6. Ambure, Pravin, Stephen J. Barigye, and Rafael Gozalbes. "Machine Learning Approaches in Computational Toxicology Studies." Chemometrics and Cheminformatics in Aquatic Toxicology (2021): 125-155.
5. Ambure P, and M. Natalia Dias Soeiro Cordeiro. "Importance of Data Curation in QSAR Studies Especially While Modeling Large-Size Datasets." Ecotoxicological QSARs. Humana, New York, NY, 2020. 97-109.
4. Ambure P, and Kunal Roy. "Computer-Aided Drug Design for the Identification of Multi-Target Directed Ligands (MTDLs) in Complex Diseases: An Overview." Pharmaceutical Biocatalysis. Jenny Stanford Publishing, 2019. 99-159.
3. Ambure P, Roy K, Scoring Functions in Docking Experiments, IGI Global, USA, 2016. DOI: 10.4018/978-1-5225-0115-2.ch003
2. Ambure P, Aher RB, Roy K, Recent Advances in the Open Access Cheminformatics Toolkits, Software Tools, Workflow Environments, and Databases. In Methods in Pharmacology and Toxicology, Springer, 2015 (Click here).
1. Aher RB, Ambure P, Roy K, On Some Emerging Concepts in the QSAR Paradigm. In: Current Applications of Chemometrics (M Khanmohammadi, Ed), Nova Science Publishers, USA, 2014 (click here).
20. Ambure, Pravin, Arantxa Ballesteros, Francisco Huertas, Pau Camilleri, Stephen J. Barigye, and Rafael Gozalbes. "Development of Generalized QSAR Models for Predicting Cytotoxicity and Genotoxicity of Metal Oxides Nanoparticles." International Journal of Quantitative Structure-Property Relationships (IJQSPR) 5, 4 (2020): 15-32.
19) Ambure, P., Gajewicz-Skretna, A., Cordeiro, M. N. D., & Roy, K. (2019). New workflow for QSAR model development from small data sets: Small Dataset Curator and Small Dataset Modeler. Integration of data curation, exhaustive double cross-validation, and a set of optimal model selection techniques. Journal of Chemical information and Modeling, 59(10), 4070-4076.
18) Ambure, Pravin, Amit Kumar Halder, Humbert González-Díaz, and Maria Natália DS Dias Soeiro Cordeiro. "QSAR-Co: An Open Source Software for Developing Robust Multi-tasking or Multi-target Classification-Based QSAR Models." Journal of Chemical Information and Modeling, (2019).
17) Karmakar, A., Ambure, P., Mallick, T., Das, S., Roy, K. and Begum, N.A. Exploration of synthetic antioxidant flavonoid analogs as acetylcholinesterase inhibitors: an approach towards finding their quantitative structure–activity relationship. Medicinal Chemistry Research, 2019, 1-19.
16) Roy K, Ambure P, Kar S, How Precise Are Our Quantitative Structure–Activity Relationship Derived Predictions for New Query Chemicals?. ACS Omega, 3, 2018, 11392-11406.
15) Ambure, P., Bhat, J., Puzyn, T., & Roy, K. Identifying natural compounds as multi-target-directed ligands against Alzheimer’s disease: an in silico approach. Journal of Biomolecular Structure and Dynamics, 2018, 1-25.
14) Roy K, Ambure P, Kar S, Ojha P, Is it possible to improve the quality of predictions from an “intelligent” use of multiple QSAR/QSPR/QSTR models?. Journal of Chemometrics, January 2018, 32(4), e2992.
13) Roy K, Ambure P, Aher RB.How important is to detect systematic error in predictions and understand statistical applicability domain of QSAR models?.Chemom. Intell Lab Sys. 2017, 159, Pages 108–126.
12) Roy, K. and Ambure, P. The “double cross-validation” software tool for MLR QSAR model development, Chemom Intell Lab Sys,159, 15 December 2016, 108–126.
11) Ambure, P. and Roy, K. Understanding the structural requirements of cyclic sulfone hydroxyethylamines as hBACE1 inhibitors against Aβ plaques in Alzheimer's disease: a predictive QSAR approach. RSC Advances, 2016, 6(34), 28171-28186. (DOI: 10.1039/C6RA04104C)
10) Roy K, Das RN, Ambure P, Aher RB, Be aware of error measures. Further studies on validation of predictive QSAR models. Chemom Intell Lab Sys, 152, 2016, 18-33, http://dx.doi.org/10.1016/j.chemolab.2016.01.008.
9) Ambure, P., and K. Roy. "CADD modeling of multi-target drugs against Alzheimer's disease."Curr. Drug Targets, 2015, 18(5), 522-533. (Review Article)
8) Brahmachari, G., Choo, C., Ambure, P.,& Roy, K. In vitro evaluation and in silico screening of synthetic acetylcholinesterase inhibitors bearing functionalized piperidine pharmacophores. Bioorg. Med. Chem., 2015, 23(15), 4567-4575.
7) Roy, K., Kar, S., &Ambure, P. On a simple approach for determining applicability domain of QSAR models. Chemometr Intell. Lab. Syst., 2015, 145, 22-29.
6) Ambure, P., Aher, R. B., Gajewicz, A., Puzyn, T., & Roy, K. (2015). “NanoBRIDGES” software: Open access tools to perform QSAR and nano-QSAR modeling. Chemometr. Intell. Lab. Syst., 2015, 147, 1-13.
5) Ambure P, Roy K, Exploring Structural Requirements of Imaging Agents against Aβ Plaques in Alzheimer’s disease: A QSAR approach, Comb. Chem. High T Scr., 2015, 18, 411-419.
4) Ambure P, Roy K, Advances in quantitative structure-activity relationship models of anti-Alzheimer’s agents, Expert Opin. Drug Discov.9(6), 2014, 697-723. (Review Article)
3) Ambure P, Roy K, Exploring structural requirements of leads for improving activity and selectivity against CDK5/p25 in Alzheimer’s disease: An in silico approach. RSC Advances, 13, 2014, 6702-6709.
2) Ambure P, Kar S, Roy K, Pharmacophore Mapping-Based Virtual Screening Followed by Molecular Docking Studies in Search of Potential Acetylcholinesterase Inhibitors as Anti-Alzheimer's Agents. Biosystems, 116, 2014, 10-20.
1) Ambure P, Gangwal R, Sangamwar A,3D-QSAR and molecular docking analysis of biphenyl amide derivatives as p38α mitogen-activated protein kinase inhibitors,Mol. Divers., 16 (2), 2013, 377-388.