Linkers in fragment-based drug design: an overview of the literature

ABSTRACT

Introduction

In fragment-based drug design, fragment linking is a popular strategy where two fragments binding to different sub-pockets of a target are linked together. This attractive method remains challenging especially due to the design of ideal linkers.

Areas covered

The authors review the types of linkers and chemical reactions commonly used to the synthesis of linkers, including those utilized in protein-templated fragment self-assembly, where fragments are directly linked in the presence of the protein. Finally, they detail computational workflows and software including generative models that have been developed for fragment linking.

Expert opinion

The authors believe that fragment linking offers key advantages for compound design, particularly for the design of bivalent inhibitors linking two distinct pockets of the same or different subunits. On the other hand, more studies are needed to increase the potential of protein-templated approaches in FBDD. Important computational tools such as structure-based de novo software are emerging to select suitable linkers. Fragment linking will undoubtedly benefit from developments in computational approaches and machine learning models.

KEYWORDS:

• click chemistry
• de novo design software
• drug design
• fragment
• linker
• linking
• medicinal chemistry
• self-assembling

Article highlights

• Most represented linkers are flexible and less than 5-atom long, usually made of aliphatic chains or amid functions.

• Target-guided-synthesis studies involving dynamic combinatorial chemistry and kinetic target-guided synthesis are based on the knowledge of known protein inhibitors or ligands. Triazoles are among the most common linkers.

• Various computational workflows have been suggested based on molecular docking to assist fragment linking strategies.

• Structure-based de novo design programs like LigBuilder can be utilized to generate linked compounds from pre-docked fragments.

• An increasing number of machine learning methods have been proposed to perform fragment linking that now include 3D constraints.

Declaration of interest

The authors have no other relevant affiliations or financial involvement with any organization or entity with a financial interest in or financial conflict with the subject matter or materials discussed in the manuscript apart from those disclosed.

Reviewer disclosures

Peer reviewers on this manuscript have no relevant financial or other relationships to disclose.

Additional information

Funding

The authors are funded by the Agence Nationale de la Recherche via grants Convergence PLAsCAN (ANR-17-CONV-002), ANR CK2COV (ANR-CE18-0014-01), ANR-NanoWAC (ANR-21-CE29-0012), ANR-PALUMET (ANR-20-CE44-0012) to D Grenier, S Audebert and J Preto.