This guest post by Christian Kutruff introduces the Topliss schemes for analog synthesis, illustrates when they might be useful, and provides easy-to-use “cheat sheets” of the original schemes.

Imagine you’re on a new project that has just conducted a chemical screen on a novel target, and you’re excited to see a few micromolar hits.  You want to develop structure-activity relationships (SAR) on these early hits to show management or investors that you’ve got real starting chemical matter and justify more funding for this new project.  But right now, the chemistry team is just you.  You don’t have time to make 100 compounds before the next project review meeting, especially since this isn’t even your main project.  This is where the classic Topliss tree or Topliss batchwise operational scheme for analog synthesis could be helpful to you.1

Topliss Tree – Aromatic Substitution

The Topliss tree2 is a simple stepwise process for phenyl group optimization (Figure 1). Chances are, most of your hits start with a phenyl group, substituted phenyl, or related heterocycle. By making a few compounds sequentially following the Topliss tree, you can quickly identify a more potent tool compound for use in protein co-crystallization or a functional assay. The preliminary SAR this generates also helps prioritize your many hits and identify which compounds have no rational SAR and are likely just assay interference compounds.3

Topliss Cheat Sheet Topliss Tree Topliss Scheme
Figure 1. The Topliss Tree for aromatic substitutions. Start by making and testing the potency of the unsubstituted version of a phenyl-containing compound. Next, make and test the 4-chloro analog. If the resulting analog is more potent than the starting point, follow the green arrow and make the 3,4-dichloro analog. If the 4-chloro analog is equipotent, follow the yellow arrow, making the 4-methyl compound. And if the 4-chloro analog is less active, follow the pink arrow to make the 4-methoxy analog. Follow the tree based on the potency of each new compound relative to its previous analog. Compounds in black boxes are all suitable follow-up options from their previous analogs.

The Topliss tree works based on the fundamental assumption that there are three broad properties that a substituent may change about a compound: hydrophobicity (p), electronics (σ), and sterics (Es).4  A previous post about LogD illustrated why hydrophobicity can contribute to binding affinity, and another previous post illustrated many subtle aromatic interactions where one would expect electronic effects to make a difference.  By following the Topliss tree, you’re quickly sampling this three-dimensional property space, guided by how your target responds. 

The scheme is practical because it settles you into a local maximum for potency quickly based on these fundamental properties.  You can go chasing for the much harder to find specific molecular interactions and large entropic gains later, after you’ve gotten an X-ray co-crystal structure with your shiny new tool or confirmed activity with your set of compounds of varying potency in a functional assay.

Topliss Tree – Side Chain

There’s also a handy scheme for alkyl side chains in Topliss’s 1972 article, based on similar principles (Figure 2).  This scheme works in the same way as the previous aromatic substitution scheme. This scheme can be used to follow up on compounds containing a methyl group in the previous scheme, and the aromatic scheme can be used to follow up on compounds containing a phenyl group resulting from this side chain scheme.

Topliss Tree Aliphatic Substituents
Figure 2. The Topliss Tree for side chain optimization.
The Topliss Batchwise Method

If the parallel synthesis of new analogues is straightforward, it can be faster to use the Topliss batchwise method (Figure 3).5  Here, instead of making one compound at a time, you start by making and testing a group of five compounds (including the unsubstituted phenyl compound).  You then rank order the compounds by potency, and based on the rank ordering, choose a second compound group to synthesize.  Based on the same principles as the Topliss tree, this batchwise approach usually leads to analogs with increased potency quickly.

Topliss Batchwise Scheme Topliss Tree
Figure 3. The Topliss Batchwise Scheme. Start by making and testing analogs A-E of your phenyl-containing compound, and rank order them by potency. If the rank order is A > B > E > C > D, for example, make all of the compounds in the green box. If the rank order is D > C > E > B > A, make all of the compounds in the pink box. If the rank ordering is A > B > C > D > E, make all of the compounds in the teal box, which includes the compounds in the green and yellow boxes. The rank orderings suggest the physical parameter dependencies in grey. A hyphen indicates that the potency of a pair is similar.

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Since their publications, both the stepwise and batch schemes have been successfully used by medicinal chemists as evidenced by numerous publications.6

Finally it should be noted that the goal of using the Topliss scheme is (as Topliss himself said) to obtain a “readily accessible compound in the maximum potency area in the shortest possible time…” which as experienced drug hunters know, doesn’t necessarily bring us closer to a drug.  Often the use of these schemes result in more potent but more lipophilic compounds, which is problematic for reasons discussed previously.  In fact, most drug discovery campaigns nowadays try to replace phenyl groups and many recommended substitutions (e.g. nitro, aniline) altogether as early as possible.  But there are many instances in the exploratory phase of drug discovery, such as in the hit triage case study illustrated here, where rapid potency increases and SAR trend generation are useful, regardless of how we get them.

Hope this is helpful to you, and happy hunting!

Explore drughunter.com for more.


Christian Kuttruff Chemistry Baran Lab Boehringer Ingelheim

About the Author – Christian Kuttruff

Christian Kutruff is an Associate Director of Global Business Development & Licensing in Inflammation and NCE Technologies at Boehringer Ingleheim. Christian Kuttruff was born and raised in the southwest part of Germany, close to the Black Forest. He’s wanted to be a chemist since he was a teenager experimenting in a basement lab. Christian studied chemistry at the Technical University in Munich and obtained his PhD in 2012 under the guidance of Prof. Dirk Trauner at the University of Munich, where he focused on the synthesis of natural products. Since he couldn’t get enough of total synthesis, he moved to the lab of Phil Baran at The Scripps Research Institute in La Jolla for postdoctoral studies. With the goal of helping patients by finding new treatments for severe diseases, he joined Boehringer Ingelheim as a medicinal chemist in 2014. He worked as a project leader in the areas of respiratory diseases and immunology before assuming his current role.


  1. The Topliss schemes are named after John G. Topliss, a British medicinal chemist (b. 1930) who started his career at Schering corporation before taking positions of increasing responsibility at Warner-Lambert/Parke-Davis.  Notably, under his leadership, 7 approved drugs were discovered.
  2. Topliss, J.G. “Utilization of operational schemes for analog synthesis in drug design.” J. Med. Chem. 1972, 15, 1006-1011.
  3. Aldrich, C. et al, “The Ecstasy and Agony of Assay Interefernce Compounds.” ACS Cent. Sci. 2017, 3, 143-147.
  4. Hansch, C.; Fujita, T. “p-σ-π Analysis. A Method for the Correlation of Biological Activity and Chemical Structure.” J. Am. Chem. Soc. 1964, 86, 1616-1626.
  5. Topliss, J.G. “A manual method for applying the Hansch approach to drug design.” J. Med. Chem. 1977, 20, 463-469.
  6. (a) Patt, W. C. et al. “Structure−Activity Relationships in a Series of Orally Active γ-Hydroxy Butenolide Endothelin Antagonists.” J. Med. Chem. 1997, 40, 1063-1074. (b) Kuo, G.-H. et al. “Design, Synthesis, and Structure−Activity Relationships of Phthalimide-Phenylpiperazines:  A Novel Series of Potent and Selective α1a-Adrenergic Receptor Antagonists.” J. Med. Chem. 2000, 43, 2183.