Seattle, WA, USA
June 23-26, 2024
Join Chris Khoury at the 38th ACS National Medicinal Chemistry Symposium in Seattle.
Chris will be presenting two posters at the event:
Poster title: ‘From quantum mechanics to metabolic pathways‘
Unexpected metabolism can lead to the failure of many late-stage drug candidates or even the withdrawal of approved drugs. Therefore, during early research, it is important to predict the sites of metabolism and metabolites of potential drug-like molecules, made possible through platforms such as StarDrop and Semeta.
Historically, predictive models have targeted metabolism by human Cytochrome P450 (CYP) isoforms due to their well-documented importance in phase I metabolism. Here, we will present methods to predict isoform-specific metabolism for a broad range of enzymes involved in phase I and phase II metabolism, including aldehyde oxidases (AOs), flavin-containing monooxygenases (FMOs), sulfotransferases (SULTs) and UDP-glucuronosyltransferases (UGTs) alongside CYPs. These models are based on a consistent framework, combining mechanistic quantum-mechanical simulations with machine learning, and are rigorously validated with experimental data. The resulting models predict if a potential site of metabolism on a compound is likely to be metabolised by the specified enzyme.
We will demonstrate how these site-of-metabolism models can be combined with models that predict which enzymes and isoforms are likely to metabolise a compound. Applying models iteratively to a parent compound and its metabolites enables the prediction of metabolic pathways and the resulting metabolites observed in vivo. We validate these pathway predictions by comparison with experimentally observed metabolite profiles.
Poster title: ‘using AI to derive valuable insights from drug discovery data‘
It’s impossible to experimentally measure all of the data we want for all compounds in a drug discovery project. Furthermore, the limited data we have are noisy because of experimental variability and error. However, AI can make sense of these ‘sparse and noisy’ data to offer valuable insights and guide research, saving time and money.
We will describe an AI platform, Cerella™, that applies deep learning imputation to learn from both structure-activity relationships (SAR) and directly from the relationship between experimental endpoints based on sparse data [1]. The resulting models can proactively highlight high-quality compounds by ‘filling in’ missing data more accurately than conventional quantitative structure-activity relationship (QSAR) models. Furthermore, it can identify hidden opportunities caused by missing, uncertain or inaccurate data, and prioritise experimental resources by focussing on measuring the most valuable data to inform decisions about compound progression.
We will describe practical applications of deep learning imputation and compare the results with those from conventional predictive modelling methods. We will demonstrate the application in the context of a drug discovery project, in which deep learning imputation achieved an average R2 of 0.72 vs 0.50 for the best QSAR method across 18 heterogeneous endpoints, including compound activities and ADME properties [2]. We will also present an application in combination with generative chemistry methods to identify a novel, active antimalarial compound that revealed new SAR, previously unknown to the project team [3]. Finally, we will show an application to the prediction of particularly challenging sensory properties, assessed in panels of human subjects and compare the results with other methods, including multi-target deep neural networks [4].
[1] Irwin et al. App. AI Lett. (2021) DOI: 10.1002/ail2.31
[2] Irwin et al. J. Chem. Inf Model. (2020) 60(6), pp. 2848–2857
[3] Tse et al. J. Med. Chem. (2021) 64(22) pp 1645-16463
[4] Mahmoud et al. J. Comput. Aided Mol. Des. (2021) 35(11) pp. 1125-1140
Meet the team
Chris Khoury
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