43 results on '"Tkachenko, Valery"'
Search Results
2. Size doesn't matter: predicting physico- or biochemical properties based on dozens of molecules
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Karpov, Kirill, Mitrofanov, Artem, Korolev, Vadim, and Tkachenko, Valery
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Computer Science - Machine Learning - Abstract
The use of machine learning in chemistry has become a common practice. At the same time, despite the success of modern machine learning methods, the lack of data limits their use. Using a transfer learning methodology can help solve this problem. This methodology assumes that a model built on a sufficient amount of data captures general features of the chemical compound structure on which it was trained and that the further reuse of these features on a dataset with a lack of data will greatly improve the quality of the new model. In this paper, we develop this approach for small organic molecules, implementing transfer learning with graph convolutional neural networks. The paper shows a significant improvement in the performance of models for target properties with a lack of data. The effects of the dataset composition on model quality and the applicability domain of the resulting models are also considered., Comment: 9 pages, 6 figures more...
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- 2021
Catalog
3. Graph convolutional neural networks as 'general-purpose' property predictors: the universality and limits of applicability
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Korolev, Vadim, Mitrofanov, Artem, Korotcov, Alexandru, and Tkachenko, Valery
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Physics - Computational Physics - Abstract
Nowadays the development of new functional materials/chemical compounds using machine learning (ML) techniques is a hot topic and includes several crucial steps, one of which is the choice of chemical structure representation. Classical approach of rigorous feature engineering in ML typically improves the performance of the predictive model, but at the same time, it narrows down the scope of applicability and decreases the physical interpretability of predicted results. In this study, we present graph convolutional neural networks (GCNN) as an architecture that allows to successfully predict the properties of compounds from diverse domains of chemical space, using a minimal set of meaningful descriptors. The applicability of GCNN models has been demonstrated by a wide range of chemical domain-specific properties. Their performance is comparable to state-of-the-art techniques; however, this architecture exempts from the need to carry out precise feature engineering., Comment: 8 pages, 1 figure, 3 tables; added Acknowledgments more...
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- 2019
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4. Transferable and extensible machine learning derived atomic charges for modeling hybrid nanoporous materials
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Korolev, Vadim, Mitrofanov, Artem, Marchenko, Ekaterina, Eremin, Nickolay, Tkachenko, Valery, and Kalmykov, Stepan
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Physics - Computational Physics - Abstract
Nanoporous materials have attracted significant interest as an emerging platform for adsorption-related applications. The high-throughput computational screening became a standard technique to access the performance of thousands of candidates, but its accuracy is highly dependent on a partial charge assignment method. In this study, we propose a machine learning model that can reconcile the benefits of two main approaches-the high accuracy of density-derived electrostatic and chemical charge (DDEC) method and the scalability of charge equilibration (Qeq) method. The mean absolute deviation of predicted partial charges from the original DDEC counterparts archive an excellent level of 0.01e. The model, initially designed for metal-organic frameworks, is also capable of assigning charges to another class of nanoporous materials, covalent organic frameworks, with acceptable accuracy. Adsorption properties of carbon dioxide, calculated by means of machine learning derived charges, are consistent with the reference data obtained with DDEC charges., Comment: 13 pages, 9 figures more...
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- 2019
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5. Open-source QSAR models for pKa prediction using multiple machine learning approaches
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Mansouri, Kamel, Cariello, Neal F., Korotcov, Alexandru, Tkachenko, Valery, Grulke, Chris M., Sprankle, Catherine S., Allen, David, Casey, Warren M., Kleinstreuer, Nicole C., and Williams, Antony J.
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- 2019
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6. Programmatic conversion of crystal structures into 3D printable files using Jmol
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Scalfani, Vincent F., Williams, Antony J., Tkachenko, Valery, Karapetyan, Karen, Pshenichnov, Alexey, Hanson, Robert M., Liddie, Jahred M., and Bara, Jason E.
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- 2016
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7. The Chemical Validation and Standardization Platform (CVSP): large-scale automated validation of chemical structure datasets
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Karapetyan, Karen, Batchelor, Colin, Sharpe, David, Tkachenko, Valery, and Williams, Antony J
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- 2015
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8. The Royal Society of Chemistry and the delivery of chemistry data repositories for the community
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Williams, Antony and Tkachenko, Valery
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- 2014
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9. The ChEMBL database as linked open data
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Willighagen, Egon L, Waagmeester, Andra, Spjuth, Ola, Ansell, Peter, Williams, Antony J, Tkachenko, Valery, Hastings, Janna, Chen, Bin, and Wild, David J
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- 2013
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10. Erratum: CATMoS: Collaborative Acute Toxicity Modeling Suite
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Mansouri, Kamel, primary, Karmaus, Agnes L., additional, Fitzpatrick, Jeremy, additional, Patlewicz, Grace, additional, Pradeep, Prachi, additional, Alberga, Domenico, additional, Alepee, Nathalie, additional, Allen, Timothy E.H., additional, Allen, Dave, additional, Alves, Vinicius M., additional, Andrade, Carolina H., additional, Auernhammer, Tyler R., additional, Ballabio, Davide, additional, Bell, Shannon, additional, Benfenati, Emilio, additional, Bhattacharya, Sudin, additional, Bastos, Joyce V., additional, Boyd, Stephen, additional, Brown, J. B., additional, Capuzzi, Stephen J., additional, Chushak, Yaroslav, additional, Ciallella, Heather, additional, Clark, Alex M., additional, Consonni, Viviana, additional, Daga, Pankaj R., additional, Ekins, Sean, additional, Farag, Sherif, additional, Fedorov, Maxim, additional, Fourches, Denis, additional, Gadaleta, Domenico, additional, Gao, Feng, additional, Gearhart, Jeffery M., additional, Goh, Garett, additional, Goodman, Jonathan M., additional, Grisoni, Francesca, additional, Grulke, Christopher M., additional, Hartung, Thomas, additional, Hirn, Matthew, additional, Karpov, Pavel, additional, Korotcov, Alexandru, additional, Lavado, Giovanna J., additional, Lawless, Michael, additional, Li, Xinhao, additional, Luechtefeld, Thomas, additional, Lunghini, Filippo, additional, Mangiatordi, Giuseppe F., additional, Marcou, Gilles, additional, Marsh, Dan, additional, Martin, Todd, additional, Mauri, Andrea, additional, Muratov, Eugene N., additional, Myatt, Glenn J., additional, Nguyen, Dac-Trung, additional, Nicolotti, Orazio, additional, Note, Reine, additional, Pande, Paritosh, additional, Parks, Amanda K., additional, Peryea, Tyler, additional, Polash, Ahsan H., additional, Rallo, Robert, additional, Roncaglioni, Alessandra, additional, Rowlands, Craig, additional, Ruiz, Patricia, additional, Russo, Daniel P., additional, Sayed, Ahmed, additional, Sayre, Risa, additional, Sheils, Timothy, additional, Siegel, Charles, additional, Silva, Arthur C., additional, Simeonov, Anton, additional, Sosnin, Sergey, additional, Southall, Noel, additional, Strickland, Judy, additional, Tang, Yun, additional, Teppen, Brian, additional, Tetko, Igor V., additional, Thomas, Dennis, additional, Tkachenko, Valery, additional, Todeschini, Roberto, additional, Toma, Cosimo, additional, Tripodi, Ignacio, additional, Trisciuzzi, Daniela, additional, Tropsha, Alexander, additional, Varnek, Alexandre, additional, Vukovic, Kristijan, additional, Wang, Zhongyu, additional, Wang, Liguo, additional, Waters, Katrina M., additional, Wedlake, Andrew J., additional, Wijeyesakere, Sanjeeva J., additional, Wilson, Dan, additional, Xiao, Zijun, additional, Yang, Hongbin, additional, Zahoranszky-Kohalmi, Gergely, additional, Zakharov, Alexey V., additional, Zhang, Fagen F., additional, Zhang, Zhen, additional, Zhao, Tongan, additional, Zhu, Hao, additional, Zorn, Kimberley M., additional, Casey, Warren, additional, and Kleinstreuer, Nicole C., additional more...
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- 2021
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11. Online chemical modeling environment (OCHEM): web platform for data storage, model development and publishing of chemical information
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Sushko, Iurii, Novotarskyi, Sergii, Körner, Robert, Pandey, Anil Kumar, Rupp, Matthias, Teetz, Wolfram, Brandmaier, Stefan, Abdelaziz, Ahmed, Prokopenko, Volodymyr V., Tanchuk, Vsevolod Y., Todeschini, Roberto, Varnek, Alexandre, Marcou, Gilles, Ertl, Peter, Potemkin, Vladimir, Grishina, Maria, Gasteiger, Johann, Schwab, Christof, Baskin, Igor I., Palyulin, Vladimir A., Radchenko, Eugene V., Welsh, William J., Kholodovych, Vladyslav, Chekmarev, Dmitriy, Cherkasov, Artem, Aires-de-Sousa, Joao, Zhang, Qing-You, Bender, Andreas, Nigsch, Florian, Patiny, Luc, Williams, Antony, Tkachenko, Valery, and Tetko, Igor V. more...
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- 2011
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12. Automatic vs. manual curation of a multi-source chemical dictionary: the impact on text mining
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Hettne, Kristina M, Williams, Antony J, van Mulligen, Erik M, Kleinjans, Jos, Tkachenko, Valery, and Kors, Jan A
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- 2010
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13. CATMoS: Collaborative Acute Toxicity Modeling Suite
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Mansouri, K, Karmaus, A, Fitzpatrick, J, Patlewicz, G, Pradeep, P, Alberga, D, Alepee, N, Allen, T, Allen, D, Alves, V, Andrade, C, Auernhammer, T, Ballabio, D, Bell, S, Benfenati, E, Bhattacharya, S, Bastos, J, Boyd, S, Brown, J, Capuzzi, S, Chushak, Y, Ciallella, H, Clark, A, Consonni, V, Daga, P, Ekins, S, Farag, S, Fedorov, M, Fourches, D, Gadaleta, D, Gao, F, Gearhart, J, Goh, G, Goodman, J, Grisoni, F, Grulke, C, Hartung, T, Hirn, M, Karpov, P, Korotcov, A, Lavado, G, Lawless, M, Li, X, Luechtefeld, T, Lunghini, F, Mangiatordi, G, Marcou, G, Marsh, D, Martin, T, Mauri, A, Muratov, E, Myatt, G, Nguyen, D, Nicolotti, O, Note, R, Pande, P, Parks, A, Peryea, T, Polash, A, Rallo, R, Roncaglioni, A, Rowlands, C, Ruiz, P, Russo, D, Sayed, A, Sayre, R, Sheils, T, Siegel, C, Silva, A, Simeonov, A, Sosnin, S, Southall, N, Strickland, J, Tang, Y, Teppen, B, Tetko, I, Thomas, D, Tkachenko, V, Todeschini, R, Toma, C, Tripodi, I, Trisciuzzi, D, Tropsha, A, Varnek, A, Vukovic, K, Wang, Z, Wang, L, Waters, K, Wedlake, A, Wijeyesakere, S, Wilson, D, Xiao, Z, Yang, H, Zahoranszky-Kohalmi, G, Zakharov, A, Zhang, F, Zhang, Z, Zhao, T, Zhu, H, Zorn, K, Casey, W, Kleinstreuer, N, Mansouri, Kamel, Karmaus, Agnes L, Fitzpatrick, Jeremy, Patlewicz, Grace, Pradeep, Prachi, Alberga, Domenico, Alepee, Nathalie, Allen, Timothy E H, Allen, Dave, Alves, Vinicius M, Andrade, Carolina H, Auernhammer, Tyler R, Ballabio, Davide, Bell, Shannon, Benfenati, Emilio, Bhattacharya, Sudin, Bastos, Joyce V, Boyd, Stephen, Brown, J B, Capuzzi, Stephen J, Chushak, Yaroslav, Ciallella, Heather, Clark, Alex M, Consonni, Viviana, Daga, Pankaj R, Ekins, Sean, Farag, Sherif, Fedorov, Maxim, Fourches, Denis, Gadaleta, Domenico, Gao, Feng, Gearhart, Jeffery M, Goh, Garett, Goodman, Jonathan M, Grisoni, Francesca, Grulke, Christopher M, Hartung, Thomas, Hirn, Matthew, Karpov, Pavel, Korotcov, Alexandru, Lavado, Giovanna J, Lawless, Michael, Li, Xinhao, Luechtefeld, Thomas, Lunghini, Filippo, Mangiatordi, Giuseppe F, Marcou, Gilles, Marsh, Dan, Martin, Todd, Mauri, Andrea, Muratov, Eugene N, Myatt, Glenn J, Nguyen, Dac-Trung, Nicolotti, Orazio, Note, Reine, Pande, Paritosh, Parks, Amanda K, Peryea, Tyler, Polash, Ahsan H, Rallo, Robert, Roncaglioni, Alessandra, Rowlands, Craig, Ruiz, Patricia, Russo, Daniel P, Sayed, Ahmed, Sayre, Risa, Sheils, Timothy, Siegel, Charles, Silva, Arthur C, Simeonov, Anton, Sosnin, Sergey, Southall, Noel, Strickland, Judy, Tang, Yun, Teppen, Brian, Tetko, Igor V, Thomas, Dennis, Tkachenko, Valery, Todeschini, Roberto, Toma, Cosimo, Tripodi, Ignacio, Trisciuzzi, Daniela, Tropsha, Alexander, Varnek, Alexandre, Vukovic, Kristijan, Wang, Zhongyu, Wang, Liguo, Waters, Katrina M, Wedlake, Andrew J, Wijeyesakere, Sanjeeva J, Wilson, Dan, Xiao, Zijun, Yang, Hongbin, Zahoranszky-Kohalmi, Gergely, Zakharov, Alexey V, Zhang, Fagen F, Zhang, Zhen, Zhao, Tongan, Zhu, Hao, Zorn, Kimberley M, Casey, Warren, Kleinstreuer, Nicole C, Mansouri, K, Karmaus, A, Fitzpatrick, J, Patlewicz, G, Pradeep, P, Alberga, D, Alepee, N, Allen, T, Allen, D, Alves, V, Andrade, C, Auernhammer, T, Ballabio, D, Bell, S, Benfenati, E, Bhattacharya, S, Bastos, J, Boyd, S, Brown, J, Capuzzi, S, Chushak, Y, Ciallella, H, Clark, A, Consonni, V, Daga, P, Ekins, S, Farag, S, Fedorov, M, Fourches, D, Gadaleta, D, Gao, F, Gearhart, J, Goh, G, Goodman, J, Grisoni, F, Grulke, C, Hartung, T, Hirn, M, Karpov, P, Korotcov, A, Lavado, G, Lawless, M, Li, X, Luechtefeld, T, Lunghini, F, Mangiatordi, G, Marcou, G, Marsh, D, Martin, T, Mauri, A, Muratov, E, Myatt, G, Nguyen, D, Nicolotti, O, Note, R, Pande, P, Parks, A, Peryea, T, Polash, A, Rallo, R, Roncaglioni, A, Rowlands, C, Ruiz, P, Russo, D, Sayed, A, Sayre, R, Sheils, T, Siegel, C, Silva, A, Simeonov, A, Sosnin, S, Southall, N, Strickland, J, Tang, Y, Teppen, B, Tetko, I, Thomas, D, Tkachenko, V, Todeschini, R, Toma, C, Tripodi, I, Trisciuzzi, D, Tropsha, A, Varnek, A, Vukovic, K, Wang, Z, Wang, L, Waters, K, Wedlake, A, Wijeyesakere, S, Wilson, D, Xiao, Z, Yang, H, Zahoranszky-Kohalmi, G, Zakharov, A, Zhang, F, Zhang, Z, Zhao, T, Zhu, H, Zorn, K, Casey, W, Kleinstreuer, N, Mansouri, Kamel, Karmaus, Agnes L, Fitzpatrick, Jeremy, Patlewicz, Grace, Pradeep, Prachi, Alberga, Domenico, Alepee, Nathalie, Allen, Timothy E H, Allen, Dave, Alves, Vinicius M, Andrade, Carolina H, Auernhammer, Tyler R, Ballabio, Davide, Bell, Shannon, Benfenati, Emilio, Bhattacharya, Sudin, Bastos, Joyce V, Boyd, Stephen, Brown, J B, Capuzzi, Stephen J, Chushak, Yaroslav, Ciallella, Heather, Clark, Alex M, Consonni, Viviana, Daga, Pankaj R, Ekins, Sean, Farag, Sherif, Fedorov, Maxim, Fourches, Denis, Gadaleta, Domenico, Gao, Feng, Gearhart, Jeffery M, Goh, Garett, Goodman, Jonathan M, Grisoni, Francesca, Grulke, Christopher M, Hartung, Thomas, Hirn, Matthew, Karpov, Pavel, Korotcov, Alexandru, Lavado, Giovanna J, Lawless, Michael, Li, Xinhao, Luechtefeld, Thomas, Lunghini, Filippo, Mangiatordi, Giuseppe F, Marcou, Gilles, Marsh, Dan, Martin, Todd, Mauri, Andrea, Muratov, Eugene N, Myatt, Glenn J, Nguyen, Dac-Trung, Nicolotti, Orazio, Note, Reine, Pande, Paritosh, Parks, Amanda K, Peryea, Tyler, Polash, Ahsan H, Rallo, Robert, Roncaglioni, Alessandra, Rowlands, Craig, Ruiz, Patricia, Russo, Daniel P, Sayed, Ahmed, Sayre, Risa, Sheils, Timothy, Siegel, Charles, Silva, Arthur C, Simeonov, Anton, Sosnin, Sergey, Southall, Noel, Strickland, Judy, Tang, Yun, Teppen, Brian, Tetko, Igor V, Thomas, Dennis, Tkachenko, Valery, Todeschini, Roberto, Toma, Cosimo, Tripodi, Ignacio, Trisciuzzi, Daniela, Tropsha, Alexander, Varnek, Alexandre, Vukovic, Kristijan, Wang, Zhongyu, Wang, Liguo, Waters, Katrina M, Wedlake, Andrew J, Wijeyesakere, Sanjeeva J, Wilson, Dan, Xiao, Zijun, Yang, Hongbin, Zahoranszky-Kohalmi, Gergely, Zakharov, Alexey V, Zhang, Fagen F, Zhang, Zhen, Zhao, Tongan, Zhu, Hao, Zorn, Kimberley M, Casey, Warren, and Kleinstreuer, Nicole C more...
- Abstract
Background: Humans are exposed to tens of thousands of chemical substances that need to be assessed for their potential toxicity. Acute systemic toxicity testing serves as the basis for regulatory hazard classification, labeling, and risk management. However, it is cost- and time-prohibitive to evaluate all new and existing chemicals using traditional rodent acute toxicity tests. In silica models built using existing data facilitate rapid acute tox- icity predictions without using animals. Objkctivks: The U.S. Interagency Coordinating Committee on the Validation of Alternative Methods (ICCVAM) Acute Toxicity Workgroup organ- ized an international collaboration to develop in silica models for predicting acute oral toxicity based on five different end points: Lethal Dose 50 (LD50 value, U.S. Environmental Protection Agency hazard (four) categories, Globally Harmonized System for Classification and Labeling hazard (five) categories, very toxic chemicals [LD50 (LD50 ≤ 50 mg/kg)], and nontoxic chemicals (LD50 > 2,000 mg/kg). Mkthods: An acute oral toxicity data inventory for 11,992 chemicals was compiled, split into training and evaluation sets, and made available to 35 participating international research groups that submitted a total of 139 predictive models. Predictions that fell within the applicability domains of the submitted models were evaluated using external validation sets. These were then combined into consensus models to leverage strengths of individual approaches. Results: The resulting consensus predictions, which leverage the collective strengths of each individual model, form the Collaborative Acute Toxicity Modeling Suite (CATMoS). CATMoS demonstrated high performance in terms of accuracy and robustness when compared with in viva results. Discussion: CATMoS is being evaluated by regulatory agencies for its utility and applicability as a potential replacement for in viva rat acute oral toxicity studies. CATMoS predictions for more than 800,000 chemica more...
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- 2021
14. Deep Learning Insights into Lanthanides Complexation Chemistry
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Mitrofanov, Artem A., primary, Matveev, Petr I., additional, Yakubova, Kristina V., additional, Korotcov, Alexandru, additional, Sattarov, Boris, additional, Tkachenko, Valery, additional, and Kalmykov, Stepan N., additional more...
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- 2021
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15. CATMoS: Collaborative Acute Toxicity Modeling Suite
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Mansouri, Kamel, primary, Karmaus, Agnes L., additional, Fitzpatrick, Jeremy, additional, Patlewicz, Grace, additional, Pradeep, Prachi, additional, Alberga, Domenico, additional, Alepee, Nathalie, additional, Allen, Timothy E.H., additional, Allen, Dave, additional, Alves, Vinicius M., additional, Andrade, Carolina H., additional, Auernhammer, Tyler R., additional, Ballabio, Davide, additional, Bell, Shannon, additional, Benfenati, Emilio, additional, Bhattacharya, Sudin, additional, Bastos, Joyce V., additional, Boyd, Stephen, additional, Brown, J.B., additional, Capuzzi, Stephen J., additional, Chushak, Yaroslav, additional, Ciallella, Heather, additional, Clark, Alex M., additional, Consonni, Viviana, additional, Daga, Pankaj R., additional, Ekins, Sean, additional, Farag, Sherif, additional, Fedorov, Maxim, additional, Fourches, Denis, additional, Gadaleta, Domenico, additional, Gao, Feng, additional, Gearhart, Jeffery M., additional, Goh, Garett, additional, Goodman, Jonathan M., additional, Grisoni, Francesca, additional, Grulke, Christopher M., additional, Hartung, Thomas, additional, Hirn, Matthew, additional, Karpov, Pavel, additional, Korotcov, Alexandru, additional, Lavado, Giovanna J., additional, Lawless, Michael, additional, Li, Xinhao, additional, Luechtefeld, Thomas, additional, Lunghini, Filippo, additional, Mangiatordi, Giuseppe F., additional, Marcou, Gilles, additional, Marsh, Dan, additional, Martin, Todd, additional, Mauri, Andrea, additional, Muratov, Eugene N., additional, Myatt, Glenn J., additional, Nguyen, Dac-Trung, additional, Nicolotti, Orazio, additional, Note, Reine, additional, Pande, Paritosh, additional, Parks, Amanda K., additional, Peryea, Tyler, additional, Polash, Ahsan H., additional, Rallo, Robert, additional, Roncaglioni, Alessandra, additional, Rowlands, Craig, additional, Ruiz, Patricia, additional, Russo, Daniel P., additional, Sayed, Ahmed, additional, Sayre, Risa, additional, Sheils, Timothy, additional, Siegel, Charles, additional, Silva, Arthur C., additional, Simeonov, Anton, additional, Sosnin, Sergey, additional, Southall, Noel, additional, Strickland, Judy, additional, Tang, Yun, additional, Teppen, Brian, additional, Tetko, Igor V., additional, Thomas, Dennis, additional, Tkachenko, Valery, additional, Todeschini, Roberto, additional, Toma, Cosimo, additional, Tripodi, Ignacio, additional, Trisciuzzi, Daniela, additional, Tropsha, Alexander, additional, Varnek, Alexandre, additional, Vukovic, Kristijan, additional, Wang, Zhongyu, additional, Wang, Liguo, additional, Waters, Katrina M., additional, Wedlake, Andrew J., additional, Wijeyesakere, Sanjeeva J., additional, Wilson, Dan, additional, Xiao, Zijun, additional, Yang, Hongbin, additional, Zahoranszky-Kohalmi, Gergely, additional, Zakharov, Alexey V., additional, Zhang, Fagen F., additional, Zhang, Zhen, additional, Zhao, Tongan, additional, Zhu, Hao, additional, Zorn, Kimberley M., additional, Casey, Warren, additional, and Kleinstreuer, Nicole C., additional more...
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- 2021
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16. ChemSpider - building a foundation for the semantic web by hosting a crowd sourced databasing platform for chemistry
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Williams Anthony J, Tkachenko Valery, Golotvin Sergey, Kidd Richard, and McCann Graham
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Information technology ,T58.5-58.64 ,Chemistry ,QD1-999 - Published
- 2010
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17. MOESM2 of Open-source QSAR models for pKa prediction using multiple machine learning approaches
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Mansouri, Kamel, Cariello, Neal, Korotcov, Alexandru, Tkachenko, Valery, Grulke, Chris, Sprankle, Catherine, Allen, David, Casey, Warren, Kleinstreuer, Nicole, and Williams, Antony
- Abstract
Additional file 2. Additional data analysis and modeling information.
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- 2019
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18. Prediction of pKa from chemical structure using free and open-source tools
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Williams, Antony, Mansouri, Kamel, Tkachenko, Valery, Cariello, Neal, and Korotcov, Alex
- Abstract
The ionization state of a chemical, reflected in pKa values, affects lipophilicity, solubility, protein binding and the ability of a chemical to cross the plasma membrane. These properties govern the pharmacokinetic parameters such as absorption, distribution, metabolism, excretion and toxicity and thus pKa is a fundamental chemical property and is used in many models of chemical toxicity. Experimentally determining pKa is not feasible for high-throughput assays. Predicting pKa is challenging and existing models have been developed only using restricted chemical space (e.g., anilines, phenols, benzoic acids, primary amines) and lack of a generalized model impedes ADME modeling. No free and open source models exist for heterogeneous chemical classes, however, several proprietary programs exist. In this work, pKa open data bundled with DataWarrior (http://www.openmolecules.org/) were used to develop predictive models for pKa. After data cleaning, there were ~3100 and ~3900 monoprotic chemicals with an acidic or basic pKa, respectively. 1D and 2D chemical descriptors (AlogP, Topological polar surface area, etc) in addition to 12 fingerprints (presence or absence of a chemical group) were generated using PaDEL software. Three datasets were used: acidic, basic and acidic and basic combined. 13 datasets were examined, the 1D/2D descriptors and 12 fingerprints. Using the Extreme Gradient Boosting algorithm showed that the MACCS and Substructure Count fingerprints yielded the best results, with models showing an R-Squared of ~0.78 and a RMSE of 1.42. Recently, Deep Learning models have showed remarkable progress in image recognition and natural language processing. To determine if the Deep Learning algorithms would increase model performance we examined the datasets and found that the Deep Learning models were somewhat superior than Extreme Gradient Boosting with an R-Squared of ~0.80 and an RMSE of ~1.38. This work does not reflect U.S. EPA policy. more...
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- 2018
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19. Royal Society of Chemistry Activities to Develop a Data Repository for Chemistry-Specific Data
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Williams, Antony, Day, Aileen, Karapetyan, Ken, Batchelor, Colin, Corbett, Peter, Steele, Jon, and Tkachenko, Valery
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The Royal Society of Chemistry publishes many thousands of articles per year, the majority of these containing rich chemistry data that, in general, in limited in its value when isolated only to the HTML or PDF form of the articles commonly consumed by readers. RSC also has an archive of over 300,000 articles containing rich chemistry data especially in the form of chemicals, reactions, property data and analytical spectra. RSC is developing a platform integrating these various forms of chemistry data. The data will be aggregated both during the manuscript deposition process as well as the result of text-mining and extraction of data from across the RSC archive. This presentation will report on the development of the platform including our success in extracting compounds, reactions and spectral data from articles. We will also discuss our developing process for handling data at manuscript deposition and the integration and support of eLab Notebooks (ELNS) in terms of facilitating data deposition and sourcing data. Each of these processes is intended to ensure long-term access to research data with the intention of facilitating improved discovery. more...
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- 2014
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20. Ontology Work at the Royal Society of Chemistry
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Williams, Antony, Batchelor, Colin, Tkachenko, Valery, Karapetyan, Ken, and Steele, Jon
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We provide an overview of the use we make of ontologies at the Royal Society of Chemistry. Our engagement with the ontology community began in 2006 with preparations for Project Prospect, which used ChEBI and other Open Biomedical Ontologies to mark up journal articles. Subsequently Project Prospect has evolved into DERA (Digitally Enhancing the RSC Archive) and we have developed further ontologies for text markup, covering analytical methods and name reactions. Most recently we have been contributing to CHEMINF, an open-source cheminformatics ontology, as part of our work on disseminating calculated physicochemical properties of molecules via the Open PHACTS. We show how we represent these properties and how it can serve as a template for disseminating different sorts of chemical information. more...
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- 2014
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21. The UK National Chemical Database Service – an integration of commercial and public chemistry services to support chemists in the United Kingdom
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Williams, Antony, Kidd, Richard, and Tkachenko, Valery
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At a time when the data explosion has simply been redefined as “Big”, the hurdles associated with building a subject-specific data repository for chemistry are daunting. Combining a multitude of non-standard data formats for chemicals, related properties, reactions, spectra etc., together with the confusion of licensing and embargoing, and providing for data exchange and integration with services and platforms external to the repository, the challenge is significant. This all at a time when semantic technologies are touted as the fundamental technology to enhance integration and discoverability. Funding agencies are demanding change, especially a change towards access to open data to parallel their expectations around Open Access publishing. The Royal Society of Chemistry has been funded by the Engineering and Physical Science Research of the UK to deliver a “chemical database service” for UK scientists. This presentation will provide an overview of the challenges associated with this project and our progress in delivering a chemistry repository capable of handling the complex data types associated with chemistry. The benefits of such a repository in terms of providing data to develop prediction models to further enable scientific discovery will be discussed and the potential impact on the future of scientific publishing will also be examined. more...
- Published
- 2014
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22. Royal Society of Chemistry developments to support open drug discovery
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Williams, Antony, Karapetyan, Ken, Tkachenko, Valery, Batchelor, Colin, Pshenichnov, Alexey, and Steele, Jon
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In recent years the Royal Society of Chemistry has become known for our development of freely accessible data platforms including ChemSpider, ChemSpider Reactions and our new chemistry data repository. In order to support drug discovery RSC participates in a number of projects including the Open PHACTS semantic web project, the PharmaSea natural products discovery project and the Open Source Drug Discovery project in collaboration with a team in India. Our most recent developments include extending our efforts to support neglected diseases by the provision of high quality datasets resulting from our curation efforts to support modeling, the delivery of enhanced application programming interfaces to allow open source drug discovery teams to both source and deposit data from our chemistry databases and the provision of a micropublishing platform to report on various aspects of work supporting neglected disease drug discovery. This presentation will review our existing efforts and our plans for extended development. more...
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- 2014
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23. Accessing Royal Society of Chemistry resources and making chemistry mobile
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Williams, Antony, Pshenichnov, Alexey, and Tkachenko, Valery
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The ongoing drive towards mobile devices is now simply one of generic ubiquity. It is less an issue of whether a scientist has a mobile device but rather what brand, what generation and what apps do they have installed. Chemistry has fast been moving to mobile devices for a number of years now and today is it possible to draw chemical compounds, perform searches of databases both on device and in the cloud. Modeling of data using server based platforms is increasing in scope and capabilities. The Royal Society of Chemistry was early in recognizing the potential power of mobile platforms in terms to allowing scientists to access data and the benefits of such devices to allowing students access to data and content. This presentation will provide an overview of our efforts to date in supporting chemistry technologies on mobile devices and our recent developments in this domain. more...
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- 2014
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24. Data enhancing the Royal Society of Chemistry publication archive
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Williams, Antony, Batchelor, Colin, Corbett, Peter, Karapetyan, Ken, and Tkachenko, Valery
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The Royal Society of Chemistry has an archive of hundreds of thousands of published articles containing various types of chemistry related data – compounds, reactions, property data, spectral data etc. RSC has a vision of extracting as much of these data as possible and providing access via ChemSpider and its related projects. To this end we have applied a combination of text-mining extraction, image conversion and chemical validation and standardization approaches. The outcome of this project will result in new chemistry related data being added to our chemical and reaction databases and in the ability to more tightly couple web-based versions of the articles with these extracted data. The ability to search across the archive will be enhanced as a result. This presentation will report on our progress in this data extraction project and discuss how we will ultimately use similar approaches in our publishing pipeline to enhance article markup for new publications. more...
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- 2014
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25. Identification of 'Known Unknowns' Utilizing Accurate Mass Data and ChemSpider
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Williams, Antony, Little, James, Pshenichnov, Alexey, and Tkachenko, Valery
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In many cases, an unknown to an investigator is actually known in the chemical literature, a reference database, or an internet resource. We refer to these types of compounds as “known unknowns.” ChemSpider is a very valuable internet database of known compounds useful in the identification of these types of compounds in commercial, environmental, forensic, and natural product samples. The database contains over 26 million entries from hundreds of data sources and is provided as a free resource to the community. Accurate mass mass spectrometry data is used to query the database by either elemental composition or a monoisotopic mass. Searching by elemental composition is the preferred approach. However, it is often difficult to determine a unique elemental composition for compounds with molecular weights greater than 600 Da. In these cases, searching by the monoisotopic mass is advantageous. In either case, the search results are refined by sorting the number of references associated with each compound in descending order. This raises the most useful candidates to the top of the list for further evaluation. These approaches were shown to be successful in identifying “known unknowns” noted in our laboratory and for compounds of interest to others. more...
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- 2013
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26. Approaches for extraction and digital chromatography of chemical data
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Williams, Antony, Sharpe, David, Batchelor, Colin, Tkachenko, Valery, Karapetyan, Ken, and Kidd, Richard
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"The traditional perception of the publishing process has been that it culminates in a print article. The Royal Society of Chemistry (RSC) has for many years been acutely aware that there is a wealth of information contained in scientific communications that we publish and that its true value can only be unlocked by enabling the discovery of the data within them. This is challenging due to the variety of ways that scientists provide data, textually, graphically, and increasingly in supplementary information. This talk will outline how the RSC has applied innovative approaches, developed both internally and externally, to identifying important chemical data within the literature and provides tools to anyone using chemical data to analyse and improve its quality. Examples will include: Project Prospect, the Experimental Data Checker, our CIF data importer, ChemSpider and our structure validation and standardization service." more...
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- 2013
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27. Serving up and consuming community content for chemists using wikis
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Williams, Antony, Sharpe, David, Pshenichnov, Alexey, Ekins, Sean, and Tkachenko, Valery
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Wikipedia has become the world’s most famous encyclopedia using as it’s platform the MediaWiki open source software. The software is supported not only by the MediaWiki foundation but by a community of developers who build widgets and add-ons to extend the capabilities. This presentation will review how MediaWiki has been used as a container for a number of resources of value to chemists, specifically SciMobileApps, SciDBs and ScientistsDB holding content regarding mobile scientific apps, scientific databases and scientists. We will also review how chemistry content within Wikipedia has been used to enhance the content underlying the RSC ChemSpider database and how the platform supports an educational environment for chemistry students. more...
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- 2013
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28. ChemValidator – an online service for validating and standardizing chemical structure files
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Williams, Antony, Sharpe, David, Tkachenko, Valery, Karapetyan, Ken, and Batchelor, Colin
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The production of valid and appropriate chemical structure representations which are appropriate for deposition into chemical structure databases and for inclusion into scientific publications requires adoption of a set of pre-processing filters and standardization procedures. As part of our ongoing effort to improve the quality of data for deposition into the RSC ChemSpider database, to provide a manner by which to validate and prepare data for publication and to provide a valuable service to the chemistry community, we have delivered the ChemValidator online service. This website provides access to an intuitive user interface for the upload of chemical compounds in various formats, pre-processing and standardization relative to a defined set of standards and validation checking of the chemicals according to a number of rules including hypervalency, absence of stereochemistry and charge balance. This presentation will report on the development of ChemValidator. This presentation was given by David Sharpe at the ACS Fall Meeting in 2012 more...
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- 2013
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29. Mining public domain data as a basis for drug repurposing
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Williams, Antony, Tkachenko, Valery, and Ekins, Sean
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Online databases containing high throughput screening and other property data continue to proliferate in number. Many pharmaceutical chemists will have used databases such as PubChem, ChemSpider, DrugBank, BindingDB and many others. This work will report on the potential value of these databases for providing data to be used to repurpose drugs using cheminformatics-based approaches (e.g. docking, ligand-based machine learning methods). This work will also discuss the potentially related applications of the Open PHACTS project, a European Union Innovative Medicines Initiative project, that is utilizing semantic web based approaches to integrate large scale chemical and biological data in new ways. We will report on how compound and data quality should be taken into account when utilizing data from online databases and how their careful curation can provide high quality data that can be used to underpin the delivery of molecular models that can in turn identify new uses for old drugs. more...
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- 2013
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30. ChemSpider reactions – delivering a free community resource of chemical syntheses
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Williams, Antony, Tkachenko, Valery, Batchelor, Colin, Lowe, Daniel, Karapetyan, Ken, and Sharpe, David
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Presentation delivered by Colin Batchelor from the RSC eScience team at ACS New Orleans Spring Meeting April 2013. There are dozens of public compound databases now available online, some of these providing access to tens of millions of chemical compounds. However, very little effort has been put into the delivery of databases of chemical reactions with the majority of large resources being commercial in nature. In our five years of delivering chemical based data resources to the chemistry community one of the primary requests has been that chemists want to know how to synthesize many of the chemicals they are researching. This presentation will provide an overview of our concerted efforts to enhance access to freely available chemistry data and will discuss the ChemSpider Reactions as an integrating hub of content including data extracted from US patents, from RSC Journals and databases and from our micro-publishing platform ChemSpider Synthetic Pages (CSSP). more...
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- 2013
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31. Engaging students in publishing on the internet early in their careers
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Williams, Antony, Steele, Jon, Tkachenko, Valery, Batchelor, Colin, and Sharpe, David
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As a result of the advent of internet technologies supporting participation on the internet via blogs, wikis and other social networking approaches, chemists now have an opportunity to contribute to the growing chemistry content on the web. As scientists an important skill to develop is the ability to succinctly report in a published format the details of scientific experimentation. The Royal Society of Chemistry provides a number of online systems to share chemistry data, the most well known of these being the ChemSpider database. In parallel the ChemSpider SyntheticPages (CSSP) platform is an online publishing platform for scientists, and especially students, to publish the details of chemical syntheses that they have performed. Using the rich capabilities of internet platforms, including the ability to display interactive spectral data and movies, CSSP is an ideal environment for students to publish their work, especially syntheses that might not support mainstream publication. more...
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- 2013
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32. Navigating scientific resources using wiki-based resources
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Williams, Antony, Tkachenko, Valery, Pshenichnov, Alexey, Ekins, Sean, and Clark, Alex
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There is an overwhelming number of new resources for chemistry that would likely benefit both librarians and students in terms of improving access to data and information. While commercial solutions provided by an institution may be the primary resources there is now an enormous range of online tools, databases, resources, apps for mobile devices and, increasingly, wikis. This presentation will provide an overview of how wiki-based resources for scientists are developing and will introduce a number of developing wikis. These include wikis that are being used to teach chemistry to students as well as to source information about scientists, scientific databases and mobile apps. more...
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- 2013
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33. The RSC chemical validation and standardization platform, a potential path to quality-conscious databases
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Williams, Antony, Karapetyan, Ken, Batchelor, Colin, and Tkachenko, Valery
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High quality chemical databases are struggling with protecting their data from the flow of wild machine-generated chemistry and lower-quality data. The period of primarily human curation prior to deposition in a database is gone and quality-conscious databases need to heavily rely on automated validation checks. An automated chemical validation system is being developed by the cheminformatics team at the Royal Society of Chemistry to be the “quality gatekeeper” of databases at the point of deposition. ChemSpider is leading a community-wide standardization approach starting with our support of the Open PHACTS semantic web project, an Innovative Medicines Initiative. The Chemical Validation and Standardization Platform (CVSP) is being designed as an open, flexible chemical validation and standardization platform that validates and standardizes chemical records. This presentation will review the existing beta version of the system and work in progress. more...
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- 2013
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34. Feeding and consuming data to support Open Notebook Science via the ChemSpider Platform
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Williams, Antony, Tkachenko, Valery, Bradley, Jean-Claude, and Lang, Andrew
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We are all benefiting from a shift towards openness fed by Open Source, Open Standards, Open Data and Open Access. Open Notebook Science is likely the scientific revolution of the near term. As more scientists become comfortable with the concepts of openly sharing their experiments and data, often in near real time, we are seeing a shift to significant increases in the availability of new data that does not have to be extracted from publications but is available as data feeds that can be delivered to the community. This presentation will provide an overview of how the ChemSpider database from the RSC supports Open Notebook Science using programmatic access to both data and services and how ChemSpider ingests data feeds to mesh together with our existing database of over 27 million chemical compounds. more...
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- 2013
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35. ChemSpider as a chemical term resolver
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Williams, Antony, Tkachenko, Valery, Ekins, Sean, and Fant, Andy
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In recent years, in parallel with the general broad trend of information proliferation, many tens of public chemical databases have been created and made available using internet technologies. In many cases fluent data exchange has occurred between these various databases as they source information from one another. While this has the advantages of linking together multiple data sources the results also include the proliferation of errors across the various databases. The lack of a public authority to resolve such errors significantly affects the quality of freely accessible chemical information. While ChemSpider has previously allowed a crowdsourcing approach to curation efforts have now migrated to addressing this problem using a "federated resolver" approach. This presentation will report on our work in this area. more...
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- 2013
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36. Data Enhancing the RSC Archive
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Williams, Antony, Batchelor, Colin, Karapetyan, Ken, Pshenichnov, Alexey, Sharpe, David, Steele, Jon, and Tkachenko, Valery
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The Royal Society of Chemistry has an archive of published journals and books stretching back to 1841. In the past decade we have digitized this archive and semantically enriched our frontfile data with chemical structures linked to our free online chemical compound database, ChemSpider. In this talk we will survey our recent efforts to extract all kinds of data – chemical structures, experimental and bibliographic data – from both our backfile and frontfile. We will also discuss our future work to extract chemical reactions to host in our ChemSpider Reactions database and will discuss the potential applications of optical structure recognition technologies for converting structure images to structures as well as using similar techniques to convert experimental spectral data into interactive data formats. A key aspect of this project is the delivery of a crowdsourcing platform for the interactive annotation and validation of the extracted data. more...
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- 2013
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37. ChemSpider – disseminating data and enabling an abundance of chemistry platforms
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Williams, Antony, Tkachenko, Valery, Karapetyan, Ken, Pshenichnov, Alexey, Batchelor, Colin, Steele, Jon, and Sharpe, David
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ChemSpider is one of the chemistry community’s primary public compound databases. Containing tens of millions of chemical compounds and its associated data ChemSpider serves data to many tens of websites and software applications at this point. This presentation will provide an overview of the expanding reach of the ChemSpider platform and the nature of solutions that it helps to enable. We will also discuss some of the future directions for the project that are envisaged and how we intend to continue expanding the impact for the platform. more...
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- 2013
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38. ChemSpider: how a free community resource of data can support the teaching of NMR spectroscopy
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Williams, Antony, Tkachenko, Valery, and Pshenichnov, Alexey
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The Royal Society of Chemistry (RSC) hosts a number of resources of direct benefit to teaching chemistry. One of these resources, ChemSpider, is a free chemical database containing data for millions of chemical compounds. The data includes thousands of spectra, primarily contributed by members of the scientific community as ChemSpider depends on crowdsourcing for certain types of data. The NMR spectral data hosted on ChemSpider are integrated to various other RSC systems including an educational wiki and a micropublishing platform for reaction syntheses. The data are also available programmatically to the community and have been used as the basis of a game to teach NMR spectroscopy. more...
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- 2013
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39. Teaching NMR Spectroscopy Using Online Resources from the Royal Society of Chemistry
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Williams, Antony, Pshenichnov, Alexey, Tkachenko, Valery, and Oakley, Richard
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The Royal Society of Chemistry hosts an online resource, ChemSpider, as a structure centric database for chemists linking over 25 million chemicals to 400 internet sites. As a crowdsourced environment members of the chemistry community can deposit spectral data to the database. Almost 2000 NMR spectra have been submitted to the database and these are the basis of both a gaming environment for learning NMR spectroscopy, the SpectralGame, as well as a new teaching environment known as SpectraSchool. This presentation will provide an overview of these two online resources and how they may be utilized for the purpose of teaching NMR spectroscopy in an Undergraduate Curriculum. more...
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- 2013
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40. Towards a gold standard and regarding quality in public domain chemistry databases and approaches to improving the situation
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Williams, Antony, Ekins, Sean, and Tkachenko, Valery
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In recent years there has been a dramatic increase in the number of freely accessible online databases serving the chemistry community. These databases are diverse in nature and encompass chemical vendor catalogs, government-funded research results, definitively focused and highly curated collections and large-scale information aggregators. As a result the internet theoretically now represents a rich array of chemistry data that can be utilized for the purpose of data-mining, for utilizing in computer models, and for integrating into other systems to expand data accessibility and aid drug discovery. With this dramatic improvement in accessibility comes a responsibility to ensure that the data are as high a quality as possible. This will ensure that time is not wasted in erroneous searches, that models are underpinned by accurate data and that the improved discoverability of online resources is not marred by the delivery of incorrect data. This paper represents an overview of some of the experiences of the authors in utilizing online chemical compound databases, critiques some of the approaches being taken to assemble data and suggests alternative approaches to collaborate to deliver definitive reference data sources for the community to utilize. more...
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- 2013
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41. Challenging, cajoling and rewarding the community for their contributions to online chemistry
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Williams, Antony, Tkachenko, Valery, Pshenichnov, Alexey, Russell, Will, Rumble, Jack, and Leeming, David
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Chemistry online is represented in various ways including publications, presentations, blog posts, wiki-contributions, data depositions, curations and annotations. Encouraging participation from the community to participate in and comment on the information delivered via these various formats would likely provide for a rich dialog exchange in some cases and improved data quality in others. At the Royal Society of Chemistry we have a number of platforms that are amenable to contribution. This presentation will provide an overview of our experiences in engaging the community to interact with our various forms of content and discuss new approaches we are utilizing to encourage crowdsourced participation. more...
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- 2013
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42. High-performance integrated virtual environment (HIVE): a robust infrastructure for next-generation sequence data analysis
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Simonyan, Vahan, primary, Chumakov, Konstantin, additional, Dingerdissen, Hayley, additional, Faison, William, additional, Goldweber, Scott, additional, Golikov, Anton, additional, Gulzar, Naila, additional, Karagiannis, Konstantinos, additional, Vinh Nguyen Lam, Phuc, additional, Maudru, Thomas, additional, Muravitskaja, Olesja, additional, Osipova, Ekaterina, additional, Pan, Yang, additional, Pschenichnov, Alexey, additional, Rostovtsev, Alexandre, additional, Santana-Quintero, Luis, additional, Smith, Krista, additional, Thompson, Elaine E., additional, Tkachenko, Valery, additional, Torcivia-Rodriguez, John, additional, Voskanian, Alin, additional, Wan, Quan, additional, Wang, Jing, additional, Wu, Tsung-Jung, additional, Wilson, Carolyn, additional, and Mazumder, Raja, additional more...
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- 2016
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43. Erratum to: Automatic vs. manual curation of a multi-source chemical dictionary: the impact on text mining
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Hettne, Kristina M, primary, Williams, Antony J, additional, van Mulligen, Erik M, additional, Kleinjans, Jos, additional, Tkachenko, Valery, additional, and Kors, Jan A, additional
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- 2010
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