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3D-shaped Fragment Library

The molecular shape is one of the essential factors in molecular recognition by a biomolecule and its affinity to the binding site [1]. Nonetheless, the vast majority of existing drugs have been centered on the sp2-rich aromatic core [2,3]. Moreover, often the same core moiety can be found in several drugs with different targeted diseases, which leads to a low specificity (selectivity) and the rise of side effects [4]. Although most fragment libraries provide a high level of diversity, having been refined to contain the right balance of properties, they all tend to have a limited shape diversity [5,6].

In this context, it has been established that a higher three-dimensionality (3D) of molecules is a desirable feature of drug candidates and is correlated with the successful passage of molecules at various stages of clinical development [7]. The use of more complex, more 3D-like sp3-rich fragments would undoubtedly build up the drug-like fragment chemical space that might, in turn, be advantageous in exploring more demanding biological targets.

In view of the above, we have carefully designed a proprietary 3D-shaped Fragment Library of 4,700 non-flat fragment-like molecules for efficient fragment-based drug discovery (FBDD). The selection was focused on physicochemical properties and descriptors that allow evaluating 3D-dimensionality and structural diversity of the fragment-like screening compounds. This screening set covers various molecule shapes: rod-like, disk-like, and spherical with sufficient diversity shown in triangle 2D normalized PMI plot (Fig.1).

The compound selection can be customized based on your requirements, cherry picking is available.

Please, contact us at for any additional information and price quotations.

Compound selection

First, the Rule of Three with several filtering criteria was applied to the Life Chemicals General Fragment Collection. Principal moments of inertia (PMI) [1,7] calculation was used as an efficient method to calculate and evaluate 3D-dimensionality. Then, appropriate diversity levels of the Library were proved by applying the max Tanimoto coefficient of diversity of 85 % (linear fingerprints were used). Finally, undesirable functionalities were eliminated by applying PAINS and our exclusive in-house medicinal chemistry filters. In total, around 4,700 readily available fragments, representing a variety of 3D shaped molecules, were selected for the Screening Library.

The following basic criteria were used to improve the functionality of 3D scaffolds in our Library:





100 - 300


> 0.47


< 100 Å2

Rotatable bonds

≤ 3.0


≤ 3.0


≤ 4.0

Chiral centers

≥ 1

Functionalization points


-CN, -NO2, Br count

≤ 1

S, Cl count

≤ 2

Ring count

1 - 4


The 2D-normalized PMI plot indicates high compound 3D diversity in Life Chemicals 3D Fragments Library.

Figure 1. The 2D-normalized PMI plot indicates high compound 3D diversity in Life Chemicals 3D Fragments Library.

Distribution of Life Chemicals 3D Fragments by Fsp3 values.

Figure 2. Distribution of Life Chemicals 3D Fragments by Fsp3 values.

Representative compounds from the 3D Fragment Library


  1. Kumar A, Zhang KYJ. Advances in the Development of Shape Similarity Methods and Their Application in Drug Discovery. Front Chem. 2018;6:315. 
  2. Morley AD, Pugliese A, Birchall K, et al. Fragment-based hit identification: thinking in 3D. Drug Discov Today. 2013;18(23-24):1221-1227.
  3. Fa S, Yamamoto M, Nishihara H, Sakamoto R, Kamiya K, Nishina Y, Ogoshi T. Carbon-rich materials with three-dimensional ordering at the angstrom level. Chem. Sci., 2020,11, 5866-5873
  4. Brooks WH, Guida WC, Daniel KG. The significance of chirality in drug design and development. Curr Top Med Chem. 2011;11(7):760-770.
  5. Karawajczyk A, Orrling KM, de Vlieger JS, Rijnders T, Tzalis D. The European Lead Factory: A Blueprint for Public-Private Partnerships in Early Drug Discovery. Front Med (Lausanne). 2017;3:75. 
  6. Dahlin JL, Walters MA. The essential roles of chemistry in high-throughput screening triage. Future Med Chem. 2014;6(11):1265-1290.
  7. Gregory Sliwoski, Sandeepkumar Kothiwale, Jens Meiler, Edward W. Lowe, Jr. Computational Methods in Drug Discovery. Pharmacol Rev. 2014 Jan; 66(1): 334–395.
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