1.
Chan, H. C. S., Shan, H., Dahoun, T.; et al. Advancing Drug Discovery via Artificial Intelligence. Trends Pharm. Sci. 2019, 40, 592–604.
Google Scholar |
Crossref |
Medline2.
Paul, S. M., Mytelka, D. S., Dunwiddie, C. T.; et al. How to Improve R&D Productivity: The Pharmaceutical Industry’s Grand Challenge. Nat. Rev. Drug Discov. 2010, 9, 203–214.
Google Scholar |
Crossref |
Medline3.
Arrowsmith, J. Trial Watch: Phase III and Submission Failures: 2007–2010. Nat. Rev. Drug Discov. 2011, 10, 87.
Google Scholar |
Crossref |
Medline4.
Arrowsmith, J. Trial Watch: Phase II Failures: 2008–2010. Nat. Rev. Drug Discov. 2011, 10, 328–329.
Google Scholar |
Crossref |
Medline5.
Waring, M. J., Arrowsmith, J., Leach, A. R.; et al. An Analysis of the Attrition of Drug Candidates from Four Major Pharmaceutical Companies. Nat. Rev. Drug Discov. 2015, 14, 475–486.
Google Scholar |
Crossref |
Medline6.
Prentis, R. A., Lis, Y., Walker, S. R. Pharmaceutical Innovation by the Seven UK-Owned Pharmaceutical Companies (1964–1985). Brit. J. Clin. Pharm. 1988, 25, 387–396.
Google Scholar |
Crossref |
Medline7.
Kola, I., Landis, J. Can the Pharmaceutical Industry Reduce Attrition Rates? Nat. Rev. Drug Discov. 2004, 3, 711–715.
Google Scholar |
Crossref |
Medline8.
Wenzel, J., Matter, H., Schmidt, F. Predictive Multitask Deep Neural Network Models for ADME-Tox Properties: Learning from Large Data Sets. J. Chem. Inf. Model. 2019, 59, 1253–1268.
Google Scholar |
Crossref |
Medline9.
Kearnes, S., Goldman, B., Pande, V. Modeling Industrial ADMET Data with Multitask Networks. arXiv:1606.08793 [stat] 2017.
https://arxiv.org/abs/1606.08793v3.
Google Scholar10.
Siramshetty, V. B., Shah, P., Kerns, E.; et al. Retrospective Assessment of Rat Liver Microsomal Stability at NCATS: Data and QSAR Models. Sci. Rep. 2020, 10, 20713.
Google Scholar |
Crossref |
Medline11.
Sun, H., Nguyen, K., Kerns, E.; et al. Highly Predictive and Interpretable Models for PAMPA Permeability. Bioorg. Med. Chem. 2017, 25, 1266–1276.
Google Scholar |
Crossref |
Medline12.
Sun, H., Shah, P., Nguyen, K.; et al. Predictive Models of Aqueous Solubility of Organic Compounds Built on a Large Dataset of High Integrity. Bioorg. Med. Chem. 2019, 27, 3110–3114.
Google Scholar |
Crossref |
Medline13.
Tiwari, P., Indoliya, Y., Chauhan, A. S.; et al. Over-Expression of Rice R1-Type MYB Transcription Factor Confers Different Abiotic Stress Tolerance in Transgenic Arabidopsis. Ecotoxicol. Environ. Saf. 2020, 206, 111361.
Google Scholar14.
Huang, R., Zhu, H., Shinn, P.; et al. The NCATS Pharmaceutical Collection: A 10-Year Update. Drug Discov. Today 2019, 24, 2341–2349.
Google Scholar |
Crossref |
Medline15.
Breiman, L. Random Forests. Mach. Learn. 2001, 45, 5–32.
Google Scholar |
Crossref16.
Pedregosa, F., Varoquaux, G., Gramfort, A.; et al. Scikit-learn: Machine Learning in ython. J. Mach. Learn. Res. 2011, 12, 2825–2830.
Google Scholar17.
Varnek, A., Baskin, I. Machine Learning Methods for Property Prediction in Chemoinformatics: Quo Vadis? J. Chem. Inf. Model. 2012, 52, 1413–1437.
Google Scholar |
Crossref |
Medline18.
Svetnik, V., Liaw, A., Tong, C.; et al. Random Forest: A Classification and Regression Tool for Compound Classification and QSAR Modeling. J. Chem. Inf. Comput. Sci. 2003, 43, 1947–1958.
Google Scholar |
Crossref |
Medline19.
Wu, Z., Ramsundar, B., Feinberg, E. N.; et al. MoleculeNet: A Benchmark for Molecular Machine Learning. Chem. Sci. 2018, 9, 513–530.
Google Scholar |
Crossref |
Medline20.
Yang, K., Swanson, K., Jin, W.; et al. Analyzing Learned Molecular Representations for Property Prediction. J. Chem. Inf. Model. 2019, 59, 3370–3388.
Google Scholar |
Crossref |
Medline21.
Bronstein, M. M., Bruna, J., LeCun, Y.; et al. Geometric Deep Learning: Going beyond Euclidean Data. IEEE Signal Process. Mag. 2017, 34, 18–42.
Google Scholar |
Crossref22.
chemprop/chemprop . Message Passing Neural Networks for Molecule Property Prediction.
https://github.com/chemprop/chemprop.
Google Scholar23.
National Center for Advancing Translational Sciences (NCATS) . Resolver.
https://tripod.nih.gov/servlet/resolver/.
Google Scholar24.
Sittampalam, G. S.;, Grossman, A.;, Brimacombe, K.;; et al., Eds. Assay Guidance Manual. Eli Lilly & Company and the National Center for Advancing Translational Sciences: Bethesda, MD, 2004.
Google Scholar25.
Zakharov, A. V., Peach, M. L., Sitzmann, M.; et al. Computational Tools and Resources for Metabolism-Related Property Predictions. 2. Application to Prediction of Half-Life Time in Human Liver Microsomes. Future Med. Chem. 2012, 4, 1933–1944.
Google Scholar |
Crossref |
Medline26.
Lee, P. H., Cucurull-Sanchez, L., Lu, J.; et al. Development of In Silico Models for Human Liver Microsomal Stability. J. Comput. Aided Mol. Des. 2007, 21, 665–673.
Google Scholar |
Crossref |
Medline27.
Sakiyama, Y., Yuki, H., Moriya, T.; et al. Predicting Human Liver Microsomal Stability with Machine Learning Techniques. J. Mol. Graph. Model. 2008, 26, 907–915.
Google Scholar |
Crossref |
Medline28.
Hu, Y., Unwalla, R., Denny, R. A.; et al. Development of QSAR Models for Microsomal Stability: Identification of Good and Bad Structural Features for Rat, Human and Mouse Microsomal Stability. J. Comput. Aided Mol. Des. 2010, 24, 23–35.
Google Scholar |
Crossref |
Medline29.
Liu, R., Schyman, P., Wallqvist, A. Critically Assessing the Predictive Power of QSAR Models for Human Liver Microsomal Stability. J. Chem. Inf. Model. 2015, 55, 1566–1575.
Google Scholar |
Crossref |
Medline30.
Chang, C., Duignan, D. B., Johnson, K. D.; et al. The Development and Validation of a Computational Model to Predict Rat Liver Microsomal Clearance. J. Pharm. Sci. 2009, 98, 2857–2867.
Google Scholar |
Crossref |
Medline31.
Akamatsu, M., Fujikawa, M., Nakao, K.; et al. In Silico Prediction of Human Oral Absorption Based on QSAR Analyses of PAMPA Permeability. Chem. Biodivers. 2009, 6, 1845–1866.
Google Scholar |
Crossref |
Medline32.
Chi, C. T., Lee, M. H., Weng, C. F.; et al. In Silico Prediction of PAMPA Effective Permeability Using a Two-QSAR Approach. Int. J. Mol. Sci. 2019, 20, 3170.
Google Scholar |
Crossref33.
Fujikawa, M., Nakao, K., Shimizu, R.; et al. QSAR Study on Permeability of Hydrophobic Compounds with Artificial Membranes. Bioorg. Med. Chem. 2007, 15, 3756–3767.
Google Scholar |
Crossref |
Medline34.
Oja, M., Maran, U. Quantitative Structure-Permeability Relationships at Various pH Values for Acidic and Basic Drugs and Drug-Like Compounds. SAR and QSAR Environ. Res. 2015, 26, 701–719.
Google Scholar |
Crossref |
Medline35.
Oja, M., Maran, U. The Permeability of an Artificial Membrane for Wide Range of pH in Human Gastrointestinal Tract: Experimental Measurements and Quantitative Structure–Activity Relationship. Mol. Inform. 2015, 34, 493–506.
Google Scholar |
Crossref |
Medline36.
Verma, R. P., Hansch, C., Selassie, C. D. Comparative QSAR Studies on PAMPA/Modified PAMPA for High Throughput Profiling of Drug Absorption Potential with Respect to Caco-2 Cells and Human Intestinal Absorption. J. Comput. Aided Mol. Des. 2007, 21, 3–22.
Google Scholar |
Crossref |
Medline37.
Ran, Y., Yalkowsky, S. H. Prediction of Drug Solubility by the General Solubility Equation (GSE). J. Chem. Inf. Comput. Sci. 2001, 41, 354–357.
Google Scholar |
Crossref |
Medline38.
Tetko, I. V., Tanchuk, V. Y., Kasheva, T. N.; et al. Estimation of Aqueous Solubility of Chemical Compounds Using E-State Indices. J. Chem. Inf. Comput. Sci. 2001, 41, 1488–1493.
Google Scholar |
Crossref |
Medline39.
Jorgensen, W. L., Duffy, E. M. Prediction of Drug Solubility from Structure. Adv. Drug Deliv. Rev. 2002, 54, 355–366.
Google Scholar |
Crossref |
Medline40.
Boobier, S., Osbourn, A., Mitchell, J. B. O. Can Human Experts Predict Solubility Better Than Computers? J. Cheminformatics 2017, 9, 63.
Google Scholar |
Crossref |
Medline41.
Lusci, A., Pollastri, G., Baldi, P. Deep Architectures and Deep Learning in Chemoinformatics: The Prediction of Aqueous Solubility for Drug-Like Molecules. J. Chem. Inf. Model. 2013, 53, 1563–1575.
Google Scholar |
Crossref |
Medline42.
Korotcov, A., Tkachenko, V., Russo, D. P.; et al. Comparison of Deep Learning with Multiple Machine Learning Methods and Metrics Using Diverse Drug Discovery Data Sets. Mol. Pharm. 2017, 14, 4462–4475.
Google Scholar |
Crossref |
Medline43.
Huuskonen, J. Estimation of Aqueous Solubility for a Diverse Set of Organic Compounds Based on Molecular Topology. J. Chem. Inf. Comput. Sci. 2000, 40, 773–777.
Google Scholar |
Crossref |
Medline44.
Perryman, A. L., Inoyama, D., Patel, J. S.; et al. Pruned Machine Learning Models to Predict Aqueous Solubility. ACS Omega 2020, 5, 16562–16567.
Google Scholar |
Crossref |
Medline45.
Nosengo, N. Can You Teach Old Drugs New Tricks? Nature 2016, 534, 314–316.
Google Scholar |
Crossref |
Medline46.
Ashburn, T. T., Thor, K. B. Drug Repositioning: Identifying and Developing New Uses for Existing Drugs. Nat. Rev. Drug Discov. 2004, 3, 673–683.
Google Scholar |
Crossref |
Medline47.
Marriner, S. E., Morris, D. L., Dickson, B.; et al. Pharmacokinetics of Albendazole in Man. Eur. J. Clin. Pharmacol. 1986, 30, 705–708.
Google Scholar |
Crossref |
Medline48.
Wishart, D. S., Knox, C., Guo, A. C.; et al. DrugBank: A Comprehensive Resource for In Silico Drug Discovery and Exploration. Nucleic Acids Res. 2006, 34, D668–D672.
Google Scholar |
Crossref |
Medline49.
Tetko, I. V., Tanchuk, V. Y. Application of Associative Neural Networks for Prediction of Lipophilicity in ALOGPS 2.1 Program. J. Chem. Inf. Comput. Sci. 2002, 42, 1136–1145.
Google Scholar |
Crossref |
Medline
留言 (0)