1
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Ma W, Hu J, Chen Z, Ai Y, Zhang Y, Dong K, Meng X, Liu L. The Development and Application of KinomePro-DL: A Deep Learning Based Online Small Molecule Kinome Selectivity Profiling Prediction Platform. J Chem Inf Model 2024; 64:7273-7290. [PMID: 39320984 DOI: 10.1021/acs.jcim.4c00595] [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: 09/27/2024]
Abstract
Characterizing the kinome selectivity profiles of kinase inhibitors is essential in the early stages of novel small-molecule drug discovery. This characterization is critical for interpreting potential adverse events caused by off-target polypharmacology effects and provides unique pharmacological insights for drug repurposing development of existing kinase inhibitor drugs. However, experimental profiling of whole kinome selectivity is still time-consuming and resource-demanding. Here, we report a deep learning classification model using an in-house built data set of inhibitors against 191 well-representative kinases constructed based on a novel strategy by systematically cleaning and integrating six public data sets. This model, a multitask deep neural network, predicts the kinome selectivity profiles of compounds with novel structures. The model demonstrates excellent predictive performance, with auROC, prc-AUC, Accuracy, and Binary_cross_entropy of 0.95, 0.92, 0.90, and 0.37, respectively. It also performs well in a priori testing for inhibitors targeting different categories of proteins from internal compound collections, significantly improving over similar models on data sets from practical application scenarios. Integrated to subsequent machine learning-enhanced virtual screening workflow, novel CDK2 kinase inhibitors with potent kinase inhibitory activity and excellent kinome selectivity profiles are successfully identified. Additionally, we developed a free online web server, KinomePro-DL, to predict the kinome selectivity profiles and kinome-wide polypharmacology effects of small molecules (available on kinomepro-dl.pharmablock.com). Uniquely, our model allows users to quickly fine-tune it with their own training data sets, enhancing both prediction accuracy and robustness.
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Affiliation(s)
- Wei Ma
- Drug Research Business Unit, PharmaBlock Sciences (Nanjing), Inc., 81 Huasheng Road, Jiangbei New Area, Nanjing, Jiangsu 210032, China
| | - Jiaqi Hu
- Drug Research Business Unit, PharmaBlock Sciences (Nanjing), Inc., 81 Huasheng Road, Jiangbei New Area, Nanjing, Jiangsu 210032, China
| | - Zhuangzhi Chen
- Drug Research Business Unit, PharmaBlock Sciences (Nanjing), Inc., 81 Huasheng Road, Jiangbei New Area, Nanjing, Jiangsu 210032, China
| | - Yaoqin Ai
- Drug Research Business Unit, PharmaBlock Sciences (Nanjing), Inc., 81 Huasheng Road, Jiangbei New Area, Nanjing, Jiangsu 210032, China
| | - Yihang Zhang
- Drug Research Business Unit, PharmaBlock Sciences (Nanjing), Inc., 81 Huasheng Road, Jiangbei New Area, Nanjing, Jiangsu 210032, China
| | - Keke Dong
- Drug Research Business Unit, PharmaBlock Sciences (Nanjing), Inc., 81 Huasheng Road, Jiangbei New Area, Nanjing, Jiangsu 210032, China
| | - Xiangfei Meng
- Drug Research Business Unit, PharmaBlock Sciences (Nanjing), Inc., 81 Huasheng Road, Jiangbei New Area, Nanjing, Jiangsu 210032, China
| | - Liu Liu
- Drug Research Business Unit, PharmaBlock Sciences (Nanjing), Inc., 81 Huasheng Road, Jiangbei New Area, Nanjing, Jiangsu 210032, China
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2
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Xerxa E, Bajorath J. Data-oriented protein kinase drug discovery. Eur J Med Chem 2024; 271:116413. [PMID: 38636127 DOI: 10.1016/j.ejmech.2024.116413] [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: 02/29/2024] [Revised: 04/06/2024] [Accepted: 04/11/2024] [Indexed: 04/20/2024]
Abstract
The continued growth of data from biological screening and medicinal chemistry provides opportunities for data-driven experimental design and decision making in early-phase drug discovery. Approaches adopted from data science help to integrate internal and public domain data and extract knowledge from historical in-house data. Protein kinase (PK) drug discovery is an exemplary area where large amounts of data are accumulating, providing a valuable knowledge base for discovery projects. Herein, the evolution of PK drug discovery and development of small molecular PK inhibitors (PKIs) is reviewed, highlighting milestone developments in the field and discussing exemplary studies providing a basis for increasing data orientation of PK discovery efforts.
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Affiliation(s)
- Elena Xerxa
- Department of Life Science Informatics and Data Science, B-IT, LIMES Program Unit Chemical Biology and Medicinal Chemistry, Lamarr Institute for Machine Learning and Artificial Intelligence, Rheinische Friedrich-Wilhelms-Universität, Friedrich-Hirzebruch-Allee 5/6, D-53115, Bonn, Germany
| | - Jürgen Bajorath
- Department of Life Science Informatics and Data Science, B-IT, LIMES Program Unit Chemical Biology and Medicinal Chemistry, Lamarr Institute for Machine Learning and Artificial Intelligence, Rheinische Friedrich-Wilhelms-Universität, Friedrich-Hirzebruch-Allee 5/6, D-53115, Bonn, Germany.
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3
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Svensson E, Hoedt PJ, Hochreiter S, Klambauer G. HyperPCM: Robust Task-Conditioned Modeling of Drug-Target Interactions. J Chem Inf Model 2024; 64:2539-2553. [PMID: 38185877 PMCID: PMC11005051 DOI: 10.1021/acs.jcim.3c01417] [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: 09/04/2023] [Revised: 11/27/2023] [Accepted: 11/27/2023] [Indexed: 01/09/2024]
Abstract
A central problem in drug discovery is to identify the interactions between drug-like compounds and protein targets. Over the past few decades, various quantitative structure-activity relationship (QSAR) and proteo-chemometric (PCM) approaches have been developed to model and predict these interactions. While QSAR approaches solely utilize representations of the drug compound, PCM methods incorporate both representations of the protein target and the drug compound, enabling them to achieve above-chance predictive accuracy on previously unseen protein targets. Both QSAR and PCM approaches have recently been improved by machine learning and deep neural networks, that allow the development of drug-target interaction prediction models from measurement data. However, deep neural networks typically require large amounts of training data and cannot robustly adapt to new tasks, such as predicting interaction for unseen protein targets at inference time. In this work, we propose to use HyperNetworks to efficiently transfer information between tasks during inference and thus to accurately predict drug-target interactions on unseen protein targets. Our HyperPCM method reaches state-of-the-art performance compared to previous methods on multiple well-known benchmarks, including Davis, DUD-E, and a ChEMBL derived data set, and particularly excels at zero-shot inference involving unseen protein targets. Our method, as well as reproducible data preparation, is available at https://github.com/ml-jku/hyper-dti.
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Affiliation(s)
- Emma Svensson
- ELLIS
Unit Linz & Institute for Machine Learning, Johannes Kepler University, Linz 4040, Austria
- Molecular
AI, Discovery Sciences, R&D, AstraZeneca, Gothenburg, 431 83, Sweden
| | - Pieter-Jan Hoedt
- ELLIS
Unit Linz & Institute for Machine Learning, Johannes Kepler University, Linz 4040, Austria
| | - Sepp Hochreiter
- ELLIS
Unit Linz & Institute for Machine Learning, Johannes Kepler University, Linz 4040, Austria
- Institute
of Advanced Research in Artificial Intelligence (IARAI), Vienna 1030, Austria
| | - Günter Klambauer
- ELLIS
Unit Linz & Institute for Machine Learning, Johannes Kepler University, Linz 4040, Austria
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4
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Wu J, Chen Y, Wu J, Zhao D, Huang J, Lin M, Wang L. Large-scale comparison of machine learning methods for profiling prediction of kinase inhibitors. J Cheminform 2024; 16:13. [PMID: 38291477 PMCID: PMC10829268 DOI: 10.1186/s13321-023-00799-5] [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: 05/30/2023] [Accepted: 12/22/2023] [Indexed: 02/01/2024] Open
Abstract
Conventional machine learning (ML) and deep learning (DL) play a key role in the selectivity prediction of kinase inhibitors. A number of models based on available datasets can be used to predict the kinase profile of compounds, but there is still controversy about the advantages and disadvantages of ML and DL for such tasks. In this study, we constructed a comprehensive benchmark dataset of kinase inhibitors, involving in 141,086 unique compounds and 216,823 well-defined bioassay data points for 354 kinases. We then systematically compared the performance of 12 ML and DL methods on the kinase profiling prediction task. Extensive experimental results reveal that (1) Descriptor-based ML models generally slightly outperform fingerprint-based ML models in terms of predictive performance. RF as an ensemble learning approach displays the overall best predictive performance. (2) Single-task graph-based DL models are generally inferior to conventional descriptor- and fingerprint-based ML models, however, the corresponding multi-task models generally improves the average accuracy of kinase profile prediction. For example, the multi-task FP-GNN model outperforms the conventional descriptor- and fingerprint-based ML models with an average AUC of 0.807. (3) Fusion models based on voting and stacking methods can further improve the performance of the kinase profiling prediction task, specifically, RF::AtomPairs + FP2 + RDKitDes fusion model performs best with the highest average AUC value of 0.825 on the test sets. These findings provide useful information for guiding choices of the ML and DL methods for the kinase profiling prediction tasks. Finally, an online platform called KIPP ( https://kipp.idruglab.cn ) and python software are developed based on the best models to support the kinase profiling prediction, as well as various kinase inhibitor identification tasks including virtual screening, compound repositioning and target fishing.
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Affiliation(s)
- 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
| | - 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
| | - 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
| | - 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
| | - 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
| | - MuJie Lin
- 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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5
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Lin Z, Shen H, Liu X, Ma W, Wang M, Ruan J, Yu H, Ma S, Sun X. Recent advances of artificial intelligence in melanoma clinical practice. Melanoma Res 2023; 33:454-461. [PMID: 37696256 DOI: 10.1097/cmr.0000000000000922] [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: 09/13/2023]
Abstract
Skin melanoma is a lethal cancer. The incidence of melanoma is increasing rapidly in all regions of the world. Despite significant breakthroughs in melanoma treatment in recent years, precise diagnosis of melanoma is still a challenge in some cases. Even specialized physicians may need time and effort to make accurate judgments. As artificial intelligence (AI) technology advances into medical practice, it may bring new solutions to this problem based on its efficiency, accuracy, and speed. This paper summarizes the recent progress of AI in melanoma-related applications, including melanoma diagnosis and classification, the discovery of new medication, guiding treatment, and prognostic assessment. The paper also compares the effectiveness of various algorithms in melanoma application and suggests future research directions for AI in melanoma clinical practice.
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Affiliation(s)
- Zijun Lin
- Guangdong Provincial Key Laboratory of Medical Molecular Diagnostics, The First Dongguan Affiliated Hospital, Guangdong Medical University
- Institute of Aging Research, School of Medical Technology, Guangdong Medical University
| | - Haoyan Shen
- School of Biomedical Engineering, Guangdong Medical University
| | - Xinguang Liu
- Guangdong Provincial Key Laboratory of Medical Molecular Diagnostics, The First Dongguan Affiliated Hospital, Guangdong Medical University
- Institute of Aging Research, School of Medical Technology, Guangdong Medical University
| | - Wanrui Ma
- Department of General Medicine, The First Dongguan Affiliated Hospital, Guangdong Medical University, Dongguan
| | - Mingfa Wang
- Department of Pathology, The Second Affiliated Hospital of Hainan Medical University, Haikou
| | - Jie Ruan
- Institute of Aging Research, School of Medical Technology, Guangdong Medical University
| | - Hongbin Yu
- Guangdong Provincial Key Laboratory of Medical Molecular Diagnostics, Chinese American Tumor Institute, Guangdong Medical University, Dongguan, China
| | - Sha Ma
- School of Biomedical Engineering, Guangdong Medical University
| | - Xuerong Sun
- Guangdong Provincial Key Laboratory of Medical Molecular Diagnostics, The First Dongguan Affiliated Hospital, Guangdong Medical University
- Institute of Aging Research, School of Medical Technology, Guangdong Medical University
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6
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Gu S, Liu H, Liu L, Hou T, Kang Y. Artificial intelligence methods in kinase target profiling: Advances and challenges. Drug Discov Today 2023; 28:103796. [PMID: 37805065 DOI: 10.1016/j.drudis.2023.103796] [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: 07/12/2023] [Revised: 09/29/2023] [Accepted: 10/03/2023] [Indexed: 10/09/2023]
Abstract
Kinases have a crucial role in regulating almost the full range of cellular processes, making them essential targets for therapeutic interventions against various diseases. Accurate kinase-profiling prediction is vital for addressing the selectivity/specificity challenges in kinase drug discovery, which is closely related to lead optimization, drug repurposing, and the understanding of potential drug side effects. In this review, we provide an overview of the latest advancements in machine learning (ML)-based and deep learning (DL)-based quantitative structure-activity relationship (QSAR) models for kinase profiling. We highlight current trends in this rapidly evolving field and discuss the existing challenges and future directions regarding experimental data set construction and model architecture design. Our aim is to offer practical insights and guidance for the development and utilization of these approaches.
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Affiliation(s)
- Shukai Gu
- College of Pharmaceutical Sciences, Zhejiang University, Hangzhou 310058, Zhejiang, China
| | - Huanxiang Liu
- Faculty of Applied Science, Macao Polytechnic University, Macao 999078
| | - Liwei Liu
- Advanced Computing and Storage Laboratory, Central Research Institute, 2012 Laboratories, Huawei Technologies Co. Ltd, Nanjing 210000, Jiangsu, China
| | - Tingjun Hou
- College of Pharmaceutical Sciences, Zhejiang University, Hangzhou 310058, Zhejiang, China.
| | - Yu Kang
- College of Pharmaceutical Sciences, Zhejiang University, Hangzhou 310058, Zhejiang, China.
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7
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Rao SPS, Manjunatha UH, Mikolajczak S, Ashigbie PG, Diagana TT. Drug discovery for parasitic diseases: powered by technology, enabled by pharmacology, informed by clinical science. Trends Parasitol 2023; 39:260-271. [PMID: 36803572 DOI: 10.1016/j.pt.2023.01.010] [Citation(s) in RCA: 5] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/30/2022] [Revised: 01/19/2023] [Accepted: 01/25/2023] [Indexed: 02/22/2023]
Abstract
While prevention is a bedrock of public health, innovative therapeutics are needed to complement the armamentarium of interventions required to achieve disease control and elimination targets for neglected diseases. Extraordinary advances in drug discovery technologies have occurred over the past decades, along with accumulation of scientific knowledge and experience in pharmacological and clinical sciences that are transforming many aspects of drug R&D across disciplines. We reflect on how these advances have propelled drug discovery for parasitic infections, focusing on malaria, kinetoplastid diseases, and cryptosporidiosis. We also discuss challenges and research priorities to accelerate discovery and development of urgently needed novel antiparasitic drugs.
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Affiliation(s)
| | | | | | - Paul G Ashigbie
- Novartis Institute for Tropical Diseases, Emeryville, CA, USA.
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8
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Rodríguez-Pérez R, Bajorath J. Explainable Machine Learning for Property Predictions in Compound Optimization. J Med Chem 2021; 64:17744-17752. [PMID: 34902252 DOI: 10.1021/acs.jmedchem.1c01789] [Citation(s) in RCA: 25] [Impact Index Per Article: 8.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/28/2022]
Abstract
The prediction of compound properties from chemical structure is a main task for machine learning (ML) in medicinal chemistry. ML is often applied to large data sets in applications such as compound screening, virtual library enumeration, or generative chemistry. Albeit desirable, a detailed understanding of ML model decisions is typically not required in these cases. By contrast, compound optimization efforts rely on small data sets to identify structural modifications leading to desired property profiles. In this situation, if ML is applied, one usually is reluctant to make decisions based on predictions that cannot be rationalized. Only few ML methods are interpretable. However, to yield insights into complex ML model decisions, explanatory approaches can be applied. Herein, methodologies for better understanding of ML models or explaining individual predictions are reviewed and current challenges in integrating ML into medicinal chemistry programs as well as future opportunities are discussed.
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Affiliation(s)
- Raquel Rodríguez-Pérez
- Department of Life Science Informatics and Data Science, B-IT, LIMES Program Unit Chemical Biology and Medicinal Chemistry, Rheinische Friedrich-Wilhelms-Universität, Friedrich-Hirzebruch-Allee 6, D-53115 Bonn, Germany.,Novartis Institutes for Biomedical Research, Novartis Campus, CH-4002 Basel, Switzerland
| | - Jürgen Bajorath
- Department of Life Science Informatics and Data Science, B-IT, LIMES Program Unit Chemical Biology and Medicinal Chemistry, Rheinische Friedrich-Wilhelms-Universität, Friedrich-Hirzebruch-Allee 6, D-53115 Bonn, Germany
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9
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Born J, Huynh T, Stroobants A, Cornell WD, Manica M. Active Site Sequence Representations of Human Kinases Outperform Full Sequence Representations for Affinity Prediction and Inhibitor Generation: 3D Effects in a 1D Model. J Chem Inf Model 2021; 62:240-257. [PMID: 34905358 DOI: 10.1021/acs.jcim.1c00889] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/11/2022]
Abstract
Recent advances in deep learning have enabled the development of large-scale multimodal models for virtual screening and de novo molecular design. The human kinome with its abundant sequence and inhibitor data presents an attractive opportunity to develop proteochemometric models that exploit the size and internal diversity of this family of targets. Here, we challenge a standard practice in sequence-based affinity prediction models: instead of leveraging the full primary structure of proteins, each target is represented by a sequence of 29 discontiguous residues defining the ATP binding site. In kinase-ligand binding affinity prediction, our results show that the reduced active site sequence representation is not only computationally more efficient but consistently yields significantly higher performance than the full primary structure. This trend persists across different models, data sets, and performance metrics and holds true when predicting pIC50 for both unseen ligands and kinases. Our interpretability analysis reveals a potential explanation for the superiority of the active site models: whereas only mild statistical effects about the extraction of three-dimensional (3D) interaction sites take place in the full sequence models, the active site models are equipped with an implicit but strong inductive bias about the 3D structure stemming from the discontiguity of the active sites. Moreover, in direct comparisons, our models perform similarly or better than previous state-of-the-art approaches in affinity prediction. We then investigate a de novo molecular design task and find that the active site provides benefits in the computational efficiency, but otherwise, both kinase representations yield similar optimized affinities (for both SMILES- and SELFIES-based molecular generators). Our work challenges the assumption that the full primary structure is indispensable for modeling human kinases.
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Affiliation(s)
- Jannis Born
- IBM Research Europe, 8804 Rüschlikon, Switzerland.,Department of Biosystems Science and Engineering, ETH Zurich, 4058 Basel, Switzerland
| | - Tien Huynh
- IBM Research, Yorktown Heights, New York 10598, United States
| | - Astrid Stroobants
- Department of Chemistry, Imperial College London, SW7 2AZ London, United Kingdom
| | - Wendy D Cornell
- IBM Research, Yorktown Heights, New York 10598, United States
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10
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Minimal screening requirements for identifying highly promiscuous kinase inhibitors. Future Med Chem 2021; 13:1083-1085. [PMID: 33998280 DOI: 10.4155/fmc-2021-0077] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/17/2022] Open
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11
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Martin EJ, Zhu XW. Collaborative Profile-QSAR: A Natural Platform for Building Collaborative Models among Competing Companies. J Chem Inf Model 2021; 61:1603-1616. [PMID: 33844519 DOI: 10.1021/acs.jcim.0c01342] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/29/2022]
Abstract
Massively multitask bioactivity models that transfer learning between thousands of assays have been shown to work dramatically better than separate models trained on each individual assay. In particular, the applicability domain for a given model can expand from compounds similar to those tested in that specific assay to those tested across the full complement of contributing assays. If many large companies would share their assay data and train models on the superset, predictions should be better than what each company can do alone. However, a company's compounds, targets, and activities are among their most guarded trade secrets. Strategies have been proposed to share just the individual collaborators' models, without exposing any of the training data. Profile-QSAR (pQSAR) is a two-level, multitask, stacked model. It uses profiles of level-1 predictions from single-task models for thousands of assays as compound descriptors for level-2 models. This work describes its simple and natural adaptation to safe collaboration by model sharing. Broad model sharing has not yet been implemented across multiple large companies, so there are numerous unanswered questions. Novartis was formed from several mergers and acquisitions. In principle, this should allow an internal simulation of model sharing. In practice, the lack of metadata about the origins of compounds and assays made this difficult. Nevertheless, we have attempted to simulate this process and propose some findings: multitask pQSAR is always an improvement over single-task models; collaborative multitask modeling did not improve predictions on internal compounds; collaboration did improve predictions for external compounds but far less than the purely internal multitask modeling for internal compounds; collaborative models for external compounds increasingly improve as overlap between compound collections increases; combining profiles from inside and outside the company is not best, with internal predictions better using only the inside profile and external using only the outside profile, but a consensus of models using all three profiles is best on external compounds and a good compromise on internal compounds. We anticipate similar results from other model-sharing approaches. Indeed, since collaborative pQSAR through model sharing is mathematically identical to pQSAR using actual shared data, we believe our conclusions should apply to collaborative modeling by any current method even including the unlikely scenario of directly sharing all chemical structures and assay data.
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Affiliation(s)
- Eric J Martin
- Novartis Institute for Biomedical Research, 5959 Horton Street, Emeryville, California 94608-2916, United States
| | - Xiang-Wei Zhu
- Novartis Institute for Biomedical Research, 5959 Horton Street, Emeryville, California 94608-2916, United States
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12
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Schuffenhauer A, Schneider N, Hintermann S, Auld D, Blank J, Cotesta S, Engeloch C, Fechner N, Gaul C, Giovannoni J, Jansen J, Joslin J, Krastel P, Lounkine E, Manchester J, Monovich LG, Pelliccioli AP, Schwarze M, Shultz MD, Stiefl N, Baeschlin DK. Evolution of Novartis' Small Molecule Screening Deck Design. J Med Chem 2020; 63:14425-14447. [PMID: 33140646 DOI: 10.1021/acs.jmedchem.0c01332] [Citation(s) in RCA: 25] [Impact Index Per Article: 6.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/22/2022]
Abstract
This article summarizes the evolution of the screening deck at the Novartis Institutes for BioMedical Research (NIBR). Historically, the screening deck was an assembly of all available compounds. In 2015, we designed a first deck to facilitate access to diverse subsets with optimized properties. We allocated the compounds as plated subsets on a 2D grid with property based ranking in one dimension and increasing structural redundancy in the other. The learnings from the 2015 screening deck were applied to the design of a next generation in 2019. We found that using traditional leadlikeness criteria (mainly MW, clogP) reduces the hit rates of attractive chemical starting points in subset screening. Consequently, the 2019 deck relies on solubility and permeability to select preferred compounds. The 2019 design also uses NIBR's experimental assay data and inferred biological activity profiles in addition to structural diversity to define redundancy across the compound sets.
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Affiliation(s)
- Ansgar Schuffenhauer
- Novartis Institutes for BioMedical Research, Novartis Campus, CH-4002 Basel, Switzerland
| | - Nadine Schneider
- Novartis Institutes for BioMedical Research, Novartis Campus, CH-4002 Basel, Switzerland
| | - Samuel Hintermann
- Novartis Institutes for BioMedical Research, Novartis Campus, CH-4002 Basel, Switzerland
| | - Douglas Auld
- Novartis Institutes for BioMedical Research Inc., 181 Massachusetts Avenue, Cambridge, Massachusetts 02139, United States
| | - Jutta Blank
- Novartis Institutes for BioMedical Research, Novartis Campus, CH-4002 Basel, Switzerland
| | - Simona Cotesta
- Novartis Institutes for BioMedical Research, Novartis Campus, CH-4002 Basel, Switzerland
| | - Caroline Engeloch
- Novartis Institutes for BioMedical Research, Novartis Campus, CH-4002 Basel, Switzerland
| | - Nikolas Fechner
- Novartis Institutes for BioMedical Research, Novartis Campus, CH-4002 Basel, Switzerland
| | - Christoph Gaul
- Novartis Institutes for BioMedical Research, Novartis Campus, CH-4002 Basel, Switzerland
| | - Jerome Giovannoni
- Novartis Institutes for BioMedical Research, Novartis Campus, CH-4002 Basel, Switzerland
| | - Johanna Jansen
- Novartis Institutes for BioMedical Research-Emeryville, 5300 Chiron Way, Emeryville, California 94608-2916, United States
| | - John Joslin
- Genomics Institute of the Novartis Foundation, San Diego, California 92121, United States
| | - Philipp Krastel
- Novartis Institutes for BioMedical Research, Novartis Campus, CH-4002 Basel, Switzerland
| | - Eugen Lounkine
- Novartis Institutes for BioMedical Research Inc., 181 Massachusetts Avenue, Cambridge, Massachusetts 02139, United States
| | - John Manchester
- Novartis Institutes for BioMedical Research Inc., 181 Massachusetts Avenue, Cambridge, Massachusetts 02139, United States
| | - Lauren G Monovich
- Novartis Institutes for BioMedical Research Inc., 181 Massachusetts Avenue, Cambridge, Massachusetts 02139, United States
| | - Anna Paola Pelliccioli
- Novartis Institutes for BioMedical Research, Novartis Campus, CH-4002 Basel, Switzerland
| | - Manuel Schwarze
- Novartis Institutes for BioMedical Research, Novartis Campus, CH-4002 Basel, Switzerland
| | - Michael D Shultz
- Novartis Institutes for BioMedical Research Inc., 181 Massachusetts Avenue, Cambridge, Massachusetts 02139, United States
| | - Nikolaus Stiefl
- Novartis Institutes for BioMedical Research, Novartis Campus, CH-4002 Basel, Switzerland
| | - Daniel K Baeschlin
- Novartis Institutes for BioMedical Research, Novartis Campus, CH-4002 Basel, Switzerland
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13
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Identifying representative kinases for inhibitor evaluation via systematic analysis of compound-based target relationships. Eur J Med Chem 2020; 204:112641. [DOI: 10.1016/j.ejmech.2020.112641] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/06/2020] [Revised: 07/01/2020] [Accepted: 07/02/2020] [Indexed: 02/07/2023]
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14
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Liu X, O'Harra KE, Bara JE, Turner CH. Molecular insight into the anion effect and free volume effect of CO 2 solubility in multivalent ionic liquids. Phys Chem Chem Phys 2020; 22:20618-20633. [PMID: 32966430 DOI: 10.1039/d0cp03424j] [Citation(s) in RCA: 19] [Impact Index Per Article: 4.8] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/23/2022]
Abstract
For many years, experimental and theoretical studies have investigated the solubility of CO2 in a variety of ionic liquids (ILs), but the overarching absorption mechanism is still unclear. Currently, two different factors are believed to dominate the absorption performance: (a) the fractional free volume (FFV) accessible for absorption; and (b) the nature of the CO2 interactions with the anion species. The FFV is often more influential than the specific choice of the anion, but neither mechanism provides a complete picture. Herein, we have attempted to decouple these mechanisms in order to provide a more definitive molecular-level perspective of CO2 absorption in IL solvents. We simulate a series of nine different multivalent ILs comprised of imidazolium cations and sulfonate/sulfonimide anions tethered to benzene rings, along with a comprehensive analysis of the CO2 absorption and underlying molecular-level features. We find that the CO2 solubility has a very strong, linear correlation with respect to FFV, but only when comparisons are constrained to a common anion species. The choice of anion results in a fundamental remapping of the correlation between CO2 solubility and FFV. Overall, the free volume effect dominates in the ILs with smaller FFV values, while the choice of anion becomes more important in the systems with larger FFVs. Our proposed mechanistic map is intended to provide a more consistent framework for guiding further IL design for gas absorption applications.
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Affiliation(s)
- Xiaoyang Liu
- Department of Chemical and Biological Engineering, The University of Alabama, Tuscaloosa, AL 35487, USA.
| | - Kathryn E O'Harra
- Department of Chemical and Biological Engineering, The University of Alabama, Tuscaloosa, AL 35487, USA.
| | - Jason E Bara
- Department of Chemical and Biological Engineering, The University of Alabama, Tuscaloosa, AL 35487, USA.
| | - C Heath Turner
- Department of Chemical and Biological Engineering, The University of Alabama, Tuscaloosa, AL 35487, USA.
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15
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Mendolia I, Contino S, Perricone U, Ardizzone E, Pirrone R. Convolutional architectures for virtual screening. BMC Bioinformatics 2020; 21:310. [PMID: 32938359 PMCID: PMC7493874 DOI: 10.1186/s12859-020-03645-9] [Citation(s) in RCA: 7] [Impact Index Per Article: 1.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/02/2020] [Accepted: 07/06/2020] [Indexed: 11/21/2022] Open
Abstract
Background A Virtual Screening algorithm has to adapt to the different stages of this process. Early screening needs to ensure that all bioactive compounds are ranked in the first positions despite of the number of false positives, while a second screening round is aimed at increasing the prediction accuracy. Results A novel CNN architecture is presented to this aim, which predicts bioactivity of candidate compounds on CDK1 using a combination of molecular fingerprints as their vector representation, and has been trained suitably to achieve good results as regards both enrichment factor and accuracy in different screening modes (98.55% accuracy in active-only selection, and 98.88% in high precision discrimination). Conclusion The proposed architecture outperforms state-of-the-art ML approaches, and some interesting insights on molecular fingerprints are devised.
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Affiliation(s)
- Isabella Mendolia
- Dipartimento di Ingegneria Universit'a degli Studi di Palermo, Viale delle Scienze, Edificio 6, 90128, Palermo, Italy.
| | - Salvatore Contino
- Dipartimento di Ingegneria Universit'a degli Studi di Palermo, Viale delle Scienze, Edificio 6, 90128, Palermo, Italy
| | - Ugo Perricone
- Gruppo Drug Design, Fondazione Ri.MED, 90133, Palermo, Italy.
| | - Edoardo Ardizzone
- Dipartimento di Ingegneria Universit'a degli Studi di Palermo, Viale delle Scienze, Edificio 6, 90128, Palermo, Italy
| | - Roberto Pirrone
- Dipartimento di Ingegneria Universit'a degli Studi di Palermo, Viale delle Scienze, Edificio 6, 90128, Palermo, Italy
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16
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Cortés-Ciriano I, Škuta C, Bender A, Svozil D. QSAR-derived affinity fingerprints (part 2): modeling performance for potency prediction. J Cheminform 2020; 12:41. [PMID: 33431016 PMCID: PMC7339533 DOI: 10.1186/s13321-020-00444-5] [Citation(s) in RCA: 10] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/07/2019] [Accepted: 05/16/2020] [Indexed: 01/22/2023] Open
Abstract
Affinity fingerprints report the activity of small molecules across a set of assays, and thus permit to gather information about the bioactivities of structurally dissimilar compounds, where models based on chemical structure alone are often limited, and model complex biological endpoints, such as human toxicity and in vitro cancer cell line sensitivity. Here, we propose to model in vitro compound activity using computationally predicted bioactivity profiles as compound descriptors. To this aim, we apply and validate a framework for the calculation of QSAR-derived affinity fingerprints (QAFFP) using a set of 1360 QSAR models generated using Ki, Kd, IC50 and EC50 data from ChEMBL database. QAFFP thus represent a method to encode and relate compounds on the basis of their similarity in bioactivity space. To benchmark the predictive power of QAFFP we assembled IC50 data from ChEMBL database for 18 diverse cancer cell lines widely used in preclinical drug discovery, and 25 diverse protein target data sets. This study complements part 1 where the performance of QAFFP in similarity searching, scaffold hopping, and bioactivity classification is evaluated. Despite being inherently noisy, we show that using QAFFP as descriptors leads to errors in prediction on the test set in the ~ 0.65-0.95 pIC50 units range, which are comparable to the estimated uncertainty of bioactivity data in ChEMBL (0.76-1.00 pIC50 units). We find that the predictive power of QAFFP is slightly worse than that of Morgan2 fingerprints and 1D and 2D physicochemical descriptors, with an effect size in the 0.02-0.08 pIC50 units range. Including QSAR models with low predictive power in the generation of QAFFP does not lead to improved predictive power. Given that the QSAR models we used to compute the QAFFP were selected on the basis of data availability alone, we anticipate better modeling results for QAFFP generated using more diverse and biologically meaningful targets. Data sets and Python code are publicly available at https://github.com/isidroc/QAFFP_regression .
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Affiliation(s)
- Isidro Cortés-Ciriano
- Centre for Molecular Informatics, Department of Chemistry, University of Cambridge, Lensfield Road, Cambridge, CB2 1EW, UK. .,European Molecular Biology Laboratory, European Bioinformatics Institute (EMBL-EBI), Hinxton, CB10 1SD, UK.
| | - Ctibor Škuta
- CZ-OPENSCREEN: National Infrastructure for Chemical Biology, Institute of Molecular Genetics of the ASCR, v. v. i., Vídeňská 1083, 142 20, Prague, Czech Republic
| | - Andreas Bender
- Centre for Molecular Informatics, Department of Chemistry, University of Cambridge, Lensfield Road, Cambridge, CB2 1EW, UK
| | - Daniel Svozil
- CZ-OPENSCREEN: National Infrastructure for Chemical Biology, Institute of Molecular Genetics of the ASCR, v. v. i., Vídeňská 1083, 142 20, Prague, Czech Republic.,CZ-OPENSCREEN: National Infrastructure for Chemical Biology, Department of Informatics and Chemistry, Faculty of Chemical Technology, University of Chemistry and Technology Prague, Technická 5, 166 28, Prague, Czech Republic
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17
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Norinder U, Spjuth O, Svensson F. Using Predicted Bioactivity Profiles to Improve Predictive Modeling. J Chem Inf Model 2020; 60:2830-2837. [PMID: 32374618 DOI: 10.1021/acs.jcim.0c00250] [Citation(s) in RCA: 10] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/28/2022]
Abstract
Predictive modeling is a cornerstone in early drug development. Using information for multiple domains or across prediction tasks has the potential to improve the performance of predictive modeling. However, aggregating data often leads to incomplete data matrices that might be limiting for modeling. In line with previous studies, we show that by generating predicted bioactivity profiles, and using these as additional features, prediction accuracy of biological endpoints can be improved. Using conformal prediction, a type of confidence predictor, we present a robust framework for the calculation of these profiles and the evaluation of their impact. We report on the outcomes from several approaches to generate the predicted profiles on 16 datasets in cytotoxicity and bioactivity and show that efficiency is improved the most when including the p-values from conformal prediction as bioactivity profiles.
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Affiliation(s)
- Ulf Norinder
- Department of Computer and Systems Sciences, Stockholm University, Box 7003, SE-164 07 Kista, Sweden.,Department of Pharmaceutical Biosciences, Uppsala University, Box 591, SE-75124 Uppsala, Sweden.,MTM Research Centre, School of Science and Technology, Örebro University, SE-70182 Örebro, Sweden
| | - Ola Spjuth
- Department of Pharmaceutical Biosciences, Uppsala University, Box 591, SE-75124 Uppsala, Sweden.,Science for Life Laboratory, Uppsala University, Box 591, SE-75124 Uppsala, Sweden
| | - Fredrik Svensson
- The Alzheimer's Research UK University College London Drug Discovery Institute, The Cruciform Building, Gower Street, WC1E 6BT London, U.K
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18
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Dang NL, Matlock MK, Hughes TB, Swamidass SJ. The Metabolic Rainbow: Deep Learning Phase I Metabolism in Five Colors. J Chem Inf Model 2020; 60:1146-1164. [DOI: 10.1021/acs.jcim.9b00836] [Citation(s) in RCA: 12] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 02/08/2023]
Affiliation(s)
- Na Le Dang
- Department of Pathology and Immunology, Washington University School of Medicine, Campus Box 8118, 660 S. Euclid Ave., St. Louis, Missouri 63110, United States
| | - Matthew K. Matlock
- Department of Pathology and Immunology, Washington University School of Medicine, Campus Box 8118, 660 S. Euclid Ave., St. Louis, Missouri 63110, United States
| | - Tyler B. Hughes
- Department of Pathology and Immunology, Washington University School of Medicine, Campus Box 8118, 660 S. Euclid Ave., St. Louis, Missouri 63110, United States
| | - S. Joshua Swamidass
- Department of Pathology and Immunology, Washington University School of Medicine, Campus Box 8118, 660 S. Euclid Ave., St. Louis, Missouri 63110, United States
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19
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20
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Da C, Zhang D, Stashko M, Vasileiadi E, Parker R, Minson KA, Huey MG, Huelse JM, Hunter D, Gilbert TSK, Norris-Drouin J, Miley M, Herring LE, Graves LM, DeRyckere D, Earp HS, Graham D, Frye SV, Wang X, Kireev D. Data-Driven Construction of Antitumor Agents with Controlled Polypharmacology. J Am Chem Soc 2019; 141:15700-15709. [PMID: 31497954 PMCID: PMC6894422 DOI: 10.1021/jacs.9b08660] [Citation(s) in RCA: 8] [Impact Index Per Article: 1.6] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/04/2023]
Abstract
Controlling which particular members of a large protein family are targeted by a drug is key to achieving a desired therapeutic response. In this study, we report a rational data-driven strategy for achieving restricted polypharmacology in the design of antitumor agents selectively targeting the TYRO3, AXL, and MERTK (TAM) family tyrosine kinases. Our computational approach, based on the concept of fragments in structural environments (FRASE), distills relevant chemical information from structural and chemogenomic databases to assemble a three-dimensional inhibitor structure directly in the protein pocket. Target engagement by the inhibitors designed led to disruption of oncogenic phenotypes as demonstrated in enzymatic assays and in a panel of cancer cell lines, including acute lymphoblastic and myeloid leukemia (ALL/AML) and nonsmall cell lung cancer (NSCLC). Structural rationale underlying the approach was corroborated by X-ray crystallography. The lead compound demonstrated potent target inhibition in a pharmacodynamic study in leukemic mice.
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Affiliation(s)
- Chenxiao Da
- Center for Integrative Chemical Biology and Drug Discovery, Division of Chemical Biology and Medicinal Chemistry, Eshelman School of Pharmacy, University of North Carolina at Chapel Hill, Chapel Hill, North Carolina, 27599-7363
| | - Dehui Zhang
- Center for Integrative Chemical Biology and Drug Discovery, Division of Chemical Biology and Medicinal Chemistry, Eshelman School of Pharmacy, University of North Carolina at Chapel Hill, Chapel Hill, North Carolina, 27599-7363
| | - Michael Stashko
- Center for Integrative Chemical Biology and Drug Discovery, Division of Chemical Biology and Medicinal Chemistry, Eshelman School of Pharmacy, University of North Carolina at Chapel Hill, Chapel Hill, North Carolina, 27599-7363
| | - Eleana Vasileiadi
- Aflac Cancer and Blood Disorders Center, Children’s Healthcare of Atlanta, and Department of Pediatrics, Emory University, Atlanta, GA 30322
| | - Rebecca Parker
- Aflac Cancer and Blood Disorders Center, Children’s Healthcare of Atlanta, and Department of Pediatrics, Emory University, Atlanta, GA 30322
| | - Katherine A. Minson
- Aflac Cancer and Blood Disorders Center, Children’s Healthcare of Atlanta, and Department of Pediatrics, Emory University, Atlanta, GA 30322
| | - Madeline G. Huey
- Aflac Cancer and Blood Disorders Center, Children’s Healthcare of Atlanta, and Department of Pediatrics, Emory University, Atlanta, GA 30322
| | - Justus M. Huelse
- Aflac Cancer and Blood Disorders Center, Children’s Healthcare of Atlanta, and Department of Pediatrics, Emory University, Atlanta, GA 30322
| | - Debra Hunter
- Department of Pharmacology, University of North Carolina at Chapel Hill, Chapel Hill, North Carolina 27599, United States
- Lineberger Comprehensive Cancer Center, Department of Medicine, School of Medicine, University of North Carolina at Chapel Hill, Chapel Hill, North Carolina 27599, United States
| | - Thomas S. K. Gilbert
- Department of Pharmacology, University of North Carolina at Chapel Hill, Chapel Hill, North Carolina 27599, United States
| | - Jacqueline Norris-Drouin
- Center for Integrative Chemical Biology and Drug Discovery, Division of Chemical Biology and Medicinal Chemistry, Eshelman School of Pharmacy, University of North Carolina at Chapel Hill, Chapel Hill, North Carolina, 27599-7363
| | - Michael Miley
- Department of Pharmacology, University of North Carolina at Chapel Hill, Chapel Hill, North Carolina 27599, United States
| | - Laura E. Herring
- Department of Pharmacology, University of North Carolina at Chapel Hill, Chapel Hill, North Carolina 27599, United States
| | - Lee M. Graves
- Department of Pharmacology, University of North Carolina at Chapel Hill, Chapel Hill, North Carolina 27599, United States
| | - Deborah DeRyckere
- Aflac Cancer and Blood Disorders Center, Children’s Healthcare of Atlanta, and Department of Pediatrics, Emory University, Atlanta, GA 30322
| | - H. Shelton Earp
- Department of Pharmacology, University of North Carolina at Chapel Hill, Chapel Hill, North Carolina 27599, United States
- Lineberger Comprehensive Cancer Center, Department of Medicine, School of Medicine, University of North Carolina at Chapel Hill, Chapel Hill, North Carolina 27599, United States
| | - Douglas Graham
- Aflac Cancer and Blood Disorders Center, Children’s Healthcare of Atlanta, and Department of Pediatrics, Emory University, Atlanta, GA 30322
| | - Stephen V. Frye
- Center for Integrative Chemical Biology and Drug Discovery, Division of Chemical Biology and Medicinal Chemistry, Eshelman School of Pharmacy, University of North Carolina at Chapel Hill, Chapel Hill, North Carolina, 27599-7363
- Lineberger Comprehensive Cancer Center, Department of Medicine, School of Medicine, University of North Carolina at Chapel Hill, Chapel Hill, North Carolina 27599, United States
| | - Xiaodong Wang
- Center for Integrative Chemical Biology and Drug Discovery, Division of Chemical Biology and Medicinal Chemistry, Eshelman School of Pharmacy, University of North Carolina at Chapel Hill, Chapel Hill, North Carolina, 27599-7363
| | - Dmitri Kireev
- Center for Integrative Chemical Biology and Drug Discovery, Division of Chemical Biology and Medicinal Chemistry, Eshelman School of Pharmacy, University of North Carolina at Chapel Hill, Chapel Hill, North Carolina, 27599-7363
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21
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Martin EJ, Polyakov VR, Zhu XW, Tian L, Mukherjee P, Liu X. All-Assay-Max2 pQSAR: Activity Predictions as Accurate as Four-Concentration IC 50s for 8558 Novartis Assays. J Chem Inf Model 2019; 59:4450-4459. [PMID: 31518124 DOI: 10.1021/acs.jcim.9b00375] [Citation(s) in RCA: 42] [Impact Index Per Article: 8.4] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/20/2023]
Abstract
Profile-quantitative structure-activity relationship (pQSAR) is a massively multitask, two-step machine learning method with unprecedented scope, accuracy, and applicability domain. In step one, a "profile" of conventional single-assay random forest regression models are trained on a very large number of biochemical and cellular pIC50 assays using Morgan 2 substructural fingerprints as compound descriptors. In step two, a panel of partial least squares (PLS) models are built using the profile of pIC50 predictions from those random forest regression models as compound descriptors (hence the name). Previously described for a panel of 728 biochemical and cellular kinase assays, we have now built an enormous pQSAR from 11 805 diverse Novartis (NVS) IC50 and EC50 assays. This large number of assays, and hence of compound descriptors for PLS, dictated reducing the profile by only including random forest regression models whose predictions correlate with the assay being modeled. The random forest regression and pQSAR models were evaluated with our "realistically novel" held-out test set, whose median average similarity to the nearest training set member across the 11 805 assays was only 0.34, comparable to the novelty of compounds actually selected from virtual screens. For the 11 805 single-assay random forest regression models, the median correlation of prediction with the experiment was only rext2 = 0.05, virtually random, and only 8% of the models achieved our standard success threshold of rext2 = 0.30. For pQSAR, the median correlation was rext2 = 0.53, comparable to four-concentration experimental IC50s, and 72% of the models met our rext2 > 0.30 standard, totaling 8558 successful models. The successful models included assays from all of the 51 annotated target subclasses, as well as 4196 phenotypic assays, indicating that pQSAR can be applied to virtually any disease area. Every month, all models are updated to include new measurements, and predictions are made for 5.5 million NVS compounds, totaling 50 billion predictions. Common uses have included virtual screening, selectivity design, toxicity and promiscuity prediction, mechanism-of-action prediction, and others. Several such actual applications are described.
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Affiliation(s)
- Eric J Martin
- Novartis Institute for Biomedical Research , 5300 Chiron Way , Emeryville , California 94608-2916 , United States
| | - Valery R Polyakov
- Novartis Institute for Biomedical Research , 5300 Chiron Way , Emeryville , California 94608-2916 , United States
| | - Xiang-Wei Zhu
- Novartis Institute for Biomedical Research , 5300 Chiron Way , Emeryville , California 94608-2916 , United States
| | - Li Tian
- Novartis Institute for Biomedical Research , 5300 Chiron Way , Emeryville , California 94608-2916 , United States.,China Novartis Institutes for BioMedical Research Company, Limited , 2F, Building 4, Novartis Campus, No. 4218 Jinke Road , Zhangjiang, Pudong, Shanghai 201203 , China
| | - Prasenjit Mukherjee
- Novartis Institute for Biomedical Research , 5300 Chiron Way , Emeryville , California 94608-2916 , United States
| | - Xin Liu
- Novartis Institute for Biomedical Research , 5300 Chiron Way , Emeryville , California 94608-2916 , United States.,China Novartis Institutes for BioMedical Research Company, Limited , 2F, Building 4, Novartis Campus, No. 4218 Jinke Road , Zhangjiang, Pudong, Shanghai 201203 , China
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22
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Whitehead TM, Irwin BWJ, Hunt P, Segall MD, Conduit GJ. Imputation of Assay Bioactivity Data Using Deep Learning. J Chem Inf Model 2019; 59:1197-1204. [PMID: 30753070 DOI: 10.1021/acs.jcim.8b00768] [Citation(s) in RCA: 31] [Impact Index Per Article: 6.2] [Reference Citation Analysis] [Abstract] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/28/2022]
Abstract
We describe a novel deep learning neural network method and its application to impute assay pIC50 values. Unlike conventional machine learning approaches, this method is trained on sparse bioactivity data as input, typical of that found in public and commercial databases, enabling it to learn directly from correlations between activities measured in different assays. In two case studies on public domain data sets we show that the neural network method outperforms traditional quantitative structure-activity relationship (QSAR) models and other leading approaches. Furthermore, by focusing on only the most confident predictions the accuracy is increased to R2 > 0.9 using our method, as compared to R2 = 0.44 when reporting all predictions.
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Affiliation(s)
- T M Whitehead
- Intellegens , Eagle Labs , Chesterton Road , Cambridge CB4 3AZ , United Kingdom
| | - B W J Irwin
- Optibrium , F5-6 Blenheim House, Cambridge Innovation Park, Denny End Road , Cambridge CB25 9PB , United Kingdom
| | - P Hunt
- Optibrium , F5-6 Blenheim House, Cambridge Innovation Park, Denny End Road , Cambridge CB25 9PB , United Kingdom
| | - M D Segall
- Optibrium , F5-6 Blenheim House, Cambridge Innovation Park, Denny End Road , Cambridge CB25 9PB , United Kingdom
| | - G J Conduit
- Intellegens , Eagle Labs , Chesterton Road , Cambridge CB4 3AZ , United Kingdom.,Cavendish Laboratory , University of Cambridge , J.J. Thomson Avenue , Cambridge CB3 0HE , United Kingdom
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23
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Szamborska-Gbur A, Rutkowska E, Dreas A, Frid M, Vilenchik M, Milik M, Brzózka K, Król M. How to design potent and selective DYRK1B inhibitors? Molecular modeling study. J Mol Model 2019; 25:41. [PMID: 30673861 DOI: 10.1007/s00894-018-3921-3] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.6] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/03/2018] [Accepted: 12/26/2018] [Indexed: 12/19/2022]
Abstract
DYRK1B protein kinase is an emerging anticancer target due to its overexpression in a variety of cancers and its role in cancer chemoresistance through maintaining cancer cells in the G0 (quiescent) state. Consequently, there is a growing interest in the development of potent and selective DYRK1B inhibitors for anticancer therapy. One of the major off-targets is another protein kinase, GSK3β, which phosphorylates an important regulator of cell cycle progression on the same residue as DYRK1B and is involved in multiple signaling pathways. In the current work, we performed a detailed comparative structural analysis of DYRK1B and GSK3β ATP-binding sites and identified key regions responsible for selectivity. As the crystal structure of DYRK1B has never been reported, we built and optimized a homology model by comparative modeling and metadynamics simulations. Calculation of interaction energies between docked ligands in the ATP-binding sites of both kinases allowed us to pinpoint key residues responsible for potency and selectivity. Specifically, the role of the gatekeeper residues in DYRK1B and GSK3β is discussed in detail, and two other residues are identified as key to selectivity of DYRK1B inhibition versus GSK3β. The analysis presented in this work was used to support the design of potent and selective azaindole-quinoline-based DYRK1B inhibitors and can facilitate development of more selective inhibitors for DYRK kinases.
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Affiliation(s)
| | | | | | - Michael Frid
- Felicitex Therapeutics, Inc., 27 Strathmore Road, Natick, MA, 01760, USA
| | - Maria Vilenchik
- Felicitex Therapeutics, Inc., 27 Strathmore Road, Natick, MA, 01760, USA
| | - Mariusz Milik
- Selvita S.A., Bobrzyńskiego 14, 30-348, Kraków, Poland
| | | | - Marcin Król
- Selvita S.A., Bobrzyńskiego 14, 30-348, Kraków, Poland.
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24
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Hessler G, Baringhaus KH. Artificial Intelligence in Drug Design. Molecules 2018; 23:E2520. [PMID: 30279331 PMCID: PMC6222615 DOI: 10.3390/molecules23102520] [Citation(s) in RCA: 149] [Impact Index Per Article: 24.8] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/05/2018] [Revised: 09/21/2018] [Accepted: 09/22/2018] [Indexed: 11/23/2022] Open
Abstract
Artificial Intelligence (AI) plays a pivotal role in drug discovery. In particular artificial neural networks such as deep neural networks or recurrent networks drive this area. Numerous applications in property or activity predictions like physicochemical and ADMET properties have recently appeared and underpin the strength of this technology in quantitative structure-property relationships (QSPR) or quantitative structure-activity relationships (QSAR). Artificial intelligence in de novo design drives the generation of meaningful new biologically active molecules towards desired properties. Several examples establish the strength of artificial intelligence in this field. Combination with synthesis planning and ease of synthesis is feasible and more and more automated drug discovery by computers is expected in the near future.
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Affiliation(s)
- Gerhard Hessler
- R&D, Integrated Drug Discovery, Industriepark Hoechst, 65926 Frankfurt am Main, Germany.
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25
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Bembenek SD, Hirst G, Mirzadegan T. Determination of a Focused Mini Kinase Panel for Early Identification of Selective Kinase Inhibitors. J Chem Inf Model 2018; 58:1434-1440. [DOI: 10.1021/acs.jcim.8b00222] [Citation(s) in RCA: 9] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/28/2022]
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26
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Pogodin PV, Lagunin AA, Rudik AV, Filimonov DA, Druzhilovskiy DS, Nicklaus MC, Poroikov VV. How to Achieve Better Results Using PASS-Based Virtual Screening: Case Study for Kinase Inhibitors. Front Chem 2018; 6:133. [PMID: 29755970 PMCID: PMC5935003 DOI: 10.3389/fchem.2018.00133] [Citation(s) in RCA: 21] [Impact Index Per Article: 3.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/29/2018] [Accepted: 04/09/2018] [Indexed: 12/16/2022] Open
Abstract
Discovery of new pharmaceutical substances is currently boosted by the possibility of utilization of the Synthetically Accessible Virtual Inventory (SAVI) library, which includes about 283 million molecules, each annotated with a proposed synthetic one-step route from commercially available starting materials. The SAVI database is well-suited for ligand-based methods of virtual screening to select molecules for experimental testing. In this study, we compare the performance of three approaches for the analysis of structure-activity relationships that differ in their criteria for selecting of "active" and "inactive" compounds included in the training sets. PASS (Prediction of Activity Spectra for Substances), which is based on a modified Naïve Bayes algorithm, was applied since it had been shown to be robust and to provide good predictions of many biological activities based on just the structural formula of a compound even if the information in the training set is incomplete. We used different subsets of kinase inhibitors for this case study because many data are currently available on this important class of drug-like molecules. Based on the subsets of kinase inhibitors extracted from the ChEMBL 20 database we performed the PASS training, and then applied the model to ChEMBL 23 compounds not yet present in ChEMBL 20 to identify novel kinase inhibitors. As one may expect, the best prediction accuracy was obtained if only the experimentally confirmed active and inactive compounds for distinct kinases in the training procedure were used. However, for some kinases, reasonable results were obtained even if we used merged training sets, in which we designated as inactives the compounds not tested against the particular kinase. Thus, depending on the availability of data for a particular biological activity, one may choose the first or the second approach for creating ligand-based computational tools to achieve the best possible results in virtual screening.
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Affiliation(s)
- Pavel V. Pogodin
- Department of Bioinformatics, Institute of Biomedical Chemistry, Moscow, Russia
| | - Alexey A. Lagunin
- Department of Bioinformatics, Institute of Biomedical Chemistry, Moscow, Russia
- Department of Bioinformatics, Medical-Biological Department, Pirogov Russian National Research Medical University, Moscow, Russia
| | - Anastasia V. Rudik
- Department of Bioinformatics, Institute of Biomedical Chemistry, Moscow, Russia
| | - Dmitry A. Filimonov
- Department of Bioinformatics, Institute of Biomedical Chemistry, Moscow, Russia
| | | | - Mark C. Nicklaus
- Computer-Aided Drug Design Group, Chemical Biology Laboratory, Center for Cancer Research, National Cancer Institute, NIH, NCI-Frederick, Frederick, MD, United States
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27
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Jacoby E, Wroblowski B, Buyck C, Neefs JM, Meyer C, Cummings MD, van Vlijmen H. Protocols for the Design of Kinase-focused Compound Libraries. Mol Inform 2017; 37:e1700119. [PMID: 29116686 DOI: 10.1002/minf.201700119] [Citation(s) in RCA: 6] [Impact Index Per Article: 0.9] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/26/2017] [Accepted: 10/20/2017] [Indexed: 01/12/2023]
Abstract
Protocols for the design of kinase-focused compound libraries are presented. Kinase-focused compound libraries can be differentiated based on the design goal. Depending on whether the library should be a discovery library specific for one particular kinase, a general discovery library for multiple distinct kinase projects, or even phenotypic screening, there exists today a variety of in silico methods to design candidate compound libraries. We address the following scenarios: 1) Datamining of SAR databases and kinase focused vendor catalogues; 2) Predictions and virtual screening; 3) Structure-based design of combinatorial kinase inhibitors; 4) Design of covalent kinase inhibitors; 5) Design of macrocyclic kinase inhibitors; and 6) Design of allosteric kinase inhibitors and activators.
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Affiliation(s)
- Edgar Jacoby
- Janssen Research & Development, Turnhoutseweg 30, 2340, Beerse, Belgium
| | | | - Christophe Buyck
- Janssen Research & Development, Turnhoutseweg 30, 2340, Beerse, Belgium
| | - Jean-Marc Neefs
- Janssen Research & Development, Turnhoutseweg 30, 2340, Beerse, Belgium
| | | | - Maxwell D Cummings
- Janssen Research & Development, 1400 McKean Rd, Spring House, PA 19477, USA
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He X, Da Ros S, Nelson J, Zhu X, Jiang T, Okram B, Jiang S, Michellys PY, Iskandar M, Espinola S, Jia Y, Bursulaya B, Kreusch A, Gao MY, Spraggon G, Baaten J, Clemmer L, Meeusen S, Huang D, Hill R, Nguyen-Tran V, Fathman J, Liu B, Tuntland T, Gordon P, Hollenbeck T, Ng K, Shi J, Bordone L, Liu H. Identification of Potent and Selective RIPK2 Inhibitors for the Treatment of Inflammatory Diseases. ACS Med Chem Lett 2017; 8:1048-1053. [PMID: 29057049 DOI: 10.1021/acsmedchemlett.7b00258] [Citation(s) in RCA: 24] [Impact Index Per Article: 3.4] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/27/2017] [Accepted: 09/27/2017] [Indexed: 12/16/2022] Open
Abstract
NOD2 (nucleotide-binding oligomerization domain-containing protein 2) is an internal pattern recognition receptor that recognizes bacterial peptidoglycan and stimulates host immune responses. Dysfunction of NOD2 pathway has been associated with a number of autoinflammatory disorders. To date, direct inhibitors of NOD2 have not been described due to technical challenges of targeting the oligomeric protein complex. Receptor interacting protein kinase 2 (RIPK2) is an intracellular serine/threonine/tyrosine kinase, a key signaling partner, and an obligate kinase for NOD2. As such, RIPK2 represents an attractive target to probe the pathological roles of NOD2 pathway. To search for selective RIPK2 inhibitors, we employed virtual library screening (VLS) and structure based design that eventually led to a potent and selective RIPK2 inhibitor 8 with excellent oral bioavailability, which was used to evaluate the effects of inhibition of RIPK2 in various in vitro assays and ex vivo and in vivo pharmacodynamic models.
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Affiliation(s)
- Xiaohui He
- Genomics Institute of the Novartis Research Foundation, 10675 John Jay Hopkins Drive, San Diego, California 92121, United States
| | - Sara Da Ros
- Genomics Institute of the Novartis Research Foundation, 10675 John Jay Hopkins Drive, San Diego, California 92121, United States
| | - John Nelson
- Genomics Institute of the Novartis Research Foundation, 10675 John Jay Hopkins Drive, San Diego, California 92121, United States
| | - Xuefeng Zhu
- Genomics Institute of the Novartis Research Foundation, 10675 John Jay Hopkins Drive, San Diego, California 92121, United States
| | - Tao Jiang
- Genomics Institute of the Novartis Research Foundation, 10675 John Jay Hopkins Drive, San Diego, California 92121, United States
| | - Barun Okram
- Genomics Institute of the Novartis Research Foundation, 10675 John Jay Hopkins Drive, San Diego, California 92121, United States
| | - Songchun Jiang
- Genomics Institute of the Novartis Research Foundation, 10675 John Jay Hopkins Drive, San Diego, California 92121, United States
| | - Pierre-Yves Michellys
- Genomics Institute of the Novartis Research Foundation, 10675 John Jay Hopkins Drive, San Diego, California 92121, United States
| | - Maya Iskandar
- Genomics Institute of the Novartis Research Foundation, 10675 John Jay Hopkins Drive, San Diego, California 92121, United States
| | - Sheryll Espinola
- Genomics Institute of the Novartis Research Foundation, 10675 John Jay Hopkins Drive, San Diego, California 92121, United States
| | - Yong Jia
- Genomics Institute of the Novartis Research Foundation, 10675 John Jay Hopkins Drive, San Diego, California 92121, United States
| | - Badry Bursulaya
- Genomics Institute of the Novartis Research Foundation, 10675 John Jay Hopkins Drive, San Diego, California 92121, United States
| | - Andreas Kreusch
- Genomics Institute of the Novartis Research Foundation, 10675 John Jay Hopkins Drive, San Diego, California 92121, United States
| | - Mu-Yun Gao
- Genomics Institute of the Novartis Research Foundation, 10675 John Jay Hopkins Drive, San Diego, California 92121, United States
| | - Glen Spraggon
- Genomics Institute of the Novartis Research Foundation, 10675 John Jay Hopkins Drive, San Diego, California 92121, United States
| | - Janine Baaten
- Genomics Institute of the Novartis Research Foundation, 10675 John Jay Hopkins Drive, San Diego, California 92121, United States
| | - Leah Clemmer
- Genomics Institute of the Novartis Research Foundation, 10675 John Jay Hopkins Drive, San Diego, California 92121, United States
| | - Shelly Meeusen
- Genomics Institute of the Novartis Research Foundation, 10675 John Jay Hopkins Drive, San Diego, California 92121, United States
| | - David Huang
- Genomics Institute of the Novartis Research Foundation, 10675 John Jay Hopkins Drive, San Diego, California 92121, United States
| | - Robert Hill
- Genomics Institute of the Novartis Research Foundation, 10675 John Jay Hopkins Drive, San Diego, California 92121, United States
| | - Vân Nguyen-Tran
- Genomics Institute of the Novartis Research Foundation, 10675 John Jay Hopkins Drive, San Diego, California 92121, United States
| | - John Fathman
- Genomics Institute of the Novartis Research Foundation, 10675 John Jay Hopkins Drive, San Diego, California 92121, United States
| | - Bo Liu
- Genomics Institute of the Novartis Research Foundation, 10675 John Jay Hopkins Drive, San Diego, California 92121, United States
| | - Tove Tuntland
- Genomics Institute of the Novartis Research Foundation, 10675 John Jay Hopkins Drive, San Diego, California 92121, United States
| | - Perry Gordon
- Genomics Institute of the Novartis Research Foundation, 10675 John Jay Hopkins Drive, San Diego, California 92121, United States
| | - Thomas Hollenbeck
- Genomics Institute of the Novartis Research Foundation, 10675 John Jay Hopkins Drive, San Diego, California 92121, United States
| | - Kenneth Ng
- Genomics Institute of the Novartis Research Foundation, 10675 John Jay Hopkins Drive, San Diego, California 92121, United States
| | - Jian Shi
- Genomics Institute of the Novartis Research Foundation, 10675 John Jay Hopkins Drive, San Diego, California 92121, United States
| | - Laura Bordone
- Genomics Institute of the Novartis Research Foundation, 10675 John Jay Hopkins Drive, San Diego, California 92121, United States
| | - Hong Liu
- Genomics Institute of the Novartis Research Foundation, 10675 John Jay Hopkins Drive, San Diego, California 92121, United States
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Martin EJ, Polyakov VR, Tian L, Perez RC. Profile-QSAR 2.0: Kinase Virtual Screening Accuracy Comparable to Four-Concentration IC 50s for Realistically Novel Compounds. J Chem Inf Model 2017. [PMID: 28651433 DOI: 10.1021/acs.jcim.7b00166] [Citation(s) in RCA: 40] [Impact Index Per Article: 5.7] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/28/2022]
Abstract
While conventional random forest regression (RFR) virtual screening models appear to have excellent accuracy on random held-out test sets, they prove lacking in actual practice. Analysis of 18 historical virtual screens showed that random test sets are far more similar to their training sets than are the compounds project teams actually order. A new, cluster-based "realistic" training/test set split, which mirrors the chemical novelty of real-life virtual screens, recapitulates the poor predictive power of RFR models in real projects. The original Profile-QSAR (pQSAR) method greatly broadened the domain of applicability over conventional models by using as independent variables a profile of activity predictions from all historical assays in a large protein family. However, the accuracy still fell short of experiment on realistic test sets. The improved "pQSAR 2.0" method replaces probabilities of activity from naïve Bayes categorical models at several thresholds with predicted IC50s from RFR models. Unexpectedly, the high accuracy also requires removing the RFR model for the actual assay of interest from the independent variable profile. With these improvements, pQSAR 2.0 activity predictions are now statistically comparable to medium-throughput four-concentration IC50 measurements even on the realistic test set. Beyond the yes/no activity predictions from a typical high-throughput screen (HTS) or conventional virtual screen, these semiquantitative IC50 predictions allow for predicted potency, ligand efficiency, lipophilic efficiency, and selectivity against antitargets, greatly facilitating hitlist triaging and enabling virtual screening panels such as toxicity panels and overall promiscuity predictions.
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Affiliation(s)
- Eric J Martin
- Novartis Institutes for Biomedical Research , 5300 Chiron Way, Emeryville, California 94608-2916, United States
| | - Valery R Polyakov
- Novartis Institutes for Biomedical Research , 5300 Chiron Way, Emeryville, California 94608-2916, United States
| | - Li Tian
- Novartis Institutes for Biomedical Research , 5300 Chiron Way, Emeryville, California 94608-2916, United States
| | - Rolando C Perez
- Novartis Institutes for Biomedical Research , 5300 Chiron Way, Emeryville, California 94608-2916, United States
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30
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Liu J, Ning X. Multi-Assay-Based Compound Prioritization via Assistance Utilization: A Machine Learning Framework. J Chem Inf Model 2017; 57:484-498. [PMID: 28234477 DOI: 10.1021/acs.jcim.6b00737] [Citation(s) in RCA: 7] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/30/2022]
Affiliation(s)
- Junfeng Liu
- Indiana University-Purdue University, Indianapolis, 723 West Michigan St., SL 280, Indianapolis, Indiana 46202, United States
| | - Xia Ning
- Indiana University-Purdue University, Indianapolis, 723 West Michigan St., SL 280, Indianapolis, Indiana 46202, United States
- Center
for Computational Biology and Bioinformatics, Indiana University School of Medicine, 410 West 10th St., HITS 5000, Indianapolis, Indiana 46202, United States
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31
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Zolghadr AR, Boroomand S. Spontaneous assembly of HSP90 inhibitors at water/octanol interface: A molecular dynamics simulation study. Chem Phys Lett 2017. [DOI: 10.1016/j.cplett.2016.12.020] [Citation(s) in RCA: 4] [Impact Index Per Article: 0.6] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/09/2023]
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32
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Bosc N, Wroblowski B, Meyer C, Bonnet P. Prediction of Protein Kinase-Ligand Interactions through 2.5D Kinochemometrics. J Chem Inf Model 2017; 57:93-101. [PMID: 27983837 DOI: 10.1021/acs.jcim.6b00520] [Citation(s) in RCA: 11] [Impact Index Per Article: 1.6] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/29/2022]
Abstract
So far, 518 protein kinases have been identified in the human genome. They share a common mechanism of protein phosphorylation and are involved in many critical biological processes of eukaryotic cells. Deregulation of the kinase phosphorylation function induces severe illnesses such as cancer, diabetes, or inflammatory diseases. Many actors in the pharmaceutical domain have made significant efforts to design potent and selective protein kinase inhibitors as new potential drugs. Because the ATP binding site is highly conserved in the protein kinase family, the design of selective inhibitors remains a challenge and has negatively impacted the progression of drug candidates to late-stage clinical development. The work presented here adopts a 2.5D kinochemometrics (KCM) approach, derived from proteochemometrics (PCM), in which protein kinases are depicted by a novel 3D descriptor and the ligands by 2D fingerprints. We demonstrate in two examples that the protein descriptor successfully classified protein kinases based on their group membership and their Asp-Phe-Gly (DFG) conformation. We also compared the performance of our models with those obtained from a full 2D KCM model and QSAR models. In both cases, the internal validation of the models demonstrated good capabilities to distinguish "active" from "inactive" protein kinase-ligand pairs. However, the external validation performed on two independent data sets showed that the two statistical models tended to overestimate the number of "inactive" pairs.
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Affiliation(s)
- Nicolas Bosc
- Institut de Chimie Organique et Analytique (ICOA), UMR CNRS-Université d'Orléans 7311 , Université d'Orléans BP 6759, 45067 Orléans Cedex 2, France
| | - Berthold Wroblowski
- Janssen Research & Development, Janssen Pharmaceutica N.V. , Turnhoutseweg 30, 2340 Beerse, Belgium
| | - Christophe Meyer
- Centre de Recherche Janssen-Cilag , Campus de Maigremont - CS 10615, 27106 Val de Reuil CEDEX, France
| | - Pascal Bonnet
- Institut de Chimie Organique et Analytique (ICOA), UMR CNRS-Université d'Orléans 7311 , Université d'Orléans BP 6759, 45067 Orléans Cedex 2, France
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Merget B, Turk S, Eid S, Rippmann F, Fulle S. Profiling Prediction of Kinase Inhibitors: Toward the Virtual Assay. J Med Chem 2016; 60:474-485. [PMID: 27966949 DOI: 10.1021/acs.jmedchem.6b01611] [Citation(s) in RCA: 73] [Impact Index Per Article: 9.1] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/26/2022]
Abstract
Kinome-wide screening would have the advantage of providing structure-activity relationships against hundreds of targets simultaneously. Here, we report the generation of ligand-based activity prediction models for over 280 kinases by employing Machine Learning methods on an extensive data set of proprietary bioactivity data combined with open data. High quality (AUC > 0.7) was achieved for ∼200 kinases by (1) combining open with proprietary data, (2) choosing Random Forest over alternative tested Machine Learning methods, and (3) balancing the training data sets. Tests on left-out and external data indicate a high value for virtual screening projects. Importantly, the derived models are evenly distributed across the kinome tree, allowing reliable profiling prediction for all kinase branches. The prediction quality was further improved by employing experimental bioactivity fingerprints of a small kinase subset. Overall, the generated models can support various hit identification tasks, including virtual screening, compound repurposing, and the detection of potential off-targets.
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Affiliation(s)
- Benjamin Merget
- BioMed X Innovation Center , Im Neuenheimer Feld 515, 69120 Heidelberg, Germany
| | - Samo Turk
- BioMed X Innovation Center , Im Neuenheimer Feld 515, 69120 Heidelberg, Germany
| | - Sameh Eid
- BioMed X Innovation Center , Im Neuenheimer Feld 515, 69120 Heidelberg, Germany
| | - Friedrich Rippmann
- Global Computational Chemistry, Merck KGaA , Frankfurter Strasse 250, 64293 Darmstadt, Germany
| | - Simone Fulle
- BioMed X Innovation Center , Im Neuenheimer Feld 515, 69120 Heidelberg, Germany
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34
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Touré BB, Giraldes J, Smith T, Sprague ER, Wang Y, Mathieu S, Chen Z, Mishina Y, Feng Y, Yan-Neale Y, Shakya S, Chen D, Meyer M, Puleo D, Brazell JT, Straub C, Sage D, Wright K, Yuan Y, Chen X, Duca J, Kim S, Tian L, Martin E, Hurov K, Shao W. Toward the Validation of Maternal Embryonic Leucine Zipper Kinase: Discovery, Optimization of Highly Potent and Selective Inhibitors, and Preliminary Biology Insight. J Med Chem 2016; 59:4711-23. [PMID: 27187609 DOI: 10.1021/acs.jmedchem.6b00052] [Citation(s) in RCA: 36] [Impact Index Per Article: 4.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/26/2023]
Abstract
MELK kinase has been implicated in playing an important role in tumorigenesis. Our previous studies suggested that MELK is involved in the regulation of cell cycle and its genetic depletion leads to growth inhibition in a subset of high MELK-expressing basal-like breast cancer cell lines. Herein we describe the discovery and optimization of novel MELK inhibitors 8a and 8b that recapitulate the cellular effects observed by short hairpin ribonucleic acid (shRNA)-mediated MELK knockdown in cellular models. We also discovered a novel fluorine-induced hydrophobic collapse that locked the ligand in its bioactive conformation and led to a 20-fold gain in potency. These novel pharmacological inhibitors achieved high exposure in vivo and were well tolerated, which may allow further in vivo evaluation.
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Affiliation(s)
- B Barry Touré
- Novartis Institutes for Biomedical Research , 250 Massachusetts Avenue, Cambridge, Massachusetts 02139, United States
| | - John Giraldes
- Novartis Institutes for Biomedical Research , 250 Massachusetts Avenue, Cambridge, Massachusetts 02139, United States
| | - Troy Smith
- Novartis Institutes for Biomedical Research , 250 Massachusetts Avenue, Cambridge, Massachusetts 02139, United States
| | - Elizabeth R Sprague
- Novartis Institutes for Biomedical Research , 250 Massachusetts Avenue, Cambridge, Massachusetts 02139, United States
| | - Yaping Wang
- Novartis Institutes for Biomedical Research , 250 Massachusetts Avenue, Cambridge, Massachusetts 02139, United States
| | - Simon Mathieu
- Novartis Institutes for Biomedical Research , 250 Massachusetts Avenue, Cambridge, Massachusetts 02139, United States
| | - Zhuoliang Chen
- Novartis Institutes for Biomedical Research , 250 Massachusetts Avenue, Cambridge, Massachusetts 02139, United States
| | - Yuji Mishina
- Novartis Institutes for Biomedical Research , 250 Massachusetts Avenue, Cambridge, Massachusetts 02139, United States
| | - Yun Feng
- Novartis Institutes for Biomedical Research , 250 Massachusetts Avenue, Cambridge, Massachusetts 02139, United States
| | - Yan Yan-Neale
- Novartis Institutes for Biomedical Research , 250 Massachusetts Avenue, Cambridge, Massachusetts 02139, United States
| | - Subarna Shakya
- Novartis Institutes for Biomedical Research , 250 Massachusetts Avenue, Cambridge, Massachusetts 02139, United States
| | - Dongshu Chen
- Novartis Institutes for Biomedical Research , 250 Massachusetts Avenue, Cambridge, Massachusetts 02139, United States
| | - Matthew Meyer
- Novartis Institutes for Biomedical Research , 250 Massachusetts Avenue, Cambridge, Massachusetts 02139, United States
| | - David Puleo
- Novartis Institutes for Biomedical Research , 250 Massachusetts Avenue, Cambridge, Massachusetts 02139, United States
| | - J Tres Brazell
- Novartis Institutes for Biomedical Research , 250 Massachusetts Avenue, Cambridge, Massachusetts 02139, United States
| | - Christopher Straub
- Novartis Institutes for Biomedical Research , 250 Massachusetts Avenue, Cambridge, Massachusetts 02139, United States
| | - David Sage
- Novartis Institutes for Biomedical Research , 250 Massachusetts Avenue, Cambridge, Massachusetts 02139, United States
| | - Kirk Wright
- Novartis Institutes for Biomedical Research , 250 Massachusetts Avenue, Cambridge, Massachusetts 02139, United States
| | - Yanqiu Yuan
- Novartis Institutes for Biomedical Research , 250 Massachusetts Avenue, Cambridge, Massachusetts 02139, United States
| | - Xin Chen
- Novartis Institutes for Biomedical Research , 250 Massachusetts Avenue, Cambridge, Massachusetts 02139, United States
| | - Jose Duca
- Novartis Institutes for Biomedical Research , 250 Massachusetts Avenue, Cambridge, Massachusetts 02139, United States
| | - Sean Kim
- Novartis Institutes for Biomedical Research , 250 Massachusetts Avenue, Cambridge, Massachusetts 02139, United States
| | - Li Tian
- Novartis Institutes for Biomedical Research , 250 Massachusetts Avenue, Cambridge, Massachusetts 02139, United States
| | - Eric Martin
- Novartis Institutes for Biomedical Research , 250 Massachusetts Avenue, Cambridge, Massachusetts 02139, United States
| | - Kristen Hurov
- Novartis Institutes for Biomedical Research , 250 Massachusetts Avenue, Cambridge, Massachusetts 02139, United States
| | - Wenlin Shao
- Novartis Institutes for Biomedical Research , 250 Massachusetts Avenue, Cambridge, Massachusetts 02139, United States
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Muegge I, Mukherjee P. An overview of molecular fingerprint similarity search in virtual screening. Expert Opin Drug Discov 2015; 11:137-48. [PMID: 26558489 DOI: 10.1517/17460441.2016.1117070] [Citation(s) in RCA: 119] [Impact Index Per Article: 13.2] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/20/2022]
Abstract
INTRODUCTION A central premise of medicinal chemistry is that structurally similar molecules exhibit similar biological activities. Molecular fingerprints encode properties of small molecules and assess their similarities computationally through bit string comparisons. Based on the similarity to a biologically active template, molecular fingerprint methods allow for identifying additional compounds with a higher chance of displaying similar biological activities against the same target - a process commonly referred to as virtual screening (VS). AREAS COVERED This article focuses on fingerprint similarity searches in the context of compound selection for enhancing hit sets, comparing compound decks, and VS. In addition, the authors discuss the application of fingerprints in predictive modeling. EXPERT OPINION Fingerprint similarity search methods are especially useful in VS if only a few unrelated ligands are known for a given target and therefore more complex and information rich methods such as pharmacophore searches or structure-based design are not applicable. In addition, fingerprint methods are used in characterizing properties of compound collections such as chemical diversity, density in chemical space, and content of biologically active molecules (biodiversity). Such assessments are important for deciding what compounds to experimentally screen, to purchase, or to assemble in a virtual compound deck for in silico screening or de novo design.
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Affiliation(s)
- Ingo Muegge
- a Boehringer Ingelheim Pharmaceuticals , Department of Small Molecule Discovery Research , Ridgefield , CT , USA
| | - Prasenjit Mukherjee
- a Boehringer Ingelheim Pharmaceuticals , Department of Small Molecule Discovery Research , Ridgefield , CT , USA
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36
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Perspective on computational and structural aspects of kinase discovery from IPK2014. BIOCHIMICA ET BIOPHYSICA ACTA-PROTEINS AND PROTEOMICS 2015; 1854:1595-604. [PMID: 25861861 DOI: 10.1016/j.bbapap.2015.03.014] [Citation(s) in RCA: 4] [Impact Index Per Article: 0.4] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 01/20/2015] [Revised: 03/29/2015] [Accepted: 03/30/2015] [Indexed: 01/16/2023]
Abstract
Recent advances in understanding the activity and selectivity of kinase inhibitors and their relationships to protein structure are presented. Conformational selection in kinases is studied from empirical, data-driven and simulation approaches. Ligand binding and its affinity are, in many cases, determined by the predetermined active and inactive conformation of kinases. Binding affinity and selectivity predictions highlight the current state of the art and advances in computational chemistry as it applies to kinase inhibitor discovery. Kinome wide inhibitor profiling and cell panel profiling lead to a better understanding of selectivity and allow for target validation and patient tailoring hypotheses. This article is part of a Special Issue entitled: Inhibitors of Protein Kinases.
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Lewis RA, Wood D. Modern 2D QSAR for drug discovery. WILEY INTERDISCIPLINARY REVIEWS-COMPUTATIONAL MOLECULAR SCIENCE 2014. [DOI: 10.1002/wcms.1187] [Citation(s) in RCA: 31] [Impact Index Per Article: 3.1] [Reference Citation Analysis] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 01/26/2023]
Affiliation(s)
- Richard A. Lewis
- Novartis Institutes for BioMedical Research; Novartis Pharma AG; Basel Switzerland
| | - David Wood
- Novartis Institutes for BioMedical Research; Novartis Horsham Research Centre; Horsham UK
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38
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Martell RE, Brooks DG, Wang Y, Wilcoxen K. Discovery of novel drugs for promising targets. Clin Ther 2014; 35:1271-81. [PMID: 24054704 DOI: 10.1016/j.clinthera.2013.08.005] [Citation(s) in RCA: 12] [Impact Index Per Article: 1.2] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/02/2013] [Revised: 06/27/2013] [Accepted: 08/13/2013] [Indexed: 11/18/2022]
Abstract
BACKGROUND Once a promising drug target is identified, the steps to actually discover and optimize a drug are diverse and challenging. OBJECTIVE The goal of this study was to provide a road map to navigate drug discovery. METHODS Review general steps for drug discovery and provide illustrating references. RESULTS A number of approaches are available to enhance and accelerate target identification and validation. Consideration of a variety of potential mechanisms of action of potential drugs can guide discovery efforts. The hit to lead stage may involve techniques such as high-throughput screening, fragment-based screening, and structure-based design, with informatics playing an ever-increasing role. Biologically relevant screening models are discussed, including cell lines, 3-dimensional culture, and in vivo screening. The process of enabling human studies for an investigational drug is also discussed. CONCLUSIONS Drug discovery is a complex process that has significantly evolved in recent years.
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Affiliation(s)
- Robert E Martell
- TESARO Inc, Waltham, Massachusetts; Tufts Medical Center, Boston, Massachusetts.
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Abstract
Drug action can be rationalized as interaction of a molecule with proteins in a regulatory network of targets from a specific biological system. Both drug and side effects are often governed by interaction of the drug molecule with many, often unrelated, targets. Accordingly, arrays of protein–ligand interaction data from numerous in vitro profiling assays today provide growing evidence of polypharmacological drug interactions, even for marketed drugs. In vitro off-target profiling has therefore become an important tool in early drug discovery to learn about potential off-target liabilities, which are sometimes beneficial, but more often safety relevant. The rapidly developing field of in silico profiling approaches is complementing in vitro profiling. These approaches capitalize from large amounts of biochemical data from multiple sources to be exploited for optimizing undesirable side effects in pharmaceutical research. Therefore, current in silico profiling models are nowadays perceived as valuable tools in drug discovery, and promise a platform to support optimally informed decisions.
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40
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Tian L, Mukherjee P, Martin E. Kinase-Kernel Models: Accurate Chemogenomic Method for the Entire Human Kinome. Mol Inform 2013; 32:922-8. [DOI: 10.1002/minf.201300091] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.1] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/13/2013] [Accepted: 09/20/2013] [Indexed: 11/06/2022]
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41
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Park H, Kim KK, Kim C, Shin JM, No KT. Descriptor-Based Profile Analysis of Kinase Inhibitors to Predict Inhibitory Activity and to Grasp Kinase Selectivity. B KOREAN CHEM SOC 2013. [DOI: 10.5012/bkcs.2013.34.9.2680] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.1] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/20/2022]
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42
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Gao C, Cahya S, Nicolaou CA, Wang J, Watson IA, Cummins DJ, Iversen PW, Vieth M. Selectivity Data: Assessment, Predictions, Concordance, and Implications. J Med Chem 2013; 56:6991-7002. [DOI: 10.1021/jm400798j] [Citation(s) in RCA: 11] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/28/2022]
Affiliation(s)
- Cen Gao
- Discovery Chemistry, ‡Discovery Statistics, §Advanced Analytics, Lilly Research Laboratories, Eli Lilly and Company, Indianapolis, Indiana 46285, United States
| | - Suntara Cahya
- Discovery Chemistry, ‡Discovery Statistics, §Advanced Analytics, Lilly Research Laboratories, Eli Lilly and Company, Indianapolis, Indiana 46285, United States
| | - Christos A. Nicolaou
- Discovery Chemistry, ‡Discovery Statistics, §Advanced Analytics, Lilly Research Laboratories, Eli Lilly and Company, Indianapolis, Indiana 46285, United States
| | - Jibo Wang
- Discovery Chemistry, ‡Discovery Statistics, §Advanced Analytics, Lilly Research Laboratories, Eli Lilly and Company, Indianapolis, Indiana 46285, United States
| | - Ian A. Watson
- Discovery Chemistry, ‡Discovery Statistics, §Advanced Analytics, Lilly Research Laboratories, Eli Lilly and Company, Indianapolis, Indiana 46285, United States
| | - David J. Cummins
- Discovery Chemistry, ‡Discovery Statistics, §Advanced Analytics, Lilly Research Laboratories, Eli Lilly and Company, Indianapolis, Indiana 46285, United States
| | - Philip W. Iversen
- Discovery Chemistry, ‡Discovery Statistics, §Advanced Analytics, Lilly Research Laboratories, Eli Lilly and Company, Indianapolis, Indiana 46285, United States
| | - Michal Vieth
- Discovery Chemistry, ‡Discovery Statistics, §Advanced Analytics, Lilly Research Laboratories, Eli Lilly and Company, Indianapolis, Indiana 46285, United States
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43
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Jane Tseng Y, Martin E, G Bologa C, Shelat AA. Cheminformatics aspects of high throughput screening: from robots to models: symposium summary. J Comput Aided Mol Des 2013; 27:443-53. [PMID: 23636795 PMCID: PMC4205101 DOI: 10.1007/s10822-013-9646-6] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.2] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/19/2013] [Accepted: 04/08/2013] [Indexed: 12/21/2022]
Abstract
The "Cheminformatics aspects of high throughput screening (HTS): from robots to models" symposium was part of the computers in chemistry technical program at the American Chemical Society National Meeting in Denver, Colorado during the fall of 2011. This symposium brought together researchers from high throughput screening centers and molecular modelers from academia and industry to discuss the integration of currently available high throughput screening data and assays with computational analysis. The topics discussed at this symposium covered the data-infrastructure at various academic, hospital, and National Institutes of Health-funded high throughput screening centers, the cheminformatics and molecular modeling methods used in real world examples to guide screening and hit-finding, and how academic and non-profit organizations can benefit from current high throughput screening cheminformatics resources. Specifically, this article also covers the remarks and discussions in the open panel discussion of the symposium and summarizes the following talks on "Accurate Kinase virtual screening: biochemical, cellular and selectivity", "Selective, privileged and promiscuous chemical patterns in high-throughput screening" and "Visualizing and exploring relationships among HTS hits using network graphs".
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Affiliation(s)
- Y Jane Tseng
- Graduate Institute of Biomedical Electronics and Bioinformatics, National Taiwan University, Taipei 106, Taiwan.
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44
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Zhou S, Li Y, Hou T. Feasibility of Using Molecular Docking-Based Virtual Screening for Searching Dual Target Kinase Inhibitors. J Chem Inf Model 2013; 53:982-96. [DOI: 10.1021/ci400065e] [Citation(s) in RCA: 23] [Impact Index Per Article: 2.1] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/05/2023]
Affiliation(s)
- Shunye Zhou
- Institute of Functional Nano & Soft Materials (FUNSOM) and Jiangsu Key Laboratory for Carbon-Based Functional Materials & Devices, Soochow University, Suzhou, Jiangsu 215123, China
| | - Youyong Li
- Institute of Functional Nano & Soft Materials (FUNSOM) and Jiangsu Key Laboratory for Carbon-Based Functional Materials & Devices, Soochow University, Suzhou, Jiangsu 215123, China
| | - Tingjun Hou
- Institute of Functional Nano & Soft Materials (FUNSOM) and Jiangsu Key Laboratory for Carbon-Based Functional Materials & Devices, Soochow University, Suzhou, Jiangsu 215123, China
- College of Pharmaceutical Sciences, Zhejiang University, Hangzhou, Zhejiang 310058, China
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45
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Biomacromolecular quantitative structure–activity relationship (BioQSAR): a proof-of-concept study on the modeling, prediction and interpretation of protein–protein binding affinity. J Comput Aided Mol Des 2013; 27:67-78. [DOI: 10.1007/s10822-012-9625-3] [Citation(s) in RCA: 78] [Impact Index Per Article: 7.1] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/30/2012] [Accepted: 12/12/2012] [Indexed: 01/22/2023]
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46
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Mukherjee P, Martin E. Profile-QSAR and Surrogate AutoShim Protein-Family Modeling of Proteases. J Chem Inf Model 2012; 52:2430-40. [DOI: 10.1021/ci300059d] [Citation(s) in RCA: 8] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/25/2022]
Affiliation(s)
- Prasenjit Mukherjee
- Oncology
and Exploratory Chemistry, Global Discovery
Chemistry, Novartis Institutes for Biomedical Research, 4560 Horton Street, Emeryville, California 94608, United States
| | - Eric Martin
- Oncology
and Exploratory Chemistry, Global Discovery
Chemistry, Novartis Institutes for Biomedical Research, 4560 Horton Street, Emeryville, California 94608, United States
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47
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Jenkins JL. Large-Scale QSAR in Target Prediction and Phenotypic HTS Assessment. Mol Inform 2012; 31:508-14. [PMID: 27477469 DOI: 10.1002/minf.201200002] [Citation(s) in RCA: 8] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/06/2012] [Accepted: 06/25/2012] [Indexed: 01/31/2023]
Abstract
The advent of in silico compound target prediction offers a potential paradigm shift in how large compound collections are understood and used strategically in high-throughput screens (HTS). Specifically, phenotypic HTS hits may be annotated both with known targets and predicted targets using large-scale QSAR models, enabling a more sophisticated hit assessment. Efforts in massive bioactivity data integration and standardization is empowering such compound-target annotations. These approaches differ fundamentally from the traditional role of QSAR in lead optimization and binding affinity predictions to global, probabilistic target predictions for thousands of human proteins.
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Affiliation(s)
- Jeremy L Jenkins
- Developmental and Molecular Pathways, Quantitative Biology, Novartis Institutes for BioMedical Research, 220 Massachusetts Ave., Cambridge, MA 02139 phone: 617-871-7155.
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48
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Chen H, Chan BK, Drummond J, Estrada AA, Gunzner-Toste J, Liu X, Liu Y, Moffat J, Shore D, Sweeney ZK, Tran T, Wang S, Zhao G, Zhu H, Burdick DJ. Discovery of Selective LRRK2 Inhibitors Guided by Computational Analysis and Molecular Modeling. J Med Chem 2012; 55:5536-45. [DOI: 10.1021/jm300452p] [Citation(s) in RCA: 61] [Impact Index Per Article: 5.1] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/08/2023]
Affiliation(s)
- Huifen Chen
- Discovery
Chemistry Department, ‡Biochemical and Cellular Pharmacology Department, §Drug Metabolism and Pharmacokinetics
Department, and ∥Neuroscience Department, Genentech, Inc., 1 DNA Way, South San Francisco, California 94080, United States
| | - Bryan K. Chan
- Discovery
Chemistry Department, ‡Biochemical and Cellular Pharmacology Department, §Drug Metabolism and Pharmacokinetics
Department, and ∥Neuroscience Department, Genentech, Inc., 1 DNA Way, South San Francisco, California 94080, United States
| | - Jason Drummond
- Discovery
Chemistry Department, ‡Biochemical and Cellular Pharmacology Department, §Drug Metabolism and Pharmacokinetics
Department, and ∥Neuroscience Department, Genentech, Inc., 1 DNA Way, South San Francisco, California 94080, United States
| | - Anthony A. Estrada
- Discovery
Chemistry Department, ‡Biochemical and Cellular Pharmacology Department, §Drug Metabolism and Pharmacokinetics
Department, and ∥Neuroscience Department, Genentech, Inc., 1 DNA Way, South San Francisco, California 94080, United States
| | - Janet Gunzner-Toste
- Discovery
Chemistry Department, ‡Biochemical and Cellular Pharmacology Department, §Drug Metabolism and Pharmacokinetics
Department, and ∥Neuroscience Department, Genentech, Inc., 1 DNA Way, South San Francisco, California 94080, United States
| | - Xingrong Liu
- Discovery
Chemistry Department, ‡Biochemical and Cellular Pharmacology Department, §Drug Metabolism and Pharmacokinetics
Department, and ∥Neuroscience Department, Genentech, Inc., 1 DNA Way, South San Francisco, California 94080, United States
| | - Yichin Liu
- Discovery
Chemistry Department, ‡Biochemical and Cellular Pharmacology Department, §Drug Metabolism and Pharmacokinetics
Department, and ∥Neuroscience Department, Genentech, Inc., 1 DNA Way, South San Francisco, California 94080, United States
| | - John Moffat
- Discovery
Chemistry Department, ‡Biochemical and Cellular Pharmacology Department, §Drug Metabolism and Pharmacokinetics
Department, and ∥Neuroscience Department, Genentech, Inc., 1 DNA Way, South San Francisco, California 94080, United States
| | - Daniel Shore
- Discovery
Chemistry Department, ‡Biochemical and Cellular Pharmacology Department, §Drug Metabolism and Pharmacokinetics
Department, and ∥Neuroscience Department, Genentech, Inc., 1 DNA Way, South San Francisco, California 94080, United States
| | - Zachary K. Sweeney
- Discovery
Chemistry Department, ‡Biochemical and Cellular Pharmacology Department, §Drug Metabolism and Pharmacokinetics
Department, and ∥Neuroscience Department, Genentech, Inc., 1 DNA Way, South San Francisco, California 94080, United States
| | - Thuy Tran
- Discovery
Chemistry Department, ‡Biochemical and Cellular Pharmacology Department, §Drug Metabolism and Pharmacokinetics
Department, and ∥Neuroscience Department, Genentech, Inc., 1 DNA Way, South San Francisco, California 94080, United States
| | - Shumei Wang
- Discovery
Chemistry Department, ‡Biochemical and Cellular Pharmacology Department, §Drug Metabolism and Pharmacokinetics
Department, and ∥Neuroscience Department, Genentech, Inc., 1 DNA Way, South San Francisco, California 94080, United States
| | - Guiling Zhao
- Discovery
Chemistry Department, ‡Biochemical and Cellular Pharmacology Department, §Drug Metabolism and Pharmacokinetics
Department, and ∥Neuroscience Department, Genentech, Inc., 1 DNA Way, South San Francisco, California 94080, United States
| | - Haitao Zhu
- Discovery
Chemistry Department, ‡Biochemical and Cellular Pharmacology Department, §Drug Metabolism and Pharmacokinetics
Department, and ∥Neuroscience Department, Genentech, Inc., 1 DNA Way, South San Francisco, California 94080, United States
| | - Daniel J. Burdick
- Discovery
Chemistry Department, ‡Biochemical and Cellular Pharmacology Department, §Drug Metabolism and Pharmacokinetics
Department, and ∥Neuroscience Department, Genentech, Inc., 1 DNA Way, South San Francisco, California 94080, United States
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Norman RA, Toader D, Ferguson AD. Structural approaches to obtain kinase selectivity. Trends Pharmacol Sci 2012; 33:273-8. [PMID: 22503441 DOI: 10.1016/j.tips.2012.03.005] [Citation(s) in RCA: 58] [Impact Index Per Article: 4.8] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/09/2012] [Revised: 03/07/2012] [Accepted: 03/08/2012] [Indexed: 11/25/2022]
Abstract
One of the grand challenges in kinase drug discovery is the design of small-molecule inhibitors with selectivity profiles that will ultimately be efficacious in the clinic. Current medicinal chemistry strategies make heavy use of structural, biophysical and computational approaches to achieve this multi-faceted goal. Here we review structure-based approaches underlying the development of several molecules that are currently in clinical trials, including the cMet inhibitor ARQ197 and the Bcr-Abl inhibitor ponatinib. We highlight the challenge posed by the emergence of resistance mutants and discuss promising lead generation strategies to obtain selective inhibitors of protein and lipid kinases such as targeting of specific sites, the use of fragment-based approaches and new chemical probes based on metal complexes.
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Affiliation(s)
- Richard A Norman
- Discovery Sciences, AstraZeneca, Mereside, Alderley Park, Macclesfield, Cheshire SK10 4TG, UK.
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50
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Martin E, Mukherjee P. Kinase-Kernel Models: Accurate In silico Screening of 4 Million Compounds Across the Entire Human Kinome. J Chem Inf Model 2012; 52:156-70. [DOI: 10.1021/ci200314j] [Citation(s) in RCA: 30] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/17/2022]
Affiliation(s)
- Eric Martin
- Oncology and Exploratory Chemistry, Global Discovery Chemistry, Novartis Institutes for Biomedical Research, 4560 Horton Street, Emeryville, California 94608, United States
| | - Prasenjit Mukherjee
- Oncology and Exploratory Chemistry, Global Discovery Chemistry, Novartis Institutes for Biomedical Research, 4560 Horton Street, Emeryville, California 94608, United States
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