Enabling late-stage drug diversification by high-throughput experimentation with geometric deep learning

Nippa, David F.; Atz, Kenneth; Hohler, Remo; Müller, Alex T.; Marx, Andreas; Bartelmus, Christian; Wuitschik, Georg; Marzuoli, Irene; Jost, Vera; Wolfard, Jens; Binder, Martin; Stepan, Antonia F.; Konrad, David; Grether, Uwe; Martin, Rainer E.; Schneider, Gisbert

doi:10.26434/chemrxiv-2022-gkxm6-v2

Cited by 3 publications

(2 citation statements)

References 22 publications

(30 reference statements)

Supporting

Mentioning

Contrasting

Order By: Relevance

“…However, it is unclear how these competing relative rates influence site selectivity in more complex cases, including cases in which the substrate contains multiple aromatic rings, because the relative rates of borylation of multiply substituted arenes and heteroarenes are not well established, and the interplay between competing steric and electronic factors are difficult to assess. Thus, a more refined approach that builds on our current understanding of the factors influencing the site or sites of the borylation of C–H bonds is needed …”

Section: Introductionmentioning

confidence: 99%

Hybrid Machine Learning Approach to Predict the Site Selectivity of Iridium-Catalyzed Arene Borylation

Caldeweyher,

Elkin,

Gheibi

et al. 2023

J. Am. Chem. Soc.

View full text Add to dashboard Cite

The borylation of aryl and heteroaryl C−H bonds is valuable for the site-selective functionalization of C−H bonds in complex molecules. Iridium catalysts ligated by bipyridine ligands catalyze the borylation of the C−H bond that is most acidic and least sterically hindered in an arene, but predicting the site of borylation in molecules containing multiple arenes is difficult. To address this challenge, we report a hybrid computational model that predicts the Site of Borylation (SoBo) in complex molecules. The SoBo model combines density functional theory, semiempirical quantum mechanics, cheminformatics, linear regression, and machine learning to predict site selectivity and to extrapolate these predictions to new chemical space. Experimental validation of SoBo showed that the model predicts the major site of borylation of pharmaceutical intermediates with higher accuracy than prior machine-learning models or human experts, demonstrating that SoBo will be useful to guide experiments for the borylation of specific C(sp 2 )−H bonds during pharmaceutical development.

show abstract

Section: Introductionmentioning

confidence: 99%

Hybrid Machine Learning Approach to Predict the Site Selectivity of Iridium-Catalyzed Arene Borylation

Caldeweyher,

Elkin,

Gheibi

et al. 2023

J. Am. Chem. Soc.

View full text Add to dashboard Cite

show abstract

“…This work fuses several developments in chemistry and drug discovery, including the recent attention on data-driven approaches for the prediction of reaction outcome (22)(23)(24)(25)(26). High-throughput synthesis, automated purification and analysis, deep learning predictions of product properties and reaction outcome are combined into the SLAP platform to produce a vast, accessible VL built from purchasable building blocks.…”

Section: Introductionmentioning

confidence: 99%

High-throughput synthesis provides data for predicting molecular properties and reaction success

Götz,

Jackl,

Jindakun

et al. 2023

Sci. Adv.

View full text Add to dashboard Cite

The generation of attractive scaffolds for drug discovery efforts requires the expeditious synthesis of diverse analogues from readily available building blocks. This endeavor necessitates a trade-off between diversity and ease of access and is further complicated by uncertainty about the synthesizability and pharmacokinetic properties of the resulting compounds. Here, we document a platform that leverages photocatalytic N-heterocycle synthesis, high-throughput experimentation, automated purification, and physicochemical assays on 1152 discrete reactions. Together, the data generated allow rational predictions of the synthesizability of stereochemically diverse C-substituted N-saturated heterocycles with deep learning and reveal unexpected trends on the relationship between structure and properties. This study exemplifies how organic chemists can exploit state-of-the-art technologies to markedly increase throughput and confidence in the preparation of drug-like molecules.

show abstract

Identifying opportunities for late-stage C-H alkylation with high-throughput experimentation and in silico reaction screening

Nippa,

Atz,

Müller

et al. 2023

Commun Chem

View full text Add to dashboard Cite

Enhancing the properties of advanced drug candidates is aided by the direct incorporation of specific chemical groups, avoiding the need to construct the entire compound from the ground up. Nevertheless, their chemical intricacy often poses challenges in predicting reactivity for C-H activation reactions and planning their synthesis. We adopted a reaction screening approach that combines high-throughput experimentation (HTE) at a nanomolar scale with computational graph neural networks (GNNs). This approach aims to identify suitable substrates for late-stage C-H alkylation using Minisci-type chemistry. GNNs were trained using experimentally generated reactions derived from in-house HTE and literature data. These trained models were then used to predict, in a forward-looking manner, the coupling of 3180 advanced heterocyclic building blocks with a diverse set of sp3-rich carboxylic acids. This predictive approach aimed to explore the substrate landscape for Minisci-type alkylations. Promising candidates were chosen, their production was scaled up, and they were subsequently isolated and characterized. This process led to the creation of 30 novel, functionally modified molecules that hold potential for further refinement. These results positively advocate the application of HTE-based machine learning to virtual reaction screening.

show abstract

Enabling late-stage drug diversification by high-throughput experimentation with geometric deep learning

Cited by 3 publications

References 22 publications

Hybrid Machine Learning Approach to Predict the Site Selectivity of Iridium-Catalyzed Arene Borylation

Hybrid Machine Learning Approach to Predict the Site Selectivity of Iridium-Catalyzed Arene Borylation

High-throughput synthesis provides data for predicting molecular properties and reaction success

Identifying opportunities for late-stage C-H alkylation with high-throughput experimentation and in silico reaction screening

Contact Info

Product

Resources

About