Abstracts & Pre-Reading Material
Some speakers have provided the following abstracts and references which might be of interest ahead of their talks.
Alessandro Troisi Dept. Chemistry, University of Liverpool, United Kingdom
We describe a general high-throughput virtual screening procedure to discover new molecular materials for organic electronics. We show that many ideas of cheminformatics for medicinal chemistry can be transferred to the domain of optoelectronics ((i) low energy emitters; (ii) dual emitters; (iii) singlet fission molecules; (iv) thin film transistors). Experimental verification is presented for some of the examples shown. The approach led to the construction of a software platform for the discovery of new organic electronics materials (DIADEM) where the search and refinement of computed property is linked with the chemical supply chain and predictions can be immediately verified experimentally. The use of such datasets in conjunction with machine learning are also discussed.
Abbie Trewin (Department of Chemistry, Lancaster University)
Artificial synthesis of amorphous framework materials designed for energy applications
We show computational methodology that enables us to artificially synthesise amorphous framework materials in-silico, using the real-world conditions and following the catalytic mechanisms, so that we can design materials for targeted applications.
Amorphous porous framework materials, including hyper-crosslinked polymers (HCPs), have been suggested as ideal materials for use in energy storage, particularly as the anode material in lithium ion batteries (LIBs). However, it is very challenging to rationalise their atomic structure and hence rational design and full understanding of their properties is not currently possible.
An example of a HCP for an energy application is organically synthesised porous carbon (OSPC-1), which was specifically designed using the structural building units, ethynyl methane to form a 3-D connected carbon amorphous network. Three highlights brought this to the attention of a wide audience: (i) an unexpected electron conductivity, (ii) an ability to be charged with more than twice the amount of lithium as compared to the graphite electrodes in state-of-the-art lithium ion batteries, and (iii) an ability to be charged at a high rate without any signs of detrimental lithium plating or dendrites that can cause explosions of devices. This opens up the potential to discover other amorphous framework materials, including conjugated microporous polymers (CMPs), for use as anodes with exceptional storage, safety, and charging properties as well as for other energy applications.
Rational design approaches have been used to great effect in the discovery of crystalline materials. However, amorphous framework materials are kinetic products, and we therefore cannot use the same approaches. The amorphous nature of HCPs means that they are highly dependent on the synthetic conditions employed, including the reaction solvent and catalyst. The lack of a defined unit cell also makes characterisation challenging, meaning simulations are one of the best available tools to understand the atomic structure.
Here we will show that the use of our in-house developed Ambuild can be used to aid the design of amorphous framework materials and can reveal the hidden complexity of the framework structure.
Photo‐Modulating CO2 Uptake of Hypercross‐linked Polymers Upcycled from Polystyrene Waste Liu, A., Mollart, C., Trewin, A., Fan, X., Lau, C. H., ChemSusChem 2023, 16, e202300019 https://doi.org/10.1002/cssc.202300019
Artificial synthesis of covalent triazine frameworks for local structure and property determination C. Mollart, S. Holcroft, M.J.G. Peach, A. Rowling, A.Trewin* Phys.Chem.Chem.Phys, 24 (34), 20025,2022.
Rationalising the Influence of Solvent Choice on the Porosity of Conjugated Microporous Polymers Catherine Mollarta and Abbie Trewin* Phys. Chem. Chem. Phys., 2020, 22, 38, 21642-21645,
Artificial Synthesis of Conjugated Microporous Polymer via Sonogashira-Hagihara Coupling, Jens M. H. Thomas, Catherine Mollart, Lauren Turner, Patrick Heasman, Pierre Fayon and Abbie Trewin, J. Phys. Chem. B, 124,33,7318-7326
Lauren Reid (Medchemica)
Using SARkush®, an automated Markush-like structure generator, to advance SAR analysis.
Markush structures and R group tables are a simple and intuitive way of summarising structure activity relationships (SAR) of chemical series. Their applications range from communicating SAR, to defining patent scopes, to providing input for various modelling approaches. Despite widespread use, producing Markush structures from large compound datasets is a tedious and time-consuming task, requiring a human to manually curate compounds into chemical series / cores and input them into R group decomposition algorithms. MedChemica’s Markush-like structure generator, SARkush®, automatically clusters compounds based on matched molecular pair (MMP)-networks and generic-atom scaffolds, and generates Markush-like depictions and decomposition tables. Importantly, the output of SARkush® provides an effective summary of the SAR and can be inputted easily into further modelling techniques, making the software an essential tool for both medicinal and computational chemists. The talk will describe the cheminformatics processes behind SARkush® and will demonstrate a number of uses, including summarising the lead optimisation story of the COVID-Moonshot project, analysing patent corpuses and building Free-Wilson models.
Simmons E. Markush structure searching over the years. World Patent Information. 2003, 25(3), 195-202.
Dossetter A.G., Griffen E.J. and Leach A.G. Matched Molecular Pair Analysis in drug discovery. Drug discovery today. 2013, 18(15-16), 724-731.
Bemis, G.W. and Murcko, M.A. The properties of known drugs 1. Molecular frameworks. J Med Chem. 1996, 39(15), 2887-2893.
Consortium TCOVIDM, Chodera J, Lee A, London N, Delft Fvon. COVID Moonshot: Open Science Discovery of SARS-CoV-2 Main Protease Inhibitors by Combining Crowdsourcing, High-Throughput Experiments, Computational Simulations, and Machine Learning. ChemRxiv. Cambridge: Cambridge Open Engage; 2020; This content is a preprint and has not been peer-reviewed.
Free S.M., Wilson J.W. A Mathematical Contribution to Structure-Activity Studies. J Med Chem. 1964, 7(4), 395-399.
Steve Enoch (Liverpool John Moores University)
Read-Across of the Genotoxicity of Residues in Pesticides Products Using a Workflow Approach for the Definition of Similarity
Crop protection products are used for prevention of crop infestation by disease and pests. To fully assess the safety profile of active ingredients, consideration of metabolites and degradation products must be completed. Genotoxicity is one of the key effects that must be investigated, specifically the ability to cause either gene mutation, or structural and/or numerical chromosomal aberrations. Unadopted guidance from European Food Safety Authority (EFSA) proposes a workflow to conclude on the genotoxic potential residues of the active ingredient. This guidance suggests predicting genotoxicity endpoints of substances without available data by read-across.
The definition of chemical similarity is the key step in the development of robust and repeatable read-across predictions. Currently, most applied methods involve either the use of structural alerts or chemical fingerprints to define similarity. However, both approaches require significant amount of expert judgement, either in initial development (structural alerts) or application (fingerprints). In addition, metabolic similarity within the chemical category increases confidence in a read-across prediction and thus contributes relevant information for a chemical similarity assessment. Thus, the ability to define scaffolds for a group of pesticides with a common mode of action would be a key advantage enabling pesticide residues to be grouped based on the presence of such scaffolds.
To address this challenge, a workflow has been developed in the KNIME workflow environment based on defining the common scaffolds present in a pesticide dataset. This approach defines the scaffolds as rings which is then extended to define a maximum common sub-structure present in all members of the resulting chemical category. When applied to a high-quality curated dataset of crop protection active ingredients and their residues the resulting categories were shown to have a high degree of metabolic similarity. The method was applied to develop several read-across case studies for pesticide residues lacking genotoxicity data. The approach offers a unique, automated, method for the development of metabolically similar chemical categories from which read-across predictions for genotoxicity can be made.
The research presented in this project is funded by CropLife Europe.
• EFSA, Guidance on the establishment of the residue definition for dietary risk assessment. EFSA Journal 2016, 14 (12)
• EFSA, Scientific opinion on genotoxicity testing strategies applicable to food and feed safety assessment. EFSA Journal 2011, 9 (9)
• Enoch et al, Sub-structure-based category formation for the prioritisation of genotoxicity hazard assessment for pesticide residues: Sulphonyl ureas. Regulatory Toxicology and Pharmacology 2022, article 105115
• Enoch et al, Sub-structure-based category formation for the prioritisation of genotoxicity hazard assessment for pesticide residues (part 2): Triazoles. Regulatory Toxicology and Pharmacology 2022, article 105237
Rachael Pirie (NextMove Software)
Can You Hear The Shape Of A Drug?
The similar property principle has been used widely in early-stage drug discovery, as only a small handful of known binders are required as templates to rapidly screen large databases. Molecular shape is a useful tool when considering similarity, as the shape of a known binder can be used as a proxy for the shape of the binding pocket when structural information is unavailable, as well as allowing consideration of conformers, and scaffold hopping to identify diverse hits. Compared to representations based on atomic distances or volume, approximations based on the molecular surface are not yet widely adopted.
This talk will present two novel molecular surface shape descriptors derived from the theory of Riemannian geometry. Both descriptors are alignment-free, quick to compute and easy to compare. An overview of both methods will be given, and the results of a retrospective benchmarking study using the DUD-E community benchmarking set, comparing performance to existing shape similarity methods, will be discussed.
Elena De Orbe (Astrazeneca)
Design of Site-Specific ADCs through Computational Modelling
Antibody-Drug Conjugates (ADCs) have emerged as a powerful modality of oncology therapeutics for targeted delivery since the first approval in 2000. Tethering a small-molecule drug to a monoclonal antibody via a synthetic linker enables the selective delivery of a highly cytotoxic payload to specific tumour cells expressing the target antigen. Traditionally, the linker-payload was attached to the antibody through random or stochastic conjugation. However, the conjugation to specific residues at the antibody surface leads to homogeneous and more stable products with desired physicochemical properties similar to the ones of the naked antibodies. Through a cross-functional collaboration, we are developing an automated ADC modelling workflow to efficiently identify the best antibody sites for cysteine engineering and subsequent site-specific conjugation. Facing the challenge of modelling novel biologics, this strategy will accelerate the design of the next generation of ADCs with improved developability properties for cancer patients.
Adam Nelson (University of Leeds)
How Best to Explore Chemical Space for Bioactive Molecular Discovery?
Natural products continue to inspire both drug discovery and chemical biology. Natural products are necessarily biologically-relevant because they arise through the evolution of biosynthetic pathways, driven by functional benefit to the host organism. In this lecture, two complementary and unified approaches for the synthesis and elaboration of fragments will be described that have taken some inspiration from natural products and biosynthesis.
First, the design and synthesis of natural product-inspired scaffolds will be described. The scaffolds were designed to have high natural product-likeness, and to be decorated to yield screening compounds with lead-like molecular properties. To demonstrate their biological relevance, a set of fragments has been prepared from the scaffolds, and has been screened against a disparate range of protein targets using high-throughput protein crystallography. It is demonstrated that the fragments can provide distinctive starting points for the discovery of modulators of epigenetic protein targets.
Second, a novel discovery approach – activity-directed synthesis (ADS) – will be described. Unlike traditional medicinal chemistry workflows, ADS deliberately harnesses the promiscuity of reactions that can yield alternative products. Although such reactions explore diverse chemical space, they are rarely exploited in current discovery approaches which generally require high-yielding reactions with predictable products. In each round of ADS, a reaction array is performed with outcomes that are critically dependent on the specific substrates/catalysts/conditions used. To steer reactions towards bioactive products, subsequent arrays are informed by the bioactivity of the product mixtures. Finally, reactions that yield highly active product mixtures are scaled up to reveal, after purification, the responsible bioactive structures. Thereby, ADS can exploit adventurous and powerful synthetic methods in the discovery of bioactive molecules in parallel with associated syntheses. In the context of fragment-based ligand discovery, the approach can enable productive fragment elaboration in the absence of structural information.
Discovery of new photocatalytic reactions to explore novel chemical space: “Discovery of photocatalytic reactions enabled by high-throughput experimentation”, S. Griggs, G. Bonney, S. Liver, S. Marsden and A. Nelson, ChemRxiv DOI: 10.26434/chemrxiv-2022-pgh1w
Review of activity-directed synthesis: G. Karageorgis, S. Liver and A. Nelson,* “Activity-directed synthesis: A flexible approach for lead generation”, ChemMedChem 2020, 15, 1776-1782.
Initial disclosure of activity-directed synthesis: G. Karageorgis, S. Warriner and A. Nelson, “Efficient discovery of bioactive scaffolds by activity-directed synthesis”, Nature Chem. 2014, 6, 872-876.
Diversity-oriented approach to biologically-relevant natural product-like scaffolds: D. J. Foley, P. G. E. Craven, P. M. Collins, R. G. Doveston, A. Aimon, R. Talon, I. Churcher, F. von Delft, S. P. Marsden and A. Nelson, “Synthesis and Demonstration of the Biological Relevance of sp3-rich Scaffolds Distantly Related to Natural Product Frameworks”, Chem. Eur. J. 2017, 23, 15227-15232.