña 15071, Spain
- Department of Computer Science and Information Technologies, Faculty of Communication Science, Universidade da Coruna, Campus Elviña s/n, A Coruña 15071, Spain
| | - Victor Maojo
- Biomedical Informatics Group, Artificial Intelligence Department, Polytechnic University of Madrid, Calle de los Ciruelos, Boadilla del Monte, Madrid 28660, Spain
| | - Alejandro Pazos
- Department of Computer Science and Information Technologies, Faculty of Computer Science, Universidade da Coruna, Campus Elviña s/n, A Coruña 15071, Spain
- CITIC-Research Center of Information and Communication Technologies, Universidade da Coruna, A Coruña 15071, Spain
- Grupo de Redes de Neuronas Artificiales y Sistemas Adaptativos. Imagen Médica y Diagnóstico Radiológico (RNASA-IMEDIR), Complexo Hospitalario Universitario de A Coruña (CHUAC), SERGAS, Universidade da Coruña, Instituto de Investigación Biomédica de A Coruña (INIBIC), A Coruña, Spain
| | - Carlos Fernandez-Lozano
- Department of Computer Science and Information Technologies, Faculty of Computer Science, Universidade da Coruna, Campus Elviña s/n, A Coruña 15071, Spain
- CITIC-Research Center of Information and Communication Technologies, Universidade da Coruna, A Coruña 15071, Spain
- Grupo de Redes de Neuronas Artificiales y Sistemas Adaptativos. Imagen Médica y Diagnóstico Radiológico (RNASA-IMEDIR), Complexo Hospitalario Universitario de A Coruña (CHUAC), SERGAS, Universidade da Coruña, Instituto de Investigación Biomédica de A Coruña (INIBIC), A Coruña, Spain
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Li M, Yan Y, Zhang X, Zhang Y, Xu X, Zhang L, Lu L, Wang J, Zhang Y, Song Q, Zhao C. Scaffold compound L971 exhibits anti-inflammatory activities through inhibition of JAK/STAT and NFκB signalling pathways. J Cell Mol Med 2021; 25:6333-6347. [PMID: 34018320 PMCID: PMC8256347 DOI: 10.1111/jcmm.16609] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/23/2021] [Revised: 04/22/2021] [Accepted: 04/26/2021] [Indexed: 12/17/2022] Open
Abstract
JAK/STAT and NFκB signalling pathways play essential roles in regulating inflammatory responses, which are important pathogenic factors of various serious immune-related diseases, and function individually or synergistically. To find prodrugs that can treat inflammation, we performed a preliminary high-throughput screening of 18 840 small molecular compounds and identified scaffold compound L971 which significantly inhibited JAK/STAT and NFκB driven luciferase activities. L971 could inhibit the constitutive and stimuli-dependent activation of STAT1, STAT3 and IκBα and could significantly down-regulate the proinflammatory gene expression in mouse peritoneal macrophages stimulated by LPS. Gene expression profiles upon L971 treatment were determined using high-throughput RNA sequencing, and significant differentially up-regulated and down-regulated genes were identified by DESeq analysis. The bioinformatic studies confirmed the anti-inflammatory effects of L971. Finally, L971 anti-inflammatory character was further verified in LPS-induced sepsis shock mouse model in vivo. Taken together, these data indicated that L971 could down-regulate both JAK/STAT and NFκB signalling activities and has the potential to treat inflammatory diseases such as sepsis shock.
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Affiliation(s)
- Mengyuan Li
- School of Medicine and PharmacyOcean University of ChinaQingdaoChina
| | - Yu Yan
- School of Medicine and PharmacyOcean University of ChinaQingdaoChina
| | - Xinxin Zhang
- Innovation Platform of Marine Drug Screening & EvaluationQingdao National Laboratory for Marine Science and TechnologyQingdaoChina
| | - Yidan Zhang
- School of Medicine and PharmacyOcean University of ChinaQingdaoChina
| | - Xiaohan Xu
- School of Medicine and PharmacyOcean University of ChinaQingdaoChina
| | - Lei Zhang
- School of Medicine and PharmacyOcean University of ChinaQingdaoChina
| | - Liangliang Lu
- School of Life ScienceLanzhou UniversityLanzhouChina
| | - Jie Wang
- School of Medicine and PharmacyOcean University of ChinaQingdaoChina
| | - Yazhuo Zhang
- School of Medicine and PharmacyOcean University of ChinaQingdaoChina
| | - Qiaoling Song
- School of Medicine and PharmacyOcean University of ChinaQingdaoChina
- Innovation Platform of Marine Drug Screening & EvaluationQingdao National Laboratory for Marine Science and TechnologyQingdaoChina
| | - Chenyang Zhao
- School of Medicine and PharmacyOcean University of ChinaQingdaoChina
- Innovation Platform of Marine Drug Screening & EvaluationQingdao National Laboratory for Marine Science and TechnologyQingdaoChina
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Gao M, Liu T, Li J, Guan Q, Wang H, Yan S, Li Z, Zuo D, Zhang W, Wu Y. YAN, a novel microtubule inhibitor, inhibits P-gp and MRP1 function and induces mitotic slippage followed by apoptosis in multidrug-resistant A549/Taxol cells. Toxicol In Vitro 2020; 69:104971. [DOI: 10.1016/j.tiv.2020.104971] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/14/2020] [Revised: 08/09/2020] [Accepted: 08/13/2020] [Indexed: 01/05/2023]
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Guo Q, Zhang H, Deng Y, Zhai S, Jiang Z, Zhu D, Wang L. Ligand- and structural-based discovery of potential small molecules that target the colchicine site of tubulin for cancer treatment. Eur J Med Chem 2020; 196:112328. [DOI: 10.1016/j.ejmech.2020.112328] [Citation(s) in RCA: 15] [Impact Index Per Article: 3.8] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/28/2020] [Revised: 04/08/2020] [Accepted: 04/08/2020] [Indexed: 01/13/2023]
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