Please take note that the founders of 'MultiTargetAI' firm conducted all the research work mentioned below before creating the company. This is to showcase the skills and expertise of the MultiTargetAI team, considering that the firm is newly established.
Title of the project: “Identifying new anti-cancer drugs by computational multi-target approaches targeting the G-quadruplex DNA.“
Principal investigator: Dr. Jyotsna Bhat-Ambure
Project Coordinator: Dr. Rafael Gozalbes (Director, Moldrug AI Systems)
Budget: 172,932.48 €
Publication: 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. Click here
Software developed: G4-QuadScreen
Title of the project: “Computational evaluation of pharmaceutical and cosmetic materials: an approach towards a green and sustainable environment.”
Principal investigator: Dr. Pravin Ambure
Project Coordinator: Dr. Rafael Gozalbes (Director ProtoQSAR SL, Moldrug AI Systems)
Budget: 172,932.48 €
Weblink: Click here
Videolink: Click here
Title of the project: “An Open Source Software for Developing Robust Multitasking or Multitarget Classification-Based QSAR Models”
This project involves developing software entitled ‘QSAR-Co’, it was executed at the Department of Biochemistry and Chemistry, University of Porto, Portugal. The software was developed by Dr. Pravin Ambure in collaboration with Prof. Natalia Cordeiro (University of Porto, Portugal), Dr. Amit Kumar Haldar (University of Porto, Portugal), and Prof. Humberto González‐Díaz (University of the Basque Country, Spain).
Core Researcher: Dr. Pravin Ambure
Project Coordinator: Prof. Natalia Cordeiro (University of Porto, Portugal)
Publication: Ambure, P.; Halder, A. K.; Gonzalez Diaz, H.; Cordeiro, M. N. D. QSAR-Co: An Open Source Software for Developing Robust Multitasking or Multitarget Classification-Based QSAR Models. Journal of Chemical Information and Modeling 2019, 59 (6), 2538–2544. Click here
Software developed: QSAR-Co
Weblink: Click here
Title: “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“
Quantitative structure-activity relationship (QSAR) modeling is a well-known in silico technique with extensive applications in several major fields such as drug design, predictive toxicology, materials science, food science, etc. Handling small-sized datasets due to the lack of experimental data for specialized endpoints is a crucial task for the QSAR researcher. In this study, we have designed an integrated workflow/scheme capable of dealing with small dataset modeling that integrates dataset curation, “exhaustive” double cross-validation, and a set of optimal model selection techniques including consensus predictions. We have developed two software tools, namely, Small Dataset Curator, version 1.0.0, and Small Dataset Modeler, version 1.0.0, to effortlessly execute the proposed workflow. We have performed case studies employing seven diverse datasets to demonstrate the performance of the proposed scheme (including data curation) for small dataset QSAR modeling. The case studies also confirm the usability and stability of the developed software tools.
Core Researcher: Dr. Pravin Ambure
Project Coordinator: Prof. Kunal Roy (Jadavpur University, India), Dr. Agnieszka Gajewicz-Skretna (University of Gdansk, Poland), Prof. Natalia Cordeiro (University of Porto, Portugal)
Publication: Ambure, P.; Gajewicz-Skretna, A.; Cordeiro, M. N. D.; Roy, K. 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 2019, 59 (10), 4070–4076. Click here
Software developed: Small Dataset Modeler, Small Dataset Curator (freely available at the link given below)
Official Website link: Click here
Title of the project: “Computational Risk Assessment of Ionic Liquids Before their Use in Net Technologies (CRAB)“
The project was focused on the Computational Risk Assessment of ionic liquids. Under this project, Dr. Pravin Ambure developed a software RExIL version 1.0 software (a freely available standalone tool), which is exclusively developed for evaluating the risk and fate of newly designed (and already known) ionic liquids. This software package consists of three unique modules, i.e., i) Property Predictor, ii) Virtual Screener, and iii) Fate Predictor. All these modules work on a knowledge-rich database (knowledge base), which comprises 28 QSTR/QSPR models that encode several eco-toxicity, cytotoxicity, and physicochemical endpoints. Along with these modules, one can also execute and employ two widely used open-source standalone software tools, i.e., JChemPaint version 3.3 (a chemical structure editor) and PaDEL-Descriptors version 2.21 (a descriptor calculating software) and a ‘Universal SMILES Generator’ tool.
Fellow Researcher: Dr. Pravin Ambure
Project Coordinator: Prof. Tomasz Puzyn
Team members: Anita Sosnowska, Maciej Barycki, Anna Rybińska, Ewelina Wyrzykowska (University of Gdansk, Poland)
Publication: 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. Click here
Software developed: RExIL v1.0 (freely available at the link given below)
Official Website link: Click here
Title of the project: “Building Bridges between Specialists on Computational and Empirical Risk Assessment of Engineered Nanomaterials“
The project was focused on the development of new tools for computational risk assessment of engineered nanoparticles (NPs). Dr. Ambure worked on the development of novel NanoQSAR methodologies and on the development of a grouping and read-across platform for nanoparticles.
Fellow Researcher: Dr. Pravin Ambure
Project Coordinator: Prof. Tomasz Puzyn (University of Gdansk, Poland), Dr. Agnieszka Gajewicz-Skretna (University of Gdansk, Poland)
Publication: 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. Click here
Software developed: AM-MDI 1.2, Genetic Algorithm 1.4, Modified K-Medoids 1.2, NanoProfiler 1.2, S-MLR 1.2, vWSP 1.2 (freely available at the link given below)
Official Website link: Click here
