Late-stage Functionalization and its Impact on Modern Drug Discovery

Nippa, David F.; Hohler, Remo; Stepan, Antonia F.; Grether, Uwe; Konrad, David; Martin, Rainer E.

doi:10.2533/chimia.2022.258

Cited by 13 publications

(14 citation statements)

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“…In combination with new chemical methodologies, LSF approaches offer significant potential for modern drug discovery in supporting diversity-oriented synthesis, allowing for the installation of transient handles such as boron or phosphorus containing groups [160] and the decoration of 3-dimensional building blocks with intrinsically high drug-likeness. [161] Combining LSF with recent advances in high-throughput experimentation (HTE), lab automation methods, design of experiment (DOE) software, machine learning and artificial intelligence might enable the generation of tools for predicting individual CÀ H bond manipulations in a prospective manner, allowing the efficient synthesis of structurally novel target molecule. [162] Figure 7.…”

Section: New Trends In Drug Discoverymentioning

confidence: 99%

EFMC: Trends in Medicinal Chemistry and Chemical Biology

et al. 2023

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Ground-breaking research in disease biology and continuous efforts in method development have uncovered a range of potential new drug targets. Increasingly, the drug discovery process is informed by technologies involving chemical probes as tools. Applications for chemical probes comprise target identification and assessment, as well as the qualification of small molecules as chemical starting points and drug candidates. Progress in probe chemistry has opened the way to novel assay formats and pharmaceutical compound classes. The European Federation of Medicinal Chemistry and Chemical Biology (EFMC) has launched the Chemical Biology Initiative to advance science in the field of medicinal chemistry and chemical biology, while representing all members of this extended scientific community. This review provides an overview of the many important developments in the field of chemical biology that have happened at the lively interface of academic and industrial research.

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Section: New Trends In Drug Discoverymentioning

confidence: 99%

EFMC: Trends in Medicinal Chemistry and Chemical Biology

et al. 2023

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“…Computational workflow to train SoBo to predict the site of borylation. Starting from a molecular representation (1), three-dimensional structures are generated and activation barriers for the oxidative addition of each substrate C-H bond to the iridium catalyst are calculated (2). Next, a partial least squares regressor (3a) and sterimol-based steric approximator (3b) are trained to predict site selectivity.…”

Section: Hybrid Computational Workflowmentioning

confidence: 99%

“…[1] While reactions are being developed that occur with remarkable chemoselectivity for C-H bonds over classic functional groups, site selectivity is challenging to achieve and difficult to predict because of the ubiquity of C-H bonds and the effects of competing chemical phenomena on relative rates (Fig. 1A) [2]. While heuristic guidelines can help predict site selectivity, they are frequently limited to cases in which single factors dictate the reaction outcome.…”

Section: Introductionmentioning

confidence: 99%

A Hybrid Machine-Learning Approach to Predict the Iridium-Catalyzed Borylation of C–H Bonds

Caldeweyher

Elkin

Gheibi

et al. 2022

Preprint

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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 aryl C–H bonds that are most acidic and least sterically hindered, 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, semi-empirical 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(sp2)–H bonds during pharmaceutical development.

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“…Drug discovery is an expensive and risky enterprise where large investments and low drug approval rates heavily incentivise innovation in chemical synthesis that can streamline discovery and manufacture. , LSF is now a key strategy used to advance medicinal chemistry programs, enabling substantial opportunities for efficiently accessing a diverse range of analogues of existing biologically active molecules. , The fundamental benefit of this approach is the avoidance of time- and resource-intensive de novo synthesis of individual analogues. The primary applications of LSF in drug discovery programs have been comprehensively reviewed elsewhere. ,− ,− Of these, the most common application is to generate diverse libraries from existing drug molecules or natural products that explore novel chemical space to probe SAR − (e.g., sclareolide ( 1 ), Figure A). In addition, LSF methodologies have also been used in the preparation of radiolabeled chemical probes (Figure B), which play a vital role in studying metabolic pathways, establishing putative mechanisms of action and quantifying target engagement in vivo . − Finally, LSF has been used in the identification and preparation of drug metabolites (Figure C)a crucial aspect of drug discovery to develop an improved understanding of these pathways en route to safety and efficacy studies. ,, …”

Section: Introductionmentioning

confidence: 99%

Late-stage Functionalization for Improving Drug-like Molecular Properties

Castellino,

Montgomery,

Danon

et al. 2023

Chem. Rev.

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The development of late-stage functionalization (LSF) methodologies, particularly C–H functionalization, has revolutionized the field of organic synthesis. Over the past decade, medicinal chemists have begun to implement LSF strategies into their drug discovery programs, allowing for the drug discovery process to become more efficient. Most reported applications of late-stage C–H functionalization of drugs and drug-like molecules have been to rapidly diversify screening libraries to explore structure–activity relationships. However, there has been a growing trend toward the use of LSF methodologies as an efficient tool for improving drug-like molecular properties of promising drug candidates. In this review, we have comprehensively reviewed recent progress in this emerging area. Particular emphasis is placed on case studies where multiple LSF techniques were implemented to generate a library of novel analogues with improved drug-like properties. We have critically analyzed the current scope of LSF strategies to improve drug-like properties and commented on how we believe LSF can transform drug discovery in the future. Overall, we aim to provide a comprehensive survey of LSF techniques as tools for efficiently improving drug-like molecular properties, anticipating its continued uptake in drug discovery programs.

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Late-stage Functionalization and its Impact on Modern Drug Discovery

Cited by 13 publications

References 0 publications

EFMC: Trends in Medicinal Chemistry and Chemical Biology

EFMC: Trends in Medicinal Chemistry and Chemical Biology

A Hybrid Machine-Learning Approach to Predict the Iridium-Catalyzed Borylation of C–H Bonds

Late-stage Functionalization for Improving Drug-like Molecular Properties

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