The Experts below are selected from a list of 27657 Experts worldwide ranked by ideXlab platform
C H Arrowsmith - One of the best experts on this subject based on the ideXlab platform.
-
discovery of a potent and selective coactivator associated arginine methyltransferase 1 carm1 inhibitor by Virtual Screening
Journal of Medicinal Chemistry, 2016Co-Authors: Renato Ferreira De Freitas, Magdalena M Szewczyk, Dalia Barsytelovejoy, Mohammad S Eram, David Smil, Steven Kennedy, Peter J Brown, V Santhakumar, C H ArrowsmithAbstract:Protein arginine methyltransferases (PRMTs) represent an emerging target class in oncology and other disease areas. So far, the most successful strategy to identify PRMT inhibitors has been to screen large to medium-size chemical libraries. Attempts to develop PRMT inhibitors using receptor-based computational methods have met limited success. Here, using Virtual Screening approaches, we identify 11 CARM1 (PRMT4) inhibitors with ligand efficiencies ranging from 0.28 to 0.84. CARM1 selective hits were further validated by orthogonal methods. Two structure-based rounds of optimization produced 27 (SGC2085), a CARM1 inhibitor with an IC50 of 50 nM and more than hundred-fold selectivity over other PRMTs. These results indicate that Virtual Screening strategies can be successfully applied to Rossmann-fold protein methyltransferases.
-
Discovery of a Potent and Selective Coactivator Associated Arginine Methyltransferase 1 (CARM1) Inhibitor by Virtual Screening
2016Co-Authors: Renato Ferreira De Freitas, Mohammad S Eram, David Smil, Steven Kennedy, Peter J Brown, V Santhakumar, C H Arrowsmith, Magdalena M. Szewczyk, Dalia Barsyte-lovejoy, Masoud VedadiAbstract:Protein arginine methyltransferases (PRMTs) represent an emerging target class in oncology and other disease areas. So far, the most successful strategy to identify PRMT inhibitors has been to screen large to medium-size chemical libraries. Attempts to develop PRMT inhibitors using receptor-based computational methods have met limited success. Here, using Virtual Screening approaches, we identify 11 CARM1 (PRMT4) inhibitors with ligand efficiencies ranging from 0.28 to 0.84. CARM1 selective hits were further validated by orthogonal methods. Two structure-based rounds of optimization produced 27 (SGC2085), a CARM1 inhibitor with an IC50 of 50 nM and more than hundred-fold selectivity over other PRMTs. These results indicate that Virtual Screening strategies can be successfully applied to Rossmann-fold protein methyltransferases
Changguo Zhan - One of the best experts on this subject based on the ideXlab platform.
-
identify potent sars cov 2 main protease inhibitors via accelerated free energy perturbation based Virtual Screening of existing drugs
Proceedings of the National Academy of Sciences of the United States of America, 2020Co-Authors: Yiyou Huang, Lei Zhang, Runduo Liu, Lingli Zhou, Yuxi Lin, Hao Liu, Yuxia Zhang, Jun Cui, Changguo ZhanAbstract:The COVID-19 pandemic caused by severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) has become a global crisis. There is no therapeutic treatment specific for COVID-19. It is highly desirable to identify potential antiviral agents against SARS-CoV-2 from existing drugs available for other diseases and thus repurpose them for treatment of COVID-19. In general, a drug repurposing effort for treatment of a new disease, such as COVID-19, usually starts from a Virtual Screening of existing drugs, followed by experimental validation, but the actual hit rate is generally rather low with traditional computational methods. Here we report a Virtual Screening approach with accelerated free energy perturbation-based absolute binding free energy (FEP-ABFE) predictions and its use in identifying drugs targeting SARS-CoV-2 main protease (Mpro). The accurate FEP-ABFE predictions were based on the use of a restraint energy distribution (RED) function, making the practical FEP-ABFE-based Virtual Screening of the existing drug library possible. As a result, out of 25 drugs predicted, 15 were confirmed as potent inhibitors of SARS-CoV-2 Mpro The most potent one is dipyridamole (inhibitory constant Ki = 0.04 µM) which has shown promising therapeutic effects in subsequently conducted clinical studies for treatment of patients with COVID-19. Additionally, hydroxychloroquine (Ki = 0.36 µM) and chloroquine (Ki = 0.56 µM) were also found to potently inhibit SARS-CoV-2 Mpro We anticipate that the FEP-ABFE prediction-based Virtual Screening approach will be useful in many other drug repurposing or discovery efforts.
-
identify potent sars cov 2 main protease inhibitors via accelerated free energy perturbation based Virtual Screening of existing drugs
bioRxiv, 2020Co-Authors: Yiyou Huang, Lei Zhang, Runduo Liu, Lingli Zhou, Yuxi Lin, Hao Liu, Yuxia Zhang, Jun Cui, Changguo ZhanAbstract:Abstract Coronavirus disease 2019 (COVID-19) pandemic caused by severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) has become a global crisis. There is no therapeutic treatment specific for COVID-19. It is highly desirable to identify potential antiviral agents against SARS-CoV-2 from existing drugs available for other diseases and, thus, repurpose them for treatment of COVID-19. In general, a drug repurposing effort for treatment of a new disease, such as COVID-19, usually starts from a Virtual Screening of existing drugs, followed by experimental validation, but the actual hit rate is generally rather low with traditional computational methods. Here we report a new Virtual Screening approach with accelerated free energy perturbation-based absolute binding free energy (FEP-ABFE) predictions and its use in identifying drugs targeting SARS-CoV-2 main protease (Mpro). The accurate FEP-ABFE predictions were based on the use of a new restraint energy distribution (RED) function designed to accelerate the FEP-ABFE calculations and make the practical FEP-ABFE-based Virtual Screening of the existing drug library possible for the first time. As a result, out of twenty-five drugs predicted, fifteen were confirmed as potent inhibitors of SARS-CoV-2 Mpro. The most potent one is dipyridamole (Ki=0.04 μM) which has showed promising therapeutic effects in subsequently conducted clinical studies for treatment of patients with COVID-19. Additionally, hydroxychloroquine (Ki=0.36 μM) and chloroquine (Ki=0.56 μM) were also found to potently inhibit SARS-CoV-2 Mpro for the first time. We anticipate that the FEP-ABFE prediction-based Virtual Screening approach will be useful in many other drug repurposing or discovery efforts. Significance Statement Drug repurposing effort for treatment of a new disease, such as COVID-19, usually starts from a Virtual Screening of existing drugs, followed by experimental validation, but the actual hit rate is generally rather low with traditional computational methods. It has been demonstrated that a new Virtual Screening approach with accelerated free energy perturbation-based absolute binding free energy (FEP-ABFE) predictions can reach an unprecedently high hit rate, leading to successful identification of 16 potent inhibitors of SARS-CoV-2 main protease (Mpro) from computationally selected 25 drugs under a threshold of Ki = 4 μM. The outcomes of this study are valuable for not only drug repurposing to treat COVID-19, but also demonstrating the promising potential of the FEP-ABFE prediction-based Virtual Screening approach.
Yuxi Lin - One of the best experts on this subject based on the ideXlab platform.
-
identify potent sars cov 2 main protease inhibitors via accelerated free energy perturbation based Virtual Screening of existing drugs
Proceedings of the National Academy of Sciences of the United States of America, 2020Co-Authors: Yiyou Huang, Lei Zhang, Runduo Liu, Lingli Zhou, Yuxi Lin, Hao Liu, Yuxia Zhang, Jun Cui, Changguo ZhanAbstract:The COVID-19 pandemic caused by severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) has become a global crisis. There is no therapeutic treatment specific for COVID-19. It is highly desirable to identify potential antiviral agents against SARS-CoV-2 from existing drugs available for other diseases and thus repurpose them for treatment of COVID-19. In general, a drug repurposing effort for treatment of a new disease, such as COVID-19, usually starts from a Virtual Screening of existing drugs, followed by experimental validation, but the actual hit rate is generally rather low with traditional computational methods. Here we report a Virtual Screening approach with accelerated free energy perturbation-based absolute binding free energy (FEP-ABFE) predictions and its use in identifying drugs targeting SARS-CoV-2 main protease (Mpro). The accurate FEP-ABFE predictions were based on the use of a restraint energy distribution (RED) function, making the practical FEP-ABFE-based Virtual Screening of the existing drug library possible. As a result, out of 25 drugs predicted, 15 were confirmed as potent inhibitors of SARS-CoV-2 Mpro The most potent one is dipyridamole (inhibitory constant Ki = 0.04 µM) which has shown promising therapeutic effects in subsequently conducted clinical studies for treatment of patients with COVID-19. Additionally, hydroxychloroquine (Ki = 0.36 µM) and chloroquine (Ki = 0.56 µM) were also found to potently inhibit SARS-CoV-2 Mpro We anticipate that the FEP-ABFE prediction-based Virtual Screening approach will be useful in many other drug repurposing or discovery efforts.
-
identify potent sars cov 2 main protease inhibitors via accelerated free energy perturbation based Virtual Screening of existing drugs
bioRxiv, 2020Co-Authors: Yiyou Huang, Lei Zhang, Runduo Liu, Lingli Zhou, Yuxi Lin, Hao Liu, Yuxia Zhang, Jun Cui, Changguo ZhanAbstract:Abstract Coronavirus disease 2019 (COVID-19) pandemic caused by severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) has become a global crisis. There is no therapeutic treatment specific for COVID-19. It is highly desirable to identify potential antiviral agents against SARS-CoV-2 from existing drugs available for other diseases and, thus, repurpose them for treatment of COVID-19. In general, a drug repurposing effort for treatment of a new disease, such as COVID-19, usually starts from a Virtual Screening of existing drugs, followed by experimental validation, but the actual hit rate is generally rather low with traditional computational methods. Here we report a new Virtual Screening approach with accelerated free energy perturbation-based absolute binding free energy (FEP-ABFE) predictions and its use in identifying drugs targeting SARS-CoV-2 main protease (Mpro). The accurate FEP-ABFE predictions were based on the use of a new restraint energy distribution (RED) function designed to accelerate the FEP-ABFE calculations and make the practical FEP-ABFE-based Virtual Screening of the existing drug library possible for the first time. As a result, out of twenty-five drugs predicted, fifteen were confirmed as potent inhibitors of SARS-CoV-2 Mpro. The most potent one is dipyridamole (Ki=0.04 μM) which has showed promising therapeutic effects in subsequently conducted clinical studies for treatment of patients with COVID-19. Additionally, hydroxychloroquine (Ki=0.36 μM) and chloroquine (Ki=0.56 μM) were also found to potently inhibit SARS-CoV-2 Mpro for the first time. We anticipate that the FEP-ABFE prediction-based Virtual Screening approach will be useful in many other drug repurposing or discovery efforts. Significance Statement Drug repurposing effort for treatment of a new disease, such as COVID-19, usually starts from a Virtual Screening of existing drugs, followed by experimental validation, but the actual hit rate is generally rather low with traditional computational methods. It has been demonstrated that a new Virtual Screening approach with accelerated free energy perturbation-based absolute binding free energy (FEP-ABFE) predictions can reach an unprecedently high hit rate, leading to successful identification of 16 potent inhibitors of SARS-CoV-2 main protease (Mpro) from computationally selected 25 drugs under a threshold of Ki = 4 μM. The outcomes of this study are valuable for not only drug repurposing to treat COVID-19, but also demonstrating the promising potential of the FEP-ABFE prediction-based Virtual Screening approach.
Jeanlouis Reymond - One of the best experts on this subject based on the ideXlab platform.
-
visualization and Virtual Screening of the chemical universe database gdb 17
Journal of Chemical Information and Modeling, 2013Co-Authors: Lars Ruddigkeit, Lorenz C Blum, Jeanlouis ReymondAbstract:The chemical universe database GDB-17 contains 166.4 billion molecules of up to 17 atoms of C, N, O, S, and halogens obeying rules for chemical stability, synthetic feasibility, and medicinal chemistry. GDB-17 was analyzed using 42 integer value descriptors of molecular structure which we term “Molecular Quantum Numbers” (MQN). Principal component analysis and representation of the (PC1, PC2)-plane provided a graphical overview of the GDB-17 chemical space. Rapid ligand-based Virtual Screening (LBVS) of GDB-17 using the city-block distance CBDMQN as a similarity search measure was enabled by a hashed MQN-fingerprint. LBVS of the entire GDB-17 and of selected subsets identified shape similar, scaffold hopping analogs (ROCS > 1.6 and TSF < 0.5) of 15 drugs. Over 97% of these analogs occurred within CBDMQN ≤ 12 from each drug, a constraint which might help focus advanced Virtual Screening. An MQN-searchable 50 million subset of GDB-17 is publicly available at www.gdb.unibe.ch.
-
discovery of α7 nicotinic receptor ligands by Virtual Screening of the chemical universe database gdb 13
Journal of Chemical Information and Modeling, 2011Co-Authors: Lorenz C Blum, Ruud Van Deursen, Sonia Bertrand, Milena Mayer, Justus J Burgi, Daniel Bertrand, Jeanlouis ReymondAbstract:The chemical universe database GDB-13 enumerates 977 million organic molecules up to 13 atoms of C, N, O, Cl, and S that are Virtually possible following simple rules for chemical stability and synthetic feasibility. Analogs of nicotine were identified in GDB-13 using the city-block distance in MQN-space (CBDMQN) as a similarity measure, combined with a restriction eliminating problematic structural elements. The search was carried out with a Web browser available at www.gdb.unibe.ch. This Virtual Screening procedure selected 31 504 analogs of nicotine from GDB-13, from which 48 were known nicotinic ligands reported in Chembl. An additional 60 Virtual Screening hits were purchased and tested for modulation of the acetylcholine signal at the human α7 nAChR expressed in Xenopus oocytes, which led to the identification of three previously unknown inhibitors. These experiments demonstrate for the first time the use of GDB-13 for ligand discovery.
Yiyou Huang - One of the best experts on this subject based on the ideXlab platform.
-
identify potent sars cov 2 main protease inhibitors via accelerated free energy perturbation based Virtual Screening of existing drugs
Proceedings of the National Academy of Sciences of the United States of America, 2020Co-Authors: Yiyou Huang, Lei Zhang, Runduo Liu, Lingli Zhou, Yuxi Lin, Hao Liu, Yuxia Zhang, Jun Cui, Changguo ZhanAbstract:The COVID-19 pandemic caused by severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) has become a global crisis. There is no therapeutic treatment specific for COVID-19. It is highly desirable to identify potential antiviral agents against SARS-CoV-2 from existing drugs available for other diseases and thus repurpose them for treatment of COVID-19. In general, a drug repurposing effort for treatment of a new disease, such as COVID-19, usually starts from a Virtual Screening of existing drugs, followed by experimental validation, but the actual hit rate is generally rather low with traditional computational methods. Here we report a Virtual Screening approach with accelerated free energy perturbation-based absolute binding free energy (FEP-ABFE) predictions and its use in identifying drugs targeting SARS-CoV-2 main protease (Mpro). The accurate FEP-ABFE predictions were based on the use of a restraint energy distribution (RED) function, making the practical FEP-ABFE-based Virtual Screening of the existing drug library possible. As a result, out of 25 drugs predicted, 15 were confirmed as potent inhibitors of SARS-CoV-2 Mpro The most potent one is dipyridamole (inhibitory constant Ki = 0.04 µM) which has shown promising therapeutic effects in subsequently conducted clinical studies for treatment of patients with COVID-19. Additionally, hydroxychloroquine (Ki = 0.36 µM) and chloroquine (Ki = 0.56 µM) were also found to potently inhibit SARS-CoV-2 Mpro We anticipate that the FEP-ABFE prediction-based Virtual Screening approach will be useful in many other drug repurposing or discovery efforts.
-
identify potent sars cov 2 main protease inhibitors via accelerated free energy perturbation based Virtual Screening of existing drugs
bioRxiv, 2020Co-Authors: Yiyou Huang, Lei Zhang, Runduo Liu, Lingli Zhou, Yuxi Lin, Hao Liu, Yuxia Zhang, Jun Cui, Changguo ZhanAbstract:Abstract Coronavirus disease 2019 (COVID-19) pandemic caused by severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) has become a global crisis. There is no therapeutic treatment specific for COVID-19. It is highly desirable to identify potential antiviral agents against SARS-CoV-2 from existing drugs available for other diseases and, thus, repurpose them for treatment of COVID-19. In general, a drug repurposing effort for treatment of a new disease, such as COVID-19, usually starts from a Virtual Screening of existing drugs, followed by experimental validation, but the actual hit rate is generally rather low with traditional computational methods. Here we report a new Virtual Screening approach with accelerated free energy perturbation-based absolute binding free energy (FEP-ABFE) predictions and its use in identifying drugs targeting SARS-CoV-2 main protease (Mpro). The accurate FEP-ABFE predictions were based on the use of a new restraint energy distribution (RED) function designed to accelerate the FEP-ABFE calculations and make the practical FEP-ABFE-based Virtual Screening of the existing drug library possible for the first time. As a result, out of twenty-five drugs predicted, fifteen were confirmed as potent inhibitors of SARS-CoV-2 Mpro. The most potent one is dipyridamole (Ki=0.04 μM) which has showed promising therapeutic effects in subsequently conducted clinical studies for treatment of patients with COVID-19. Additionally, hydroxychloroquine (Ki=0.36 μM) and chloroquine (Ki=0.56 μM) were also found to potently inhibit SARS-CoV-2 Mpro for the first time. We anticipate that the FEP-ABFE prediction-based Virtual Screening approach will be useful in many other drug repurposing or discovery efforts. Significance Statement Drug repurposing effort for treatment of a new disease, such as COVID-19, usually starts from a Virtual Screening of existing drugs, followed by experimental validation, but the actual hit rate is generally rather low with traditional computational methods. It has been demonstrated that a new Virtual Screening approach with accelerated free energy perturbation-based absolute binding free energy (FEP-ABFE) predictions can reach an unprecedently high hit rate, leading to successful identification of 16 potent inhibitors of SARS-CoV-2 main protease (Mpro) from computationally selected 25 drugs under a threshold of Ki = 4 μM. The outcomes of this study are valuable for not only drug repurposing to treat COVID-19, but also demonstrating the promising potential of the FEP-ABFE prediction-based Virtual Screening approach.
