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Wang K, Zhong F, Zhang ZD, Li HQ, Tian S. Recent advances in the development of P2Y 14R inhibitors: a patent and literature review (2018-present). Expert Opin Ther Pat 2024; 34:611-625. [PMID: 38889204 DOI: 10.1080/13543776.2024.2369634] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/18/2023] [Accepted: 06/12/2024] [Indexed: 06/20/2024]
Abstract
INTRODUCTION The P2Y14 receptor (P2Y14R), a member of the G protein-coupled receptor family, is activated by extracellular nucleotides. Due to its involvement in inflammatory, immunological and other associated processes, P2Y14R has emerged as a promising therapeutic target. Despite lacking a determined three-dimensional crystal structure, the homology modeling technique based on closely related P2Y receptors' crystallography has been extensively utilized for developing active compounds targeting P2Y14R. Recent discoveries have unveiled numerous highly effective and subtype-specific P2Y14R inhibitors. This study presents an overview of the latest advancements in P2Y14R inhibitors. AREAS COVERED This review presents an overview of the advancements in P2Y14R inhibitor research over the past five years, encompassing new patents, journal articles, and highlighting the therapeutic prospects inherent in these compounds. EXPERT OPINION The recent revelation of the vast potential of P2Y14R inhibitors has led to the development of novel compounds that exhibit promising capabilities for the treatment of sterile inflammation of the kidney, potentially diabetes, and asthma. Despite being a relatively nascent class of compounds, certain members have already exhibited their capacity to surmount specific challenges posed by conventional P2Y14R inhibitors. Targeting P2Y14R through small molecules may present a promising therapeutic strategy for effectively managing diverse inflammatory diseases.
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Affiliation(s)
- Kai Wang
- College of Pharmaceutical Sciences, Soochow University, Suzhou, China
| | - Fen Zhong
- College of Pharmaceutical Sciences, Soochow University, Suzhou, China
| | - Zhou-Dong Zhang
- College of Pharmaceutical Sciences, Soochow University, Suzhou, China
| | - Huan-Qiu Li
- College of Pharmaceutical Sciences, Soochow University, Suzhou, China
- Jiangsu Province Engineering Research Center of Precision Diagnostics and Therapeutics Development, Soochow University, Suzhou, China
| | - Sheng Tian
- College of Pharmaceutical Sciences, Soochow University, Suzhou, China
- Jiangsu Province Engineering Research Center of Precision Diagnostics and Therapeutics Development, Soochow University, Suzhou, China
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Vinod V, Maity S, Zaspel P, Kleinekathöfer U. Multifidelity Machine Learning for Molecular Excitation Energies. J Chem Theory Comput 2023; 19:7658-7670. [PMID: 37862054 DOI: 10.1021/acs.jctc.3c00882] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/21/2023]
Abstract
The accurate but fast calculation of molecular excited states is still a very challenging topic. For many applications, detailed knowledge of the energy funnel in larger molecular aggregates is of key importance, requiring highly accurate excitation energies. To this end, machine learning techniques can be a very useful tool, though the cost of generating highly accurate training data sets still remains a severe challenge. To overcome this hurdle, this work proposes the use of multifidelity machine learning where very little training data from high accuracies is combined with cheaper and less accurate data to achieve the accuracy of the costlier level. In the present study, the approach is employed to predict vertical excitation energies to the first excited state for three molecules of increasing size, namely, benzene, naphthalene, and anthracene. The energies are trained and tested for conformations stemming from classical molecular dynamics and density functional based tight-binding simulations. It can be shown that the multifidelity machine learning model can achieve the same accuracy as a machine learning model built only on high-cost training data while expending a much lower computational effort to generate the data. The numerical gain observed in these benchmark test calculations was over a factor of 30 but certainly can be much higher for high-accuracy data.
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Affiliation(s)
- Vivin Vinod
- School of Mathematics and Natural Science, University of Wuppertal, Wuppertal 42119, Germany
- School of Computer Science and Engineering, Constructor University, Campus Ring 1, Bremen 28759, Germany
| | - Sayan Maity
- School of Science, Constructor University, Campus Ring 1, Bremen 28759, Germany
| | - Peter Zaspel
- School of Mathematics and Natural Science, University of Wuppertal, Wuppertal 42119, Germany
- School of Computer Science and Engineering, Constructor University, Campus Ring 1, Bremen 28759, Germany
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Vandermause J, Xie Y, Lim JS, Owen CJ, Kozinsky B. Active learning of reactive Bayesian force fields applied to heterogeneous catalysis dynamics of H/Pt. Nat Commun 2022; 13:5183. [PMID: 36055982 PMCID: PMC9440250 DOI: 10.1038/s41467-022-32294-0] [Citation(s) in RCA: 24] [Impact Index Per Article: 12.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/16/2021] [Accepted: 07/21/2022] [Indexed: 11/08/2022] Open
Abstract
Atomistic modeling of chemically reactive systems has so far relied on either expensive ab initio methods or bond-order force fields requiring arduous parametrization. Here, we describe a Bayesian active learning framework for autonomous "on-the-fly" training of fast and accurate reactive many-body force fields during molecular dynamics simulations. At each time-step, predictive uncertainties of a sparse Gaussian process are evaluated to automatically determine whether additional ab initio training data are needed. We introduce a general method for mapping trained kernel models onto equivalent polynomial models whose prediction cost is much lower and independent of the training set size. As a demonstration, we perform direct two-phase simulations of heterogeneous H2 turnover on the Pt(111) catalyst surface at chemical accuracy. The model trains itself in three days and performs at twice the speed of a ReaxFF model, while maintaining much higher fidelity to DFT and excellent agreement with experiment.
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Affiliation(s)
- Jonathan Vandermause
- Department of Physics, Harvard University, Cambridge, MA, 02138, USA.
- John A. Paulson School of Engineering and Applied Sciences, Harvard University, Cambridge, MA, 02138, USA.
| | - Yu Xie
- John A. Paulson School of Engineering and Applied Sciences, Harvard University, Cambridge, MA, 02138, USA
| | - Jin Soo Lim
- Department of Chemistry and Chemical Biology, Harvard University, Cambridge, MA, 02138, USA
| | - Cameron J Owen
- Department of Chemistry and Chemical Biology, Harvard University, Cambridge, MA, 02138, USA
| | - Boris Kozinsky
- John A. Paulson School of Engineering and Applied Sciences, Harvard University, Cambridge, MA, 02138, USA.
- Robert Bosch LLC, Research and Technology Center, Cambridge, MA, 02139, USA.
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Atz K, Isert C, Böcker MNA, Jiménez-Luna J, Schneider G. Δ-Quantum machine-learning for medicinal chemistry. Phys Chem Chem Phys 2022; 24:10775-10783. [PMID: 35470831 PMCID: PMC9093086 DOI: 10.1039/d2cp00834c] [Citation(s) in RCA: 16] [Impact Index Per Article: 8.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/19/2022] [Accepted: 04/05/2022] [Indexed: 11/21/2022]
Abstract
Many molecular design tasks benefit from fast and accurate calculations of quantum-mechanical (QM) properties. However, the computational cost of QM methods applied to drug-like molecules currently renders large-scale applications of quantum chemistry challenging. Aiming to mitigate this problem, we developed DelFTa, an open-source toolbox for the prediction of electronic properties of drug-like molecules at the density functional (DFT) level of theory, using Δ-machine-learning. Δ-Learning corrects the prediction error (Δ) of a fast but inaccurate property calculation. DelFTa employs state-of-the-art three-dimensional message-passing neural networks trained on a large dataset of QM properties. It provides access to a wide array of quantum observables on the molecular, atomic and bond levels by predicting approximations to DFT values from a low-cost semiempirical baseline. Δ-Learning outperformed its direct-learning counterpart for most of the considered QM endpoints. The results suggest that predictions for non-covalent intra- and intermolecular interactions can be extrapolated to larger biomolecular systems. The software is fully open-sourced and features documented command-line and Python APIs.
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Affiliation(s)
- Kenneth Atz
- ETH Zurich, Department of Chemistry and Applied Biosciences, Vladimir-Prelog-Weg 4, 8093 Zurich, Switzerland.
| | - Clemens Isert
- ETH Zurich, Department of Chemistry and Applied Biosciences, Vladimir-Prelog-Weg 4, 8093 Zurich, Switzerland.
| | - Markus N A Böcker
- ETH Zurich, Department of Chemistry and Applied Biosciences, Vladimir-Prelog-Weg 4, 8093 Zurich, Switzerland.
| | - José Jiménez-Luna
- ETH Zurich, Department of Chemistry and Applied Biosciences, Vladimir-Prelog-Weg 4, 8093 Zurich, Switzerland.
- Department of Medicinal Chemistry, Boehringer Ingelheim Pharma GmbH & Co. KG, Birkendorfer Straße 65, 88397 Biberach an der Riss, Germany
| | - Gisbert Schneider
- ETH Zurich, Department of Chemistry and Applied Biosciences, Vladimir-Prelog-Weg 4, 8093 Zurich, Switzerland.
- ETH Singapore SEC Ltd., 1 CREATE Way, #06-01 CREATE Tower, Singapore 138602, Singapore
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Zhang R, Li X, Zhang X, Qin H, Xiao W. Machine learning approaches for elucidating the biological effects of natural products. Nat Prod Rep 2021; 38:346-361. [PMID: 32869826 DOI: 10.1039/d0np00043d] [Citation(s) in RCA: 42] [Impact Index Per Article: 14.0] [Reference Citation Analysis] [Abstract] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/15/2022]
Abstract
Covering: 2000 to 2020 Machine learning (ML) is an efficient tool for the prediction of bioactivity and the study of structure-activity relationships. Over the past decade, an emerging trend for combining these approaches with the study of natural products (NPs) has developed in order to manage the challenge of the discovery of bioactive NPs. In the present review, we will introduce the basic principles and protocols for using the ML approach to investigate the bioactivity of NPs, citing a series of practical examples regarding the study of anti-microbial, anti-cancer, and anti-inflammatory NPs, etc. ML algorithms manage a variety of classification and regression problems associated with bioactive NPs, from those that are linear to non-linear and from pure compounds to plant extracts. Inspired by cases reported in the literature and our own experience, a number of key points have been emphasized for reducing modeling errors, including dataset preparation and applicability domain analysis.
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Affiliation(s)
- Ruihan Zhang
- Key Laboratory of Medicinal Chemistry for Natural Resource, Ministry of Education, Yunnan Research & Development Center for Natural Products, School of Chemical Science and Technology, Yunnan University, 2 Rd Cuihubei, P. R. China.
| | - Xiaoli Li
- Key Laboratory of Medicinal Chemistry for Natural Resource, Ministry of Education, Yunnan Research & Development Center for Natural Products, School of Chemical Science and Technology, Yunnan University, 2 Rd Cuihubei, P. R. China.
| | - Xingjie Zhang
- Key Laboratory of Medicinal Chemistry for Natural Resource, Ministry of Education, Yunnan Research & Development Center for Natural Products, School of Chemical Science and Technology, Yunnan University, 2 Rd Cuihubei, P. R. China.
| | - Huayan Qin
- Key Laboratory of Medicinal Chemistry for Natural Resource, Ministry of Education, Yunnan Research & Development Center for Natural Products, School of Chemical Science and Technology, Yunnan University, 2 Rd Cuihubei, P. R. China.
| | - Weilie Xiao
- Key Laboratory of Medicinal Chemistry for Natural Resource, Ministry of Education, Yunnan Research & Development Center for Natural Products, School of Chemical Science and Technology, Yunnan University, 2 Rd Cuihubei, P. R. China.
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6
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Alberto AVP, da Silva Ferreira NC, Soares RF, Alves LA. Molecular Modeling Applied to the Discovery of New Lead Compounds for P2 Receptors Based on Natural Sources. Front Pharmacol 2020; 11:01221. [PMID: 33117147 PMCID: PMC7553047 DOI: 10.3389/fphar.2020.01221] [Citation(s) in RCA: 6] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Key Words] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/17/2020] [Accepted: 07/27/2020] [Indexed: 12/24/2022] Open
Abstract
P2 receptors are a family of transmembrane receptors activated by nucleotides and nucleosides. Two classes have been described in mammals, P2X and P2Y, which are implicated in various diseases. Currently, only P2Y12 has medicines approved for clinical use as antiplatelet agents and natural products have emerged as a source of new drugs with action on P2 receptors due to the diversity of chemical structures. In drug discovery, in silico virtual screening (VS) techniques have become popular because they have numerous advantages, which include the evaluation of thousands of molecules against a target, usually proteins, faster and cheaper than classical high throughput screening (HTS). The number of studies using VS techniques has been growing in recent years and has led to the discovery of new molecules of natural origin with action on different P2X and P2Y receptors. Using different algorithms it is possible to obtain information on absorption, distribution, metabolism, toxicity, as well as predictions on biological activity and the lead-likeness of the selected hits. Selected biomolecules may then be tested by molecular dynamics and, if necessary, rationally designed or modified to improve their interaction for the target. The algorithms of these in silico tools are being improved to permit the precision development of new drugs and, in the future, this process will take the front of drug development against some central nervous system (CNS) disorders. Therefore, this review discusses the methodologies of in silico tools concerning P2 receptors, as well as future perspectives and discoveries, such as the employment of artificial intelligence in drug discovery.
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Affiliation(s)
- Anael Viana Pinto Alberto
- Laboratory of Cellular Communication, Oswaldo Cruz Institute, Oswaldo Cruz Foundation, Rio de Janeiro, Brazil
| | | | - Rafael Ferreira Soares
- Laboratory of Functional Genomics and Bioinformatics, Oswaldo Cruz Institute, Oswaldo Cruz Foundation, Rio de Janeiro, Brazil
| | - Luiz Anastacio Alves
- Laboratory of Cellular Communication, Oswaldo Cruz Institute, Oswaldo Cruz Foundation, Rio de Janeiro, Brazil
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Capecchi A, Reymond JL. Assigning the Origin of Microbial Natural Products by Chemical Space Map and Machine Learning. Biomolecules 2020; 10:E1385. [PMID: 32998475 PMCID: PMC7600738 DOI: 10.3390/biom10101385] [Citation(s) in RCA: 9] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/02/2020] [Revised: 09/22/2020] [Accepted: 09/25/2020] [Indexed: 12/20/2022] Open
Abstract
Microbial natural products (NPs) are an important source of drugs, however, their structural diversity remains poorly understood. Here we used our recently reported MinHashed Atom Pair fingerprint with diameter of four bonds (MAP4), a fingerprint suitable for molecules across very different sizes, to analyze the Natural Products Atlas (NPAtlas), a database of 25,523 NPs of bacterial or fungal origin. To visualize NPAtlas by MAP4 similarity, we used the dimensionality reduction method tree map (TMAP). The resulting interactive map organizes molecules by physico-chemical properties and compound families such as peptides and glycosides. Remarkably, the map separates bacterial and fungal NPs from one another, revealing that these two compound families are intrinsically different despite their related biosynthetic pathways. We used these differences to train a machine learning model capable of distinguishing between NPs of bacterial or fungal origin.
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Affiliation(s)
| | - Jean-Louis Reymond
- Department of Chemistry and Biochemistry, University of Bern, Freiestrasse 3, 3012 Bern, Switzerland;
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Abstract
As the quantum chemistry (QC) community embraces machine learning (ML), the number of new methods and applications based on the combination of QC and ML is surging. In this Perspective, a view of the current state of affairs in this new and exciting research field is offered, challenges of using machine learning in quantum chemistry applications are described, and potential future developments are outlined. Specifically, examples of how machine learning is used to improve the accuracy and accelerate quantum chemical research are shown. Generalization and classification of existing techniques are provided to ease the navigation in the sea of literature and to guide researchers entering the field. The emphasis of this Perspective is on supervised machine learning.
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Affiliation(s)
- Pavlo O Dral
- State Key Laboratory of Physical Chemistry of Solid Surfaces, Fujian Provincial Key Laboratory of Theoretical and Computational Chemistry, Department of Chemistry, and College of Chemistry and Chemical Engineering, Xiamen University, Xiamen 361005, China
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9
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Zeng X, Zhang P, He W, Qin C, Chen S, Tao L, Wang Y, Tan Y, Gao D, Wang B, Chen Z, Chen W, Jiang YY, Chen YZ. NPASS: natural product activity and species source database for natural product research, discovery and tool development. Nucleic Acids Res 2019; 46:D1217-D1222. [PMID: 29106619 PMCID: PMC5753227 DOI: 10.1093/nar/gkx1026] [Citation(s) in RCA: 136] [Impact Index Per Article: 27.2] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/15/2017] [Accepted: 10/18/2017] [Indexed: 01/22/2023] Open
Abstract
There has been renewed interests in the exploration of natural products (NPs) for drug discovery, and continuous investigations of the therapeutic claims and mechanisms of traditional and herbal medicines. In-silico methods have been employed for facilitating these studies. These studies and the optimization of in-silico algorithms for NP applications can be facilitated by the quantitative activity and species source data of the NPs. A number of databases collectively provide the structural and other information of ∼470 000 NPs, including qualitative activity information for many NPs, but only ∼4000 NPs are with the experimental activity values. There is a need for the activity and species source data of more NPs. We therefore developed a new database, NPASS (Natural Product Activity and Species Source) to complement other databases by providing the experimental activity values and species sources of 35 032 NPs from 25 041 species targeting 5863 targets (2946 proteins, 1352 microbial species and 1227 cell-lines). NPASS contains 446 552 quantitative activity records (e.g. IC50, Ki, EC50, GI50 or MIC mainly in units of nM) of 222 092 NP-target pairs and 288 002 NP-species pairs. NPASS, http://bidd2.nus.edu.sg/NPASS/, is freely accessible with its contents searchable by keywords, physicochemical property range, structural similarity, species and target search facilities.
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Affiliation(s)
- Xian Zeng
- Breeding Base-Shenzhen Key Laboratory of Chemical Biology, the Graduate School at Shenzhen, Tsinghua University, Shenzhen Kivita Innovative Drug Discovery Institute, Shenzhen 518055, PR China.,Bioinformatics and Drug Design group, Department of Pharmacy, National University of Singapore, Singapore 117543, Singapore
| | - Peng Zhang
- Bioinformatics and Drug Design group, Department of Pharmacy, National University of Singapore, Singapore 117543, Singapore
| | - Weidong He
- Bioinformatics and Drug Design group, Department of Pharmacy, National University of Singapore, Singapore 117543, Singapore
| | - Chu Qin
- Bioinformatics and Drug Design group, Department of Pharmacy, National University of Singapore, Singapore 117543, Singapore
| | - Shangying Chen
- Bioinformatics and Drug Design group, Department of Pharmacy, National University of Singapore, Singapore 117543, Singapore
| | - Lin Tao
- Bioinformatics and Drug Design group, Department of Pharmacy, National University of Singapore, Singapore 117543, Singapore.,Zhejiang Key Laboratory of Gastro-intestinal Pathophysiology, Zhejiang Hospital of Traditional Chinese Medicine, Zhejiang Chinese Medical University, School of Medicine, Hangzhou Normal University, Hangzhou 310006, RP China
| | - Yali Wang
- Bioinformatics and Drug Design group, Department of Pharmacy, National University of Singapore, Singapore 117543, Singapore
| | - Ying Tan
- Breeding Base-Shenzhen Key Laboratory of Chemical Biology, the Graduate School at Shenzhen, Tsinghua University, Shenzhen Kivita Innovative Drug Discovery Institute, Shenzhen 518055, PR China
| | - Dan Gao
- Breeding Base-Shenzhen Key Laboratory of Chemical Biology, the Graduate School at Shenzhen, Tsinghua University, Shenzhen Kivita Innovative Drug Discovery Institute, Shenzhen 518055, PR China
| | - Bohua Wang
- Key Lab of Agricultural Products Processing and Quality Control of Nanchang City, Jiangxi Agricultural University, Nanchang 330045, PR China.,College of Life and Environmental Sciences, Collaborative Innovation Center for Efficient and Health Production of Fisheries in Hunan Province, Hunan University of Arts and Science, Changde, Hunan 415000, PR China
| | - Zhe Chen
- Zhejiang Key Laboratory of Gastro-intestinal Pathophysiology, Zhejiang Hospital of Traditional Chinese Medicine, Zhejiang Chinese Medical University, School of Medicine, Hangzhou Normal University, Hangzhou 310006, RP China
| | - Weiping Chen
- Key Lab of Agricultural Products Processing and Quality Control of Nanchang City, Jiangxi Agricultural University, Nanchang 330045, PR China
| | - Yu Yang Jiang
- Breeding Base-Shenzhen Key Laboratory of Chemical Biology, the Graduate School at Shenzhen, Tsinghua University, Shenzhen Kivita Innovative Drug Discovery Institute, Shenzhen 518055, PR China
| | - Yu Zong Chen
- Breeding Base-Shenzhen Key Laboratory of Chemical Biology, the Graduate School at Shenzhen, Tsinghua University, Shenzhen Kivita Innovative Drug Discovery Institute, Shenzhen 518055, PR China.,Bioinformatics and Drug Design group, Department of Pharmacy, National University of Singapore, Singapore 117543, Singapore
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10
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A Toolbox for the Identification of Modes of Action of Natural Products. PROGRESS IN THE CHEMISTRY OF ORGANIC NATURAL PRODUCTS 110 2019; 110:73-97. [DOI: 10.1007/978-3-030-14632-0_3] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.4] [Reference Citation Analysis] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 12/13/2022]
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11
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Abstract
Abstract
The biological pre-validation of natural products (NPs) and their underlying frameworks ensures an unrivaled source of inspiration for chemical probe and drug design. However, the poor knowledge of their drug target counterparts critically hinders the broader exploration of NPs in chemical biology and molecular medicine. Cutting-edge algorithms now provide powerful means for the target deconvolution of phenotypic screen hits and generate motivated research hypotheses. Herein, we present recent progress in artificial intelligence applied to target identification that may accelerate future NP-inspired molecular medicine.
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12
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Uteva E, Graham RS, Wilkinson RD, Wheatley RJ. Active learning in Gaussian process interpolation of potential energy surfaces. J Chem Phys 2018; 149:174114. [DOI: 10.1063/1.5051772] [Citation(s) in RCA: 30] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/30/2022] Open
Affiliation(s)
- Elena Uteva
- School of Chemistry, University of Nottingham, Nottingham NG7 2RD, United Kingdom
| | - Richard S. Graham
- School of Mathematical Sciences, University of Nottingham, Nottingham NG7 2RD, United Kingdom
| | - Richard D. Wilkinson
- School of Mathematics and Statistics, University of Sheffield, Sheffield S10 2TN, United Kingdom
| | - Richard J. Wheatley
- School of Chemistry, University of Nottingham, Nottingham NG7 2RD, United Kingdom
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13
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Uteva E, Graham RS, Wilkinson RD, Wheatley RJ. Interpolation of intermolecular potentials using Gaussian processes. J Chem Phys 2017; 147:161706. [DOI: 10.1063/1.4986489] [Citation(s) in RCA: 34] [Impact Index Per Article: 4.9] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/21/2022] Open
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14
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Cheminformatics-aided discovery of small-molecule Protein-Protein Interaction (PPI) dual inhibitors of Tumor Necrosis Factor (TNF) and Receptor Activator of NF-κB Ligand (RANKL). PLoS Comput Biol 2017; 13:e1005372. [PMID: 28426652 PMCID: PMC5398486 DOI: 10.1371/journal.pcbi.1005372] [Citation(s) in RCA: 44] [Impact Index Per Article: 6.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/08/2016] [Accepted: 01/17/2017] [Indexed: 12/20/2022] Open
Abstract
We present an in silico drug discovery pipeline developed and applied for the identification and virtual screening of small-molecule Protein-Protein Interaction (PPI) compounds that act as dual inhibitors of TNF and RANKL through the trimerization interface. The cheminformatics part of the pipeline was developed by combining structure-based with ligand-based modeling using the largest available set of known TNF inhibitors in the literature (2481 small molecules). To facilitate virtual screening, the consensus predictive model was made freely available at: http://enalos.insilicotox.com/TNFPubChem/. We thus generated a priority list of nine small molecules as candidates for direct TNF function inhibition. In vitro evaluation of these compounds led to the selection of two small molecules that act as potent direct inhibitors of TNF function, with IC50 values comparable to those of a previously-described direct inhibitor (SPD304), but with significantly reduced toxicity. These molecules were also identified as RANKL inhibitors and validated in vitro with respect to this second functionality. Direct binding of the two compounds was confirmed both for TNF and RANKL, as well as their ability to inhibit the biologically-active trimer forms. Molecular dynamics calculations were also carried out for the two small molecules in each protein to offer additional insight into the interactions that govern TNF and RANKL complex formation. To our knowledge, these compounds, namely T8 and T23, constitute the second and third published examples of dual small-molecule direct function inhibitors of TNF and RANKL, and could serve as lead compounds for the development of novel treatments for inflammatory and autoimmune diseases.
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Rodrigues T, Reker D, Schneider P, Schneider G. Counting on natural products for drug design. Nat Chem 2016; 8:531-41. [PMID: 27219696 DOI: 10.1038/nchem.2479] [Citation(s) in RCA: 756] [Impact Index Per Article: 94.5] [Reference Citation Analysis] [Abstract] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/20/2015] [Accepted: 02/12/2016] [Indexed: 02/08/2023]
Abstract
Natural products and their molecular frameworks have a long tradition as valuable starting points for medicinal chemistry and drug discovery. Recently, there has been a revitalization of interest in the inclusion of these chemotypes in compound collections for screening and achieving selective target modulation. Here we discuss natural-product-inspired drug discovery with a focus on recent advances in the design of synthetically tractable small molecules that mimic nature's chemistry. We highlight the potential of innovative computational tools in processing structurally complex natural products to predict their macromolecular targets and attempt to forecast the role that natural-product-derived fragments and fragment-like natural products will play in next-generation drug discovery.
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Affiliation(s)
- Tiago Rodrigues
- Swiss Federal Institute of Technology (ETH), Department of Chemistry and Applied Biosciences, Institute of Pharmaceutical Sciences, Vladimir-Prelog-Weg 4, 8093 Zürich, Switzerland
| | - Daniel Reker
- Swiss Federal Institute of Technology (ETH), Department of Chemistry and Applied Biosciences, Institute of Pharmaceutical Sciences, Vladimir-Prelog-Weg 4, 8093 Zürich, Switzerland
| | - Petra Schneider
- Swiss Federal Institute of Technology (ETH), Department of Chemistry and Applied Biosciences, Institute of Pharmaceutical Sciences, Vladimir-Prelog-Weg 4, 8093 Zürich, Switzerland.,inSili.com LLC, Segantinisteig 3, 8049 Zürich, Switzerland
| | - Gisbert Schneider
- Swiss Federal Institute of Technology (ETH), Department of Chemistry and Applied Biosciences, Institute of Pharmaceutical Sciences, Vladimir-Prelog-Weg 4, 8093 Zürich, Switzerland
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16
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Beck DAC, Carothers JM, Subramanian VR, Pfaendtner J. Data science: Accelerating innovation and discovery in chemical engineering. AIChE J 2016. [DOI: 10.1002/aic.15192] [Citation(s) in RCA: 49] [Impact Index Per Article: 6.1] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/24/2023]
Affiliation(s)
- David A. C. Beck
- Department of Chemical Engineering; University of Washington; Seattle WA
- eScience Institute, University of Washington; Seattle WA
| | - James M. Carothers
- Department of Chemical Engineering; University of Washington; Seattle WA
| | | | - Jim Pfaendtner
- Department of Chemical Engineering; University of Washington; Seattle WA
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Abstract
Computational medicinal chemistry offers viable strategies for finding, characterizing, and optimizing innovative pharmacologically active compounds. Technological advances in both computer hardware and software as well as biological chemistry have enabled a renaissance of computer-assisted "de novo" design of molecules with desired pharmacological properties. Here, we present our current perspective on the concept of automated molecule generation by highlighting chemocentric methods that may capture druglike chemical space, consider ligand promiscuity for hit and lead finding, and provide fresh ideas for the rational design of customized screening of compound libraries.
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Affiliation(s)
- Petra Schneider
- Department of Chemistry and Applied Biosciences, Institute of Pharmaceutical Sciences, Swiss Federal Institute of Technology (ETH) , Vladimir-Prelog-Weg 4, 8093 Zürich, Switzerland.,inSili.com LLC , Segantinisteig 3, 8049 Zürich, Switzerland
| | - Gisbert Schneider
- Department of Chemistry and Applied Biosciences, Institute of Pharmaceutical Sciences, Swiss Federal Institute of Technology (ETH) , Vladimir-Prelog-Weg 4, 8093 Zürich, Switzerland
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18
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Ain QU, Aleksandrova A, Roessler FD, Ballester PJ. Machine-learning scoring functions to improve structure-based binding affinity prediction and virtual screening. WILEY INTERDISCIPLINARY REVIEWS. COMPUTATIONAL MOLECULAR SCIENCE 2015; 5:405-424. [PMID: 27110292 PMCID: PMC4832270 DOI: 10.1002/wcms.1225] [Citation(s) in RCA: 190] [Impact Index Per Article: 21.1] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 06/03/2015] [Revised: 07/17/2015] [Accepted: 07/18/2015] [Indexed: 12/29/2022]
Abstract
Docking tools to predict whether and how a small molecule binds to a target can be applied if a structural model of such target is available. The reliability of docking depends, however, on the accuracy of the adopted scoring function (SF). Despite intense research over the years, improving the accuracy of SFs for structure-based binding affinity prediction or virtual screening has proven to be a challenging task for any class of method. New SFs based on modern machine-learning regression models, which do not impose a predetermined functional form and thus are able to exploit effectively much larger amounts of experimental data, have recently been introduced. These machine-learning SFs have been shown to outperform a wide range of classical SFs at both binding affinity prediction and virtual screening. The emerging picture from these studies is that the classical approach of using linear regression with a small number of expert-selected structural features can be strongly improved by a machine-learning approach based on nonlinear regression allied with comprehensive data-driven feature selection. Furthermore, the performance of classical SFs does not grow with larger training datasets and hence this performance gap is expected to widen as more training data becomes available in the future. Other topics covered in this review include predicting the reliability of a SF on a particular target class, generating synthetic data to improve predictive performance and modeling guidelines for SF development. WIREs Comput Mol Sci 2015, 5:405-424. doi: 10.1002/wcms.1225 For further resources related to this article, please visit the WIREs website.
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Affiliation(s)
- Qurrat Ul Ain
- Department of Chemistry, Centre for Molecular Informatics University of Cambridge Cambridge UK
| | | | - Florian D Roessler
- Department of Chemistry, Centre for Molecular Informatics University of Cambridge Cambridge UK
| | - Pedro J Ballester
- Cancer Research Center of Marseille, (INSERM U1068, Institut Paoli-Calmettes, Aix-Marseille Université, CNRS UMR7258) Marseille France
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Juárez-Jiménez J, Barril X, Orozco M, Pouplana R, Luque FJ. Assessing the suitability of the multilevel strategy for the conformational analysis of small ligands. J Phys Chem B 2014; 119:1164-72. [PMID: 25319869 DOI: 10.1021/jp506779y] [Citation(s) in RCA: 15] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/28/2022]
Abstract
Predicting the conformational preferences of flexible compounds is a challenging problem in drug design, where the recognition between ligand and receptor is affected by the ability of the interacting partners to adopt a favorable conformation for the binding. To explore the conformational space of flexible ligands and to obtain the relative free energy of the conformation wells, we have recently reported a multilevel computational strategy that relies on the predominant-state approximation-where the conformational space is partitioned into distinct conformational wells-and combines a low-level method for sampling the conformational minima and high-level ab initio calculations for estimating their relative stability. In this study, we assess the performance of the multilevel strategy for predicting the conformational preferences of a series of structurally related phenylethylamines and streptomycin in aqueous solution. The charged nature of these compounds and the chemical complexity of streptomycin make them a challenging test for the multilevel approach. Furthermore, we explore the suitability of using a molecular mechanics approach as a source of approximate ensembles in the first stage of the multilevel strategy. The results support the reliability of the multilevel approach for obtaining an accurate conformational ensemble of small (bio)organic molecules in aqueous solution.
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Affiliation(s)
- Jordi Juárez-Jiménez
- Departament de Fisicoquímica and Institut de Biomedicina (IBUB), Facultat de Farmàcia, Universitat de Barcelona , Avda. Prat de la Riba, 171, 08921 Santa Coloma de Gramenet, Spain
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Abstract
The computer-assisted generation of new chemical entities (NCEs) has matured into solid technology supporting early drug discovery. Both ligand- and receptor-based methods are increasingly used for designing small lead- and druglike molecules with anticipated multi-target activities. Advanced "polypharmacology" prediction tools are essential pillars of these endeavors. In addition, it has been realized that iterative design-synthesis-test cycles facilitate the rapid identification of NCEs with the desired activity profile. Lab-on-a-chip platforms integrating synthesis, analytics and bioactivity determination and controlled by adaptive, chemistry-driven de novo design software will play an important role for future drug discovery.
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Affiliation(s)
- Gisbert Schneider
- Swiss Federal Institute of Technology (ETH), Department of Chemistry and Applied Biosciences, Vladimir-Prelog-Weg 4, 8093 Zürich, Switzerland.
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