Reaxys academic collaboration network members
See recordings of webinars(opens in new tab/window) presented by our academic collaboration members as they share their expertise with using Reaxys in their work.
Dr. Andreas Brunschweiger
Professor of Chemistry & Chemical Biology, TU Dortmund University (Germany)
Research focus
DNA-encoded libraries (DELs) are a powerful technology for target-based small molecule screening. DEL design raises the question of chemical reaction selection for accessing chemical and especially structural diversity. However, chemical reaction space is vast, thus computer-assisted tools for reaction database mining are needed. His group develops a tool allowing for filtering relevant reactions from the reaction space and organizing the still impressive number of potentially useful reactions by clustering. This tool shall support decision-making for encoded library design.
Relevant publications
M. Potowski, F. Losch, E. Wünnemann, J. K. Dahmen, S. Chines, A. Brunschweiger. Screening of metal ions and organocatalysts on solid support-coupled DNA oligonucleotides guides design of DNA-encoded reactions. Chem. Sci., 2019, 10, 10481-10492.
M. Potowski, V. B. K. Kunig, L. Eberlein, A. Vakalopoulos, S. M. Kast, A. Brunschweiger. Chemically stabilized DNA barcodes for DNA-encoded chemistry. Angew. Chem. Int. Ed., 2021, 60, 19744-19749.
Connect with Dr. Brunschweiger(opens in new tab/window)
Leroy (Lee) Cronin
Professor, School of Chemistry, University of Glasgow, Scotland
Research focus
A Universal Approach to Reaction Informatics: Today it is possible to design and synthesize many of the physically allowed molecules and materials conceivable if practical, yet paradoxically it is not possible to reproduce or rerun these successful procedures with high reliability. This is because many of the conditions devised for the manual or semi-manual synthesis are not uniformly recorded and there is no standard way of recording reaction informatics. His group works on solving this problem by devising a universal approach to recording reaction informatics and chemical synthesis that allows them to translate all procedures, manual or automatic, to a new standard language for exploring chemical reactions and synthesis.
Furthermore, this new approach maps into a universal programming language for chemistry that is accessible to ALL synthetic chemists and will work on ALL robotic systems (subject to suitable specification), thus capable of universally turning code into reliable chemistry and materials processes.
Relevant publications
J. Granda, L. Donina, V. Dragone, D. –L. Long, L. Cronin. Controlling an organic synthesis robot with machine learning to search for new reactivity. Nature, 2018, 559, 377-381.
S. Steiner, J. Wolf, S. Glatzel, A. Andreou, J. Granda, G. Keenan, T. Hinkley, G. Aragon-Camarasa, P. J. Kitson, D. Angelone, L. Cronin. Organic synthesis in a modular robotic system driven by a chemical programming language. Science, 2019, 363, 144-152.
] S. Hessam M. Mehr, M. Craven, A. Leonov, G. Keenan, L. Cronin. A universal system for digitization and automatic execution of the chemical synthesis literature. Science, 2020, 370, 101-108A.
D. Caramelli, J. M. Granda, S. Hessam M. Mehr, D. Cambié, A. B. Henson and L. Cronin. Reactivity First Approach to Autonomous Discovery of New Chemistry. ChemRxiv, Theoretical and Computational Chemistry, 2021.
Latest honors & awards
NIH Integrated Challenge Prize, JSCC International Award (both in 2019)
Connect with Prof. Cronin
Alexei Lapkin
Professor, Department of Chemical Engineering & Biotechnology, University of Cambridge, United Kingdom
Research focus
Alexei Lapkin group’s current focus is on Sustainable Chemistry, Data Science and ML in Chemical Process Development. They work on the development of innovative digital technologies to address sustainability challenges in the chemical industries. Machine learning methods and Big Data approaches to design of reaction pathways for circular economy are two areas being developed. Prof. Lapkin’s group are also actively pursuing development of machine learning and AI methods for process development.
Relevant publications
J.M. Weber, P. Lio, A. Lapkin. Identification of strategic molecules for future circular supply chains using large reaction networks. React. Chem. Eng., (2019). DOI: 10.1039/c9re00213h(opens in new tab/window)
Connect with Prof. Lapkin
Timur I. Madzhidov
Associate Professor, Chair of Organic Chemistry, A.M. Butlerov Institute of Chemistry, Kazan Federal University (Russia)
Research focus
As team leader of the Chemoinformatics and Molecular Modelling Lab, Prof. Madzhidov’s primary research interest lies in reaction informatics. He is one of the contributors of CGRtools, a library for reaction processing, as well as other open-source tools for reaction informatics. He developed the machine learning-based approaches for reaction rate, equilibrium constant and enantioselectivity prediction, assessment of optimal reaction conditions. He also proposed an AI-based technique to invent novel reactions. Prof. Madzhidov is active in suggesting open-source tools for chemical reaction databasing (CGRdb, RePathDB) and reaction data curation and homogenization, knowledge extraction from large reaction datasets.
Prof. Madzhidov has developed several approaches for drug design based on the application of pharmacophores and multi-instance learning.
Relevant publications
Automatized assessment of protective group reactivity: a step toward big reaction data analysis. AI Lin, TI Madzhidov, O Klimchuk, RI Nugmanov, IS Antipin, A Varnek. Journal of chemical information and modeling, 56 (11), 2140-2148. https://pubs.acs.org/doi/abs/10.1021/acs.jcim.6b00319(opens in new tab/window)
Artificial intelligence in synthetic chemistry: achievements and prospects. II Baskin, TI Madzhidov, IS Antipin, AA Varnek. Russian Chemical Reviews, 86 (11), 1127. https://iopscience.iop.org/article/10.1070/RCR4746/meta(opens in new tab/window)
Reaction Data Curation I: Chemical Structures and Transformations Standardization. T Gimadiev, A Lin, VA Afonina, D Batyrshin, RI Nugmanov, T Akhmetshin, P Sidorov, N Duybankova, J Verhoeven, J Wegner, H Ceulemans, A Gedich, TI Madzhidov, A Varnek. Molecular Informatics, 2021. https://doi.org/10.1002/minf.202100119(opens in new tab/window)
Connect with Prof. Madzhidov
Guillermo Restrepo
Professor, Max Planck Institute for Mathematics, Sciences & Interdisciplinary Center for Bioinformatics, Leipzig University (Germany)
Research focus
Guillermo Restrepo has been an active member since 2017. He focuses on the evolution of chemical knowledge and the history of chemistry to study historical expansion of the chemical space and evolution of the periodic system.
Relevant publications
Eugenio J. Llanos, Wilmer Leal, Duc H. Luu, Jürgen Jost, Peter F. Stadler, and Guillermo Restrepo. Exploration of the chemical space and its three historical regimes. PNAS, 2019. https://www.pnas.org/doi/10.1073/pnas.1816039116(opens in new tab/window)
Wilmer Leal, Eugenio J. Llanos, Peter F. Stadler, Juergen Jost, Guillermo Restrepo. ChemRxiv, 2019. https://chemrxiv.org/engage/chemrxiv/article-details/60c743f0702a9b4c2918a6ea(opens in new tab/window)
Latest honors & awards
2020 Gmelin-Beilstein Denkmünze of the German Chemical Society