Search

Your search keyword '"Tkachenko, Valery"' showing total 43 results
Start Over Author "Tkachenko, Valery" Publication Year Range Last 10 years
43 results on '"Tkachenko, Valery"'

2. Size doesn't matter: predicting physico- or biochemical properties based on dozens of molecules

Author

Karpov, Kirill, Mitrofanov, Artem, Korolev, Vadim, and Tkachenko, Valery

Subjects
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

Published
2021

3. Graph convolutional neural networks as 'general-purpose' property predictors: the universality and limits of applicability

Author

Korolev, Vadim, Mitrofanov, Artem, Korotcov, Alexandru, and Tkachenko, Valery

Subjects
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

Published
2019
Full Text
View/download PDF

4. Transferable and extensible machine learning derived atomic charges for modeling hybrid nanoporous materials

Author

Korolev, Vadim, Mitrofanov, Artem, Marchenko, Ekaterina, Eremin, Nickolay, Tkachenko, Valery, and Kalmykov, Stepan

Subjects
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

Published
2019
Full Text
View/download PDF

8. Structure standardization approaches for mass spectrometry data integration

Author

Williams, Antony, Lowe, Charles, Sinclair, Gabriel, Martin, Todd, and Tkachenko, Valery

Subjects
Toxicology (incl. clinical toxicology)
Abstract

Presentation to the American Chemical Society (ACS) Fall National Meeting Aug 2022 Search for CCTE records in EPA’s Science Inventory by typing in the title at this link. https://cfpub.epa.gov/si/si_public_search_results.cfm?advSearch=true&showCriteria=2&keyword=CCTE&TIMSType=&TIMSSubTypeID=&epaNumber=&ombCat=Any&dateBeginPublishedPresented=07/01/2017&dateEndPublishedPresented=&dateBeginUpdated=&dateEndUpdated=&DEID=&personName=&personID=&role=Any&journalName=&journalID=&publisherName=&publisherID=&sortBy=pubDate&count=25

Published
2022
Full Text
View/download PDF

9. Evaluating chemical alternatives using the Hazard Comparison Dashboard

Author

Martin, Todd, Tkachenko, Valery, and Williams, Antony

Subjects
Toxicology (incl. clinical toxicology)
Abstract

Presentation to Fall ACS on Aug. 21-25, 2022 in Chicago IL Science Inventory, CCTE products: https://cfpub.epa.gov/si/si_public_search_results.cfm?advSearch=true&showCriteria=2&keyword=CCTE&TIMSType=&TIMSSubTypeID=&epaNumber=&ombCat=Any&dateBeginPublishedPresented=07/01/2017&dateEndPublishedPresented=&dateBeginUpdated=&dateEndUpdated=&DEID=&personName=&personID=&role=Any&journalName=&journalID=&publisherName=&publisherID=&sortBy=pubDate&count=25

Published
2022
Full Text
View/download PDF

10. Machine Learning Models for Mycobacterium tuberculosis In Vitro Activity: Prediction and Target Visualization

Author

Lane, Thomas R., primary, Urbina, Fabio, additional, Rank, Laura, additional, Gerlach, Jacob, additional, Riabova, Olga, additional, Lepioshkin, Alexander, additional, Kazakova, Elena, additional, Vocat, Anthony, additional, Tkachenko, Valery, additional, Cole, Stewart, additional, Makarov, Vadim, additional, and Ekins, Sean, additional

Published
2021
Full Text
View/download PDF

11. Erratum: CATMoS: Collaborative Acute Toxicity Modeling Suite

Author

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

Published
2021
Full Text
View/download PDF

13. Estimation of Toxicity via T.E.S.T. (v5.1) and WebTEST

Author

Martin, Todd, Williams, Antony, and Tkachenko, Valery

Subjects
Toxicology
Abstract

Presentation to the QSAR 2021 International Workshop on QSAR in Environmental and Health Sciences June 2021Search for CCTE records in EPA’s Science Inventory by typing in the title at this link.https://cfpub.epa.gov/si/si_public_search_results.cfm?advSearch=true&showCriteria=2&keyword=CCTE&TIMSType=&TIMSSubTypeID=&epaNumber=&ombCat=Any&dateBeginPublishedPresented=07/01/2017&dateEndPublishedPresented=&dateBeginUpdated=&dateEndUpdated=&DEID=&personName=&personID=&role=Any&journalName=&journalID=&publisherName=&publisherID=&sortBy=pubDate&count=25

Published
2021
Full Text
View/download PDF

15. CATMoS: Collaborative Acute Toxicity Modeling Suite

Author

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

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

Published
2021

17. CATMoS: Collaborative Acute Toxicity Modeling Suite

Author

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

Published
2021
Full Text
View/download PDF

24. Machine Learning Models for Mycobacterium tuberculosisIn VitroActivity: Prediction and Target Visualization

Author

Lane, Thomas R., Urbina, Fabio, Rank, Laura, Gerlach, Jacob, Riabova, Olga, Lepioshkin, Alexander, Kazakova, Elena, Vocat, Anthony, Tkachenko, Valery, Cole, Stewart, Makarov, Vadim, and Ekins, Sean

Abstract

Tuberculosis (TB) is a major global health challenge, with approximately 1.4 million deaths per year. There is still a need to develop novel treatments for patients infected with Mycobacterium tuberculosis(Mtb). There have been many large-scale phenotypic screens that have led to the identification of thousands of new compounds. Yet, there is very limited investment in TB drug discovery which points to the need for new methods to increase the efficiency of drug discovery against Mtb. We have used machine learning approaches to learn from the public Mtbdata, resulting in many data sets and models with robust enrichment and hit rates leading to the discovery of new active compounds. Recently, we have curated predominantly small-molecule Mtbdata and developed new machine learning classification models with 18 886 molecules at different activity cutoffs. We now describe the further validation of these Bayesian models using a library of over 1000 molecules synthesized as part of EU-funded New Medicines for TB and More Medicines for TB programs. We highlight molecular features which are enriched in these active compounds. In addition, we provide new regression and classification models that can be used for scoring compound libraries or used to design new molecules. We have also visualized these molecules in the context of known molecular targets and identified clusters in chemical property space, which may aid in future target identification efforts. Finally, we are also making these data sets publicly available, representing a significant increase to the available Mtbinhibition data in the public domain.

Published
2022
Full Text
View/download PDF

25. Open Source QSAR Models For pKa Prediction Using Multiple Machine Learning Approaches

Author

Mansouri, Kamel, Cariello, Neal, Korotcov, Alex, Tkachenko, Valery, Casey, Warren, Kleinstreuer, Nicole, Allen, David, Grulke, Chris, and Williams, Antony

Subjects
education, Toxicology, human activities, health care economics and organizations
Abstract

Poster presented at American Society for Cellular and Computational Toxicology (ASCCT) meeting in September 2018.

Published
2018
Full Text
View/download PDF

31. Prediction of pKa from chemical structure using free and open-source tools

Author

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.

Published
2018
Full Text
View/download PDF

38. Graph Convolutional Neural Networks as “General-Purpose” Property Predictors: The Universality and Limits of Applicability

Author

Korolev, Vadim, Mitrofanov, Artem, Korotcov, Alexandru, and Tkachenko, Valery

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. The 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 (GCNNs) as an architecture that allows for successfully predicting 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.

Published
2020
Full Text
View/download PDF

39. High-performance integrated virtual environment (HIVE): a robust infrastructure for next-generation sequence data analysis

Author

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

Published
2016
Full Text
View/download PDF

43. Comparing and Validating Machine Learning Models for Mycobacterium tuberculosisDrug Discovery

Author

Lane, Thomas, Russo, Daniel P., Zorn, Kimberley M., Clark, Alex M., Korotcov, Alexandru, Tkachenko, Valery, Reynolds, Robert C., Perryman, Alexander L., Freundlich, Joel S., and Ekins, Sean

Abstract

Tuberculosis is a global health dilemma. In 2016, the WHO reported 10.4 million incidences and 1.7 million deaths. The need to develop new treatments for those infected with Mycobacterium tuberculosis(Mtb) has led to many large-scale phenotypic screens and many thousands of new active compounds identified in vitro. However, with limited funding, efforts to discover new active molecules against Mtbneeds to be more efficient. Several computational machine learning approaches have been shown to have good enrichment and hit rates. We have curated small molecule Mtbdata and developed new models with a total of 18,886 molecules with activity cutoffs of 10 μM, 1 μM, and 100 nM. These data sets were used to evaluate different machine learning methods (including deep learning) and metrics and to generate predictions for additional molecules published in 2017. One Mtbmodel, a combined in vitroand in vivodata Bayesian model at a 100 nM activity yielded the following metrics for 5-fold cross validation: accuracy = 0.88, precision = 0.22, recall = 0.91, specificity = 0.88, kappa = 0.31, and MCC = 0.41. We have also curated an evaluation set (n= 153 compounds) published in 2017, and when used to test our model, it showed the comparable statistics (accuracy = 0.83, precision = 0.27, recall = 1.00, specificity = 0.81, kappa = 0.36, and MCC = 0.47). We have also compared these models with additional machine learning algorithms showing Bayesian machine learning models constructed with literature Mtbdata generated by different laboratories generally were equivalent to or outperformed deep neural networks with external test sets. Finally, we have also compared our training and test sets to show they were suitably diverse and different in order to represent useful evaluation sets. Such Mtbmachine learning models could help prioritize compounds for testing in vitroand in vivo.

Published
2018
Full Text
View/download PDF

Catalog

Books, media, physical & digital resources
See catalog results