STREAM · Statistical Tools for Reaction Efficacy AssessMent: Prediction and Understanding in Organocatalyst Discovery
Horizon 2020 — Marie Skłodowska-Curie Actions
- Duration
- 2018-09-01 → 2021-08-31
- EU contribution
- €257,861
- Participants
- 2
- Scheme
- MSCA-IF
Lines connect the coordinator with its partners.
Results in brief
Statistical Tools for Reaction Efficacy AssessMent: Prediction and Understanding in Organocatalyst Discovery
The need for chiral compounds, often as single enantiomers, has escalated abruptly in recent years, driven particularly by the demands of the pharmaceutical industry. For example, two-thirds of prescription drugs are chiral, with the majority of new chiral drugs being administered as single enantiomers. Moreover, chiral compounds have found use as agricultural chemicals, flavors, fragrances, and materials. In response to this widespread demand, chemists have recorded impressive successes for the preparation of stereochemically pure compounds. Both small molecule catalysts and enzymes have been developed for such purposes. However, it was only relatively recently that such asymmetric catalysis, with enantiomeric excesses approaching 100%, was achieved with synthetic catalysts. Therefore, innovations in asymmetric reaction/catalyst development represents a goal of considerable significance for world-wide health and would improve the lives of millions of people. Thus, the overarching objective of the action was to develop data science-driven workflows that enable the prediction of experimental enantioselectivity. In these studies, we concluded that multivariate linear regression can correlate the structure of every reaction component (substrate, catalyst, and conditions) to enantioselectivity data. The resulting statistical models can anticipate how changing the substrate and catalyst structure alters the reaction outcome. We showed that these general reaction models are accurate in predicting out-of-sample, and provide guidance in reaction application to include additional substrates.
Data: CORDIS, © European Union
Project objective
This proposal seeks to further develop Statistical Tools for Reaction Efficacy AssessMent (STREAM). The goal is to build small ligand sets to screen for statistical training of correlations, identify the parameters that are most likely to describe selectivity trends for particular catalysts, and develop a virtual screening deck that allows for rapid identification of improved performers. For this proposal we will evaluate peptide-based catalysts and N-heterocyclic carbenes (NHC) on various mechanistically distinct catalytic processes, to predict and understand reaction performance. The proposed STREAM methodology not only allows for effective prediction, and thus the design of better performing catalysts, but also is a contemporary approach to mechanistic study. These modern tools are general and applicable in principle to any chemical system, thus, directly relevant to each research group developing asymmetric or site-selective reactions. The experienced researcher proposes to undertake the outgoing phase within Professor Matthew Sigman’s research laboratory (University of Utah, USA) and undertake the incoming phase within Professor Frank Glorius’ research laboratory (Westfälische Wilhelms-Universität – Münster, Germany); both proposed supervisors are experts in the mechanistic study of asymmetric catalysis (with complementary skill sets) and highly prominent figures in the field of catalyst design.
Original text from CORDIS.
Participants
- UNIVERSITAET MUENSTER · MuensterCoordinatorGermany
- UNIVERSITY OF UTAH · Salt Lake City UtahUnited States
Links
Data: CORDIS, © European Union