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AI3SD Video: Discovery of synthesisable organic materials

AI3SD Video: Discovery of synthesisable organic materials
AI3SD Video: Discovery of synthesisable organic materials
The computational discovery of new materials with useful properties is currently hindered by the difficulty in transitioning from a computational prediction to synthetic realisation. Attempts at experimental validation are often time-consuming, expensive, and frequently, the key bottleneck of material discovery.[1] Porous organic cages (POCs) have been discovered as a possible alternative material for molecular separations, catalysis, and sensing applications.[2] For POCs, a priori property prediction is possible,[3] however, it can be time-consuming and computationally expensive to explore a large number of possible candidate molecules. Despite being able to predict materials with exceptional properties, it is often challenging to predict whether it is possible to synthetically realise a potential candidate compound. In the field of drug discovery, machine learning techniques have been able to readily distinguish between synthesisable and unsynthesisable molecules, accelerating the drug discovery process.[4] Incorporating a synthetic accessibility scoring function into the precursor selection process favoured less complex, synthetically accessible precursors; thus, bridging the gap between computational screening and experimental synthesis of POCs. Using data-driven synthetic accessibility scoring techniques and high-throughput experimentation, we developed a POC screening workflow to accelerate discovery of experimentally realisable POC candidates, which we demonstrate using high-throughput, automated experimentation. Existing measures of synthetic accessibility are often tailored towards predicting synthesisable drug-like molecules, whose synthetic requirements often do not align with those of materials discovery programs. By redefining synthetic accessibility as a classification problem, we were able to develop an alternative model able to predict synthesisable materials precursors.[5] Biasing towards easy-to-synthesise precursors facilitated the synthesis of several precursors predicted to form shape-persistent POCs. Using these novel precursors, we were able to construct a precursor library able to be combined using automation, enabling the accelerated discovery of POCs and the construction of an experimentally derived POC reaction dataset. Using this dataset, we aim to develop a model able to predict POC formation, a question challenging to address using conventional computational methods. [1] Szczypiński, F. T.; Bennett, S.; Jelfs, K. E. Can We Predict Materials That Can Be Synthesised? Chem. Sci. 2021, 12 (3), 830–840. [2] Hasell, T.; Cooper, A. I. Porous Organic Cages: Soluble, Modular and Molecular Pores. Nat Rev Mater 2016, 1 (9), 16053. [3] Greenaway, R. L.; Jelfs, K. E. High‐Throughput Approaches for the Discovery of Supramolecular Organic Cages. ChemPlusChem 2020, 85 (8), 1813–1823. [4] C. W. Coley, L. Rogers, W. H. Green and K. F. Jensen, SCScore: Synthetic Complexity Learned from a Reaction Corpus, J. Chem. Inf. Model., 2018, 58, 252–261 [5] Bennett, S., Szczypiński, F.T., Turcani, L., Briggs, M.E., Greenaway, R.L., and Jelfs, K.E. (2021) Materials Precursor Score: Modeling Chemists’ Intuition for the Synthetic Accessibility of Porous Organic Cage Precursors. J. Chem. Inf. Model., 61 (9), 4342–4356.
Bennett, Steven
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Frey, Jeremy G.
ba60c559-c4af-44f1-87e6-ce69819bf23f
Kanza, Samantha
b73bcf34-3ff8-4691-bd09-aa657dcff420
Niranjan, Mahesan
5cbaeea8-7288-4b55-a89c-c43d212ddd4f
Bennett, Steven
43b1bd8e-1cc9-44dc-a7ab-2122e2b6a2f7
Frey, Jeremy G.
ba60c559-c4af-44f1-87e6-ce69819bf23f
Kanza, Samantha
b73bcf34-3ff8-4691-bd09-aa657dcff420
Niranjan, Mahesan
5cbaeea8-7288-4b55-a89c-c43d212ddd4f

Bennett, Steven (2022) AI3SD Video: Discovery of synthesisable organic materials. Frey, Jeremy G., Kanza, Samantha and Niranjan, Mahesan (eds.) AI4SD Network+ Conference, Chilworth Manor , Southampton, United Kingdom. 01 - 03 Mar 2022. (doi:10.5258/SOTON/AI3SD0196).

Record type: Conference or Workshop Item (Other)

Abstract

The computational discovery of new materials with useful properties is currently hindered by the difficulty in transitioning from a computational prediction to synthetic realisation. Attempts at experimental validation are often time-consuming, expensive, and frequently, the key bottleneck of material discovery.[1] Porous organic cages (POCs) have been discovered as a possible alternative material for molecular separations, catalysis, and sensing applications.[2] For POCs, a priori property prediction is possible,[3] however, it can be time-consuming and computationally expensive to explore a large number of possible candidate molecules. Despite being able to predict materials with exceptional properties, it is often challenging to predict whether it is possible to synthetically realise a potential candidate compound. In the field of drug discovery, machine learning techniques have been able to readily distinguish between synthesisable and unsynthesisable molecules, accelerating the drug discovery process.[4] Incorporating a synthetic accessibility scoring function into the precursor selection process favoured less complex, synthetically accessible precursors; thus, bridging the gap between computational screening and experimental synthesis of POCs. Using data-driven synthetic accessibility scoring techniques and high-throughput experimentation, we developed a POC screening workflow to accelerate discovery of experimentally realisable POC candidates, which we demonstrate using high-throughput, automated experimentation. Existing measures of synthetic accessibility are often tailored towards predicting synthesisable drug-like molecules, whose synthetic requirements often do not align with those of materials discovery programs. By redefining synthetic accessibility as a classification problem, we were able to develop an alternative model able to predict synthesisable materials precursors.[5] Biasing towards easy-to-synthesise precursors facilitated the synthesis of several precursors predicted to form shape-persistent POCs. Using these novel precursors, we were able to construct a precursor library able to be combined using automation, enabling the accelerated discovery of POCs and the construction of an experimentally derived POC reaction dataset. Using this dataset, we aim to develop a model able to predict POC formation, a question challenging to address using conventional computational methods. [1] Szczypiński, F. T.; Bennett, S.; Jelfs, K. E. Can We Predict Materials That Can Be Synthesised? Chem. Sci. 2021, 12 (3), 830–840. [2] Hasell, T.; Cooper, A. I. Porous Organic Cages: Soluble, Modular and Molecular Pores. Nat Rev Mater 2016, 1 (9), 16053. [3] Greenaway, R. L.; Jelfs, K. E. High‐Throughput Approaches for the Discovery of Supramolecular Organic Cages. ChemPlusChem 2020, 85 (8), 1813–1823. [4] C. W. Coley, L. Rogers, W. H. Green and K. F. Jensen, SCScore: Synthetic Complexity Learned from a Reaction Corpus, J. Chem. Inf. Model., 2018, 58, 252–261 [5] Bennett, S., Szczypiński, F.T., Turcani, L., Briggs, M.E., Greenaway, R.L., and Jelfs, K.E. (2021) Materials Precursor Score: Modeling Chemists’ Intuition for the Synthetic Accessibility of Porous Organic Cage Precursors. J. Chem. Inf. Model., 61 (9), 4342–4356.

Video
ai4sd_march_2022_day_2_StevenBennett - Version of Record
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Published date: 2 March 2022
Additional Information: Steven Bennett obtained his Master’s degree in Chemistry in 2018 from University College London (UCL). In October 2018, he joined the Jelfs Group at Imperial College London to work on the computational discovery of synthetically accessible functional materials, specifically porous organic cages. This work is supported by a PhD studentship from the Leverhulme Trust via the Leverhulme Centre for Functional Materials, which aims to develop his interest in sustainable, advanced material development.
Venue - Dates: AI4SD Network+ Conference, Chilworth Manor , Southampton, United Kingdom, 2022-03-01 - 2022-03-03

Identifiers

Local EPrints ID: 470004
URI: http://eprints.soton.ac.uk/id/eprint/470004
PURE UUID: 030bda93-6afd-4326-8cea-89739d005d49
ORCID for Jeremy G. Frey: ORCID iD orcid.org/0000-0003-0842-4302
ORCID for Samantha Kanza: ORCID iD orcid.org/0000-0002-4831-9489
ORCID for Mahesan Niranjan: ORCID iD orcid.org/0000-0001-7021-140X

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Date deposited: 30 Sep 2022 16:32
Last modified: 01 Oct 2022 01:56

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Contributors

Author: Steven Bennett
Editor: Jeremy G. Frey ORCID iD
Editor: Samantha Kanza ORCID iD
Editor: Mahesan Niranjan ORCID iD

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