Please be aware that the publications mentioned below were authored by the company's founders prior to establishing MultiTargetAI. This is intended to emphasize the MultiTargetAI team's capabilities and expertise, given the recent establishment of the company.
List of Publications
Bhat-Ambure, J.; Ambure, P.; Serrano-Candelas, E.; Galiana-Roselló, C.; Gil-Martínez, A.; Guerrero, M.; Martin, M.; González-García, J.; García-España, E.; Gozalbes, R. G4-QuadScreen: A Computational Tool for Identifying Multi-Target-Directed Anticancer Leads against G-Quadruplex DNA. Cancers 2023, 15 (15), 3817.
De, P.; Kar, S.; Ambure, P.; Roy, K. Prediction Reliability of QSAR Models: An Overview of Various Validation Tools. Archives of Toxicology 2022, 96 (5), 1279–1295.
Halder, A. K.; Ambure, P.; Perez-Castillo, Y.; Cordeiro, M. N. D. Turning Deep-Eutectic Solvents into Value-Added Products for CO2 Capture: A Desirability-Based Virtual Screening Study. Journal of CO2 Utilization 2022, 58, 101926.
[Book Chapter] Ambure, P.; Barigye, S. J.; Gozalbes, R. Machine Learning Approaches in Computational Toxicology Studies. Chemometrics and Cheminformatics in Aquatic Toxicology 2021, 125–155.
Puzyn, T.; Sosnowska, A.; Barycki, M.; Rybińska-Fryca, A.; Ambure, P.; Wyrzykowska, E. Computational Tools for Assessing the Risk of Ionic Liquids before Their Use in New Technologies; Laboratory of Environmental Chemometrics, 2019.
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 2019, 37 (5), 1282–1306.
Karmakar, A.; Ambure, P.; Mallick, T.; Das, S.; Roy, K.; 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, 28, 723–741.
Roy, K.; Ambure, P.; Kar, S. How Precise Are Our Quantitative Structure-Activity Relationship Derived Predictions for New Query Chemicals? ACS omega 2018, 3 (9), 11392–11406.
Roy, K.; Ambure, P.; Kar, S.; Ojha, P. K. Is It Possible to Improve the Quality of Predictions from an “Intelligent” Use of Multiple QSAR/QSPR/QSTR Models? Journal of Chemometrics 2018, e2992.
Roy, K.; Ambure, P.; Kar, S. Prediction Reliability Indicator: A New Tool to Judge the Quality of Predictions from QSAR Models for New Query Compounds. 24th May 2018 in MOL2NET 2018, International Conference on Multidisciplinary Sciences; MDPI AG.
Jana, S.; Jana, J.; Patra, K.; Mondal, S.; Bhat, J.; Sarkar, A.; Sengupta, P.; Biswas, A.; Mukherjee, M.; Tripathi, S. P.; others. LINCRNA00273 Promotes Cancer Metastasis and Its G-Quadruplex Promoter Can Serve as a Novel Target to Inhibit Cancer Invasiveness. Oncotarget 2017, 8 (66), 110234.
Bhat, J.; Mondal, S.; Sengupta, P.; Chatterjee, S. In Silico Screening and Binding Characterization of Small Molecules toward a G-Quadruplex Structure Formed in the Promoter Region of c-MYC Oncogene. ACS omega 2017, 2 (8), 4382–4397.
[Book Chapter] Ambure, P.; Roy, K. CADD Modeling of Multi-Target Drugs against Alzheimer’s Disease. Current Drug Targets 2017, 18 (5), 522–533.
Roy, K.; Ambure, P.; Aher, R. B. How Important Is to Detect Systematic Error in Predictions and Understand Statistical Applicability Domain of QSAR Models? Chemometrics and Intelligent Laboratory Systems 2017, 162, 44–54.
Bhat, J.; Chatterjee, S. Skeleton Selectivity in Complexation of Chelerythrine, Chelerythrine-like Natural Plant Alkaloids with the G-Quadruplex Formed at the Promoter of c-MYC Oncogene: In Silico Exploration. RSC Advances 2016.
Kaulage, M.; Maji, B.; Bhat, J.; Iwasaki, Y.; Chatterjee, S.; Bhattacharya, S.; Muniyappa, K. Discovery and Structural Characterization of G-Quadruplex DNA in Human Acetyl-CoA Carboxylase Gene Promoters: Its Role in Transcriptional Regulation and as a Therapeutic Target for Human Disease. Journal of Medicinal Chemistry 2016, 59 (10), 5035–5050.
Roy, K.; Ambure, P. The “Double Cross-Validation” Software Tool for MLR QSAR Model Development. Chemometrics and Intelligent Laboratory Systems 2016, 159, 108–126.
Mondal, S.; Bhat, J.; Jana, J.; Mukherjee, M.; Chatterjee, S. Reverse Watson–Crick G–G Base Pair in G-Quadruplex Formation. Molecular BioSystems 2016, 12 (1), 18–22.
Saha, T.; Guha, D.; Manna, A.; Panda, A. K.; Bhat, J.; Chatterjee, S.; Sa, G. G-Actin Guides P53 Nuclear Transport: Potential Contribution of Monomeric Actin in Altered Localization of Mutant P53. Scientific Reports 2016, 6 (1), 32626.
[Book Chapter] Ambure, P.; Roy, K. Scoring Functions in Docking Experiments. In Methods and Algorithms for Molecular Docking-Based Drug Design and Discovery; IGI Global, 2016; pp 54–98.
Roy, K.; Das, R. N.; Ambure, P.; Aher, R. B. Be Aware of Error Measures. Further Studies on Validation of Predictive QSAR Models. Chemometrics and Intelligent Laboratory Systems 2016, 152, 18–33.
Ambure, P.; 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.
Chakraborty, S.; Ghosh, S.; Banerjee, B.; Santra, A.; Bhat, J.; Adhikary, A.; Chatterjee, S.; Misra, A. K.; Sen, P. C. Mephebrindole, a Synthetic Indole Analog Coordinates the Crosstalk between P38MAPK and EIF2α/ATF4/CHOP Signalling Pathways for Induction of Apoptosis in Human Breast Carcinoma Cells. Apoptosis 2016, 21, 1106–1124.
Roy, K.; Kar, S.; Ambure, P. On a Simple Approach for Determining Applicability Domain of QSAR Models. Chemometrics and Intelligent Laboratory Systems 2015, 145, 22–29.
Grewal, B. K.; Bhat, J.; Sobhia, M. E. Molecular Dynamics Approach to Probe PKCβII–Ligand Interactions and Influence of Crystal Water Molecules on These Interactions. Expert Opinion on Therapeutic Targets 2015, 19 (1), 13–23.
Ambure, P.; Aher, R. B.; Gajewicz, A.; Puzyn, T.; Roy, K. “NanoBRIDGES” Software: Open Access Tools to Perform QSAR and Nano-QSAR Modeling. Chemometrics and Intelligent Laboratory Systems 2015, 147, 1–13.
Ghosh, A.; Pithadia, A. S.; Bhat, J.; Bera, S.; Midya, A.; Fierke, C. A.; Ramamoorthy, A.; Bhunia, A. Self-Assembly of a Nine-Residue Amyloid-Forming Peptide Fragment of SARS Corona Virus E-Protein: Mechanism of Self Aggregation and Amyloid-Inhibition of HIAPP. Biochemistry 2015, 54 (13), 2249–2261.
Nakka, K. K.; Chaudhary, N.; Joshi, S.; Bhat, J.; Singh, K.; Chatterjee, S.; Malhotra, R.; De, A.; Santra, M. K.; Dilworth, F. J.; others. Nuclear Matrix-Associated Protein SMAR1 Regulates Alternative Splicing via HDAC6-Mediated Deacetylation of Sam68. Proceedings of the National Academy of Sciences 2015, 112 (26), E3374–E3383.
[Book Chapter] Ambure, P.; Aher, R. B.; Roy, K. Recent Advances in the Open Access Cheminformatics Toolkits, Software Tools, Workflow Environments, and Databases. In Methods in Pharmacology and Toxicology; Springer, 2015.
Kodisundaram, P.; Duraikannu, A.; Balasankar, T.; Ambure, P. S.; Roy, K. Cytotoxic and Antioxidant Activity of a Set of Hetero Bicylic Methylthiadiazole Hydrazones: A Structure-Activity Study. International Journal of Molecular and Cellular Medicine 2015, 4 (2), 128.
Brahmachari, G.; Choo, C.; Ambure, P.; Roy, K. In Vitro Evaluation and in Silico Screening of Synthetic Acetylcholinesterase Inhibitors Bearing Functionalized Piperidine Pharmacophores. Bioorganic & Medicinal Chemistry 2015, 23 (15), 4567–4575.
Ambure, P.; Roy, K. Exploring Structural Requirements of Imaging Agents against Aβ Plaques in Alzheimer’s Disease: A QSAR Approach. Combinatorial Chemistry & High Throughput Screening 2015, 18 (4), 411–419.
Chaudhary, N.; Nakka, K.; Chavali, P.; Bhat, J.; Chatterjee, S.; Chattopadhyay, S. SMAR1 Coordinates HDAC6-Induced Deacetylation of Ku70 and Dictates Cell Fate upon Irradiation. Cell Death & Disease 2014, 5 (10), e1447–e1447.
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 2014, 4 (13), 6702–6709.
[Book Chapter] Aher, R.; Ambure, P.; Roy, K. On Some Emerging Concepts in the QSAR Paradigm. In Current Applications of Chemometrics; Nova Science, 2014.
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 2014, 116, 10–20.
Chakraborty, P.; Ambure, P.; Roy, K. DTC-MLR: An Open Access Web-Based Tool for MLR Model Development and Validation with Application in QSAR Studies. Journal of Engineering, Science & Management Education 2014, 7 (3), 172–177.
Kare, P.; Bhat, J.; Sobhia, M. E. Structure-Based Design and Analysis of MAO-B Inhibitors for Parkinson’s Disease: Using in Silico Approaches. Molecular diversity 2013, 17, 111–122.
Sobhia, M. E.; Grewal, B. K.; Bhat, J.; Rohit, S.; Punia, V. Protein Kinase C ΒII in Diabetic Complications: Survey of Structural, Biological and Computational Studies. Expert opinion on therapeutic targets 2012, 16 (3), 325–344.
Ambure, P. S.; Gangwal, R. P.; Sangamwar, A. T. 3D-QSAR and Molecular Docking Analysis of Biphenyl Amide Derivatives as P38 α Mitogen-Activated Protein Kinase Inhibitors. Molecular diversity 2012, 16 (2), 377–388.
