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FULLY INTEGRATED RESEARCH AND MANUFACTURING PLATFORM FOR LIFE SCIENCES AND INDUSTRIES HTS Compounds Targeted and Focused Libraries Building Blocks Fragment Libraries Custom Synthesis Computational Chemistry Early Drug Discovery Primary Drug Trials All New Chemistry — Yours to Explore FRAGMENT LIBRARIES

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HTS Compounds
Drug Safety • General toxicity • Safety pharmacology (ICH S7A and S7B) Specific Pharmacological Activity
• Antihypertensive • Anti-inflammatory • Anti-ischaemic • Antiarrhythmic
Fully Integrated Solutions for Drug Discovery
• Collection of HTS Compounds • Targeted Libraries • Fragment Libraries • Custom synthesis • Computational chemistry • Early drug discovery
Advanced building blocks
Fine Chemicals in Multigram Scale
• IR-Dyes • APIs • Fine reagents for organic synthsis • Scale-up synthesis (up to 100 kg) • Process optimization
• General Fragment Library (38,000 compounds in stock and 30,000 tangible ones);
• Ultimate Fragment Library (4,200 compounds);
• Low MW Fragment Library (3,000 compounds);
• Fragment Library With Solubility Data (12,800 compounds);
• Bromine Fragment Library (1,300 compounds);
• Fluorine Fragment Library (3,300 compounds);
• Fluorine Fragment Cocktails (1,010 compounds in 101 cocktails);
• Fsp³-enriched Fragment Library (11,500 compounds);
• PPI Fragment Library (2,500 compounds);
• 3D Fragment Library (1,500 compounds);
• Covalent Fragment Library (1,300 compounds);
• Natural Product-like Fragment Library (3,200 compounds).
The fragment-based approach to drug discovery (FBDD) has been established as an efficient tool in the search for new drugs [1,2]. It has emerged as an alternative approach to traditional lead identification via high-throughput screening (HTS). Unlike HTS, FBDD identifies smaller compounds, the “fragments”, which bind to different parts of a biological target. FBDD has several advantages over HTS. The primary rationale for screening fragments is that they give access to a broader chemical space while screening a limited number of compounds. FBDD gives a better chance for the final lead compound to have common drug-likeness parameters. Moreover, the chance of identifying a compound that is a perfect match for the intended target’s binding site is too small to rely on because a multitude of interactions between the ligand and amino acid residues in the active site are often involved. On the other hand, there are simpler molecules (fragments) with fewer unavoidable interactions and, thus, a much better chance of orienting within the binding site in a favorable manner.
In response to this evident trend in drug discovery Life Chemicals has designed its proprietary Fragment Library containing nearly 68,000 compounds.
Following the most promising current tendencies in fragment based approach to drug discovery, Life Chemicals has prepared a selection of its unique subsets given below:
In addition to these subsets the brochure presents our exclusive library of 5,200 tangible fragments – a product of unique design specifically developed for fragment-based drug discovery. The Library is a collection of first-rate quality fragments having a high fraction of sp³
carbons, notable diversity, exceptional novelty and rapid SAR generation potential.
The structure files of our Fragment Libraries can be downloaded at
The Life Chemicals General Fragment Library comprises more than 68,000 compounds with MW ≤ 300 and ClogP ≤ 3.0, including 38,000 stock available compounds and 30,000 tangible structures.
More scrupulous assay applying expanded "Rule of Three" parameters and additional
physico-chemical and structural filters enabled us to work out our Advanced Set of nearly 9,200 stock-available fragments. The criteria used to design The Life Chemicals Advanced Fragment Set are listed in the table below. Additionally, the compounds were passed through Substructure[1] and PAINS[2] structural filters.
S, Cl count
The process of fragment synthesis obeys a heuristic rule called the “Rule of Three”. State-of-the-art tuning of “Rule of Three” parameters helps to generate leads with improved ADME profile.
Physicochemical criteria used to create The Ultimate Collection are as follows:
≤ 2
≤ 1
The Life Chemicals Ultimate Fragment Library was designed by applying ultimately refined picking approach to our stock screening compound collection: the “Rule of Three” and TPSA ≤ 80 Å2 cut-off. It is known that more than 80 % of drugs on the market have an estimated logSw value greater than -4. Thus, the solubility filtering was also used to design this Fragment Subset.
Finally, PAINS and in-house developed filtering parameters, such as toxicophore and undesired functionalities, were applied to make up the set of compounds shown here. The total number of compounds in The Life Chemicals Ultimate Fragment Collection amounts to 4,200 items.
Recent tendencies in drug discovery, however, shift towards aiming at leads with lower molecular weight and higher hydrophilicity; it is not surprising, therefore, that even more meticulous criteria must be applicable to fragment libraries. In particular, lowering upper molecular weight cut-off value (to 200–240 instead of 300) is strived for by many companies practicing FBDD.
To meet these principles, Life Chemicals has designed its Low MW Fragment Library applying a number of physico-chemical filters [1, 2] to its General Fragment Collection. Finally, the compounds were passed through MedChem [3] and PAINS [4] structural filters. This resulted in a library of more than 3,000 compounds. Although the molecular weight is the main parameter which was controlled strictly, other physico-chemical characteristics were also well monitored (e. g., more than 80 % of the library has ClogP<2). Due to this upgrading a refined set of nearly 1,500 fragments was generated by means of more rigorous physico-chemical and structural cut-offs.
Advanced Low MW Fragment Set
Average Values
Solubility of fragments is known to be one of the key features to limit their use in various screening techniques of FBDD. Proceeding from this knowledge, Life Chemicals has started its project on measuring experimental solubility of proprietary fragment collec- tion. The procedure includes visual determination of solubility by observing scattering of solutions under the following concentrations:
• 200 mM in DMSO • 5 m in water phosphate buffer with 2.5 % DMSO • 1 m in water phosphate buffer with 0.5 % DMSO
The Life Chemicals Fragment Library with Experimental Solubility Data includes over 12,800 compounds, mainly from our Advanced Set of The General Fragment Library, with confirmed experimental solubility at 200 mM in DMSO and characterized by the data available for the above mentioned concentrations in phosphate buffer.
• Average MW 240 Da and logP 1.2 • Guaranteed solubility of all compounds in DMSO at high concentration (200 mM) • 77 % of the Library are soluble in water phosphate buffer at 1 mM, and 60 % – at 5 mM • Compounds were passed through MedChem structural filters worked out by our experts
According to new trends in drug discovery involving comprehensive structural analysis of approved drugs and determination of relationships between molecular complexity and pharmacological promiscuity of drug-like compounds [1–5], Life Chemicals has extrapolated the results of investigations described here to its Fragment Library design.
Recently it was found that the mean Fsp³ goes up from 0.36 for 2.2 million molecules in drug discovery phase to 0.47 for 1,179 approved drugs. Applying the mentioned above Fsp³ cut-off to The Life Chemicals General Fragment Library resulted in our Fsp³-enriched Fragment Library of more than 11,500 compounds.
Superposition of sets of the compounds with a relatively high Fsp³ value and filtering them by strict “Rule of Three” criteria along with TPSA 90Å2 cut-off gives rise to our Advanced Fsp³-enriched Fragment Subset comprising nearly 3,200 compounds. To ensure discarding “ugly” compounds, PAINS and our in-house developed toxicophore and undesired functionalities filtering were applied as well.
Physicochemical parameters of The Advanced Fsp³-enriched Fragment Library are summarized in the table below:
Crystallographic fragment-based drug discovery is gaining a high importance in modern pharmaceutical technology. Fragments, however, are poor binders, with their partial occupancy resulting in weak, difficult-to-fit electron density. The use of brominated fragments can address this challenge, as bromine can be located uniquely via anomalous scattering, and thus, provide further insight into the nature of the target surface pocket [1].
The Life Chemicals Bromine Fragment Library consists of about 1,300 compounds with MW ≤ 350 and ClogP ≤ 3.5. All the fragments contain only one Br-atom. Other important parameters (such as HBA, HBD, Fsp3, RotB, TPSA) are included into the data file and can be used for selection with multiple restrictions. While this library is available for regular cherry picking, a specially tailored diversity set can be arranged in accordance with the customer’s needs.
A subset of more than 800 compounds composed on the basis of the values given below is also available where there are no reactive compounds and undesired chemotypes, with PAINS filters being applied.
It was demonstrated that ¹F NMR-based fragment screening can be used as a very efficient tool for rapid and sensitive detection of fragment hits [1]. As shown, although the fraction of chemical space covered by fluorine containing fragments may be limited compared to that covered by non-fluorinated fragments, similar hit rates are obtained applying both methods. At the same time fragment screening with a simple one- dimensional ¹F NMR experiment (with ¹H decoupling) is significantly faster and, in many ways, more robust than traditional ¹H-based NMR screening. An in-house developed method focuses on the use of a fluorine nucleus as a detection tool, and subsequent to a ¹F fragment screen, protonated analogues of hit compounds can be mined and pursued in initial hit expansion. This approach can significantly add to the chemical space explored and provide valuable information and early stage SAR to chemists.
The Life Chemicals Fluorine Fragment Library extracted from The General Fragments Stock Collection comprises about 3,300 fluorine-containing compounds, including an Advanced Subset of nearly 2,000 compounds selected by the following criteria:
No metal or reactive compounds, no undesired chemotypes, no salts, PAINS filters applied
“Fragment space” coverage of Life Chemicals fluorinated fragments (in black) is similar to the coverage of all com- mercially available fluorinated fragments (in green).
The efficacy of ¹F NMR fragment screening can be increased substantially by pooling or cocktailing the compounds in the library. Identification of the bound fragment at the end of the NMR experiments, then, becomes the case of determining the best fragment-fit.
Life Chemicals has designed a unique Library of Fluorine Fragment Cocktails, employing “Rule of Three” criteria and MedChem structure filters, including PAINS. The choice of fluorinated motifs present in the library is fundamental in order to ensure a larger coverage of chemical space and local environment of fluorine. The Library of 1,010 fluorinated fragments is pooled in 101 cocktails of 10 compounds, each with most different ¹F chemical shifts to facilitate interpretation of screening results.
The fragments chosen for cocktails preparation satisfy the following parameters:
• Mostly only 1 peak in ¹F NMR spectrum; • Purity 95 %+; • DMSO soluble at 200 mM.
Structure Filters Applied: • No reactive functional groups; • Lilly MedChem rules; • PAINS; • Ring count 1 – 3.
PhysChem Parameters: • 130 < MW < 300; • -1.7 < ClogP < 3.0; • H-bond Donors ≤ 3; • H-bond Acceptors ≤ 4; • TPSA ≤ 110 Å2.
¹F NMR spectrum of fragment cocktail # 101 from The Life Chemicals Library of Fluorine Fragment Cocktails.
Protein-protein interactions (PPIs) are among the most attractive targets for drug design. However, this field of interactions is not well explored and there are no common features for all proteins involved in it. Several dozens of protein pairs have been fully investigat- ed so far and their crystal structures have revealed the mechanism of tight connection between interfaces. These investigations present a hard task consuming too much time and effort but they can be facilitated by development of fragment-based library focused on inhibition of protein-protein interactions.
Parameter Range Average Values
A set of descriptors and ranking mechanism of arraying fragments was formulated on the basis of our thorough analysis of published data that enabled us to construct a new Life Chemicals PPI Targeted Fragment Library.
It was shown that PPI inhibitors are typically larger and more lipophilic than inhibitors of more standard binding sites of most proteins [1]. That’s why PPI fragments have TPSA values higher than it was generally claimed when practicing a common approach to fragment-based drug design. The same applies to molecular weight of compounds that should be between 250 and 450 Da. Thus, such compounds cannot fit a standard “Rule of Three” usually used for selection of fragments. It was found that small molecules targeting PPI have highly hydrophobic core within peripheral lipophilic substituents [2]. To enhance selectivity we have proposed very reasonably chosen hydrophobic and more spatial structures (sp³-enriched).
The main result of compilation of the features mentioned above is a special chemical space for compounds with a common function of PPI interaction inhibition. One more detail that characterizes PPI fragments is the principal moment of inertia (PMI). Mean values of npr1 = 0.28, npr2 = 0.88 show the distribution of compounds in 3D space and evaluate 3D-shape diversity [3]. Finally, the compounds were passed through PAINS filter.
More than 2,500 compounds were extracted into The Life Chemicals PPI Fragment Library.
The vast majority of existing drugs has centered on sp2-rich aromatic core [1,2]. Moreover, we could often find the same core fragment in several drugs with different targeted diseases that leads to a low specificity (selectivity) and the rise of side effects. Although most fragment libraries are diverse, having been refined to contain a good balance of properties, they all tend to have a limited shape diversity [5]. 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 successful passage of molecules at various stages of clinical development. The use of more complex and sp3-rich (more 3D-like) 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 our drug-like 3D Fragment Library using physico-chemical properties and descriptors that allow to evaluate 3D-dimensionality and diversity of the molecules. The “Rule of Three” with a number of filtering criteria enabling successful 3D library design was applied to The Life Chemicals Stock Fragment Collection. Principal moments of inertia (PMI) [3,6] calculation was used as an efficient method to calculate and evaluate 3D-dimensionality. Proper diversity levels of the library were proved by applying max Tanimoto coefficient of diversity 85 % (linear fingerprints were used). Undesirable functionalities were eliminated by applying PAINS and our special in-house medicinal chemistry filters. Totally 1,500 fragments were chosen.
The following basic criteria were used to improve 3D shape functionality of fragments in our library:
In recent years, the number of drug candidates with a covalent mechanism of action progressing through clinical trials or being approved by the FDA has grown significantly; around 30 % of the marketed drugs are covalent inhibitors. This kind of inhibition has many desirable features, including higher biochemical efficiency of target disruption, less sensitivity toward pharmacokinetic parameters and increased duration of action that outlasts the pharmacokinetics of the compound [1].
Taking into account a growing interest and widespread use of fragment-fased drug discovery, Life Chemicals designed the Library of 1,300 Covalent Fragments based on The Stock Compound Collection. The design of selective covalent inhibitors is conceptually very attractive but in practice hard to achieve. That is because it is
difficult to strike the right balance between reactivity and selectivity [2-4]. Initially, The Life Chemicals Stock Collection was filtered according to “Rule of Three” criteria. Thereafter, a preliminary set of covalent fragments was created by singling out compounds with specific structural fragments (functional groups) [4-8] that are known to form covalent bonds with amino acid residues in binding sites of targeted proteins, e.g. Lys, Cys, Ser, His and Tyr. In addition, moleculeswith highly reactive electrophilic and nucleophilic groups (non-selective binders) as well as compounds with non-drug-like cores were discarded, using unwanted fragments and appropriate PAINS filters.
Chemical classes and structural fragments shown below were used for arraying potential covalent inhibitors:
• Acetals; • Acrylamides; • Acrylonitriles; • a-Dicarbonyl compounds; • Aliphatic thiols; • Aromatic thiols; • Epoxides; • Ketals; • Maleimides and related compounds;
• Other Michael acceptors; • Quinones; • Sulfonate esters; • Sulfonyl halides; • Terminal acetylenes; • Thioureas and thiones; • Vinyl sulfones; • Vinyl sulfonamides; • Other types.
A remarkable structural diversity and drug-likeness of molecular scaffolds identified in natural compounds provides a basis for the design of novel natural product-derived compound libraries for drug discovery [1,2]. Implementation of natural product-like frameworks into fragment-based drug discovery might benefit from using biologically validated areas of chemical space not occupied by average synthetic molecules [3,4].
Life Chemicals presents a collection of Natural Product-like Fragments generated via Scaffold Tree approach [5]. More than 17,000 level-two and level-three scaffolds were extracted from The Universal Natural Product Database (comprising more than 220,000 structures in its current version) [6] using scaffold tree analysis. After excluding undesirable and primitive chemotypes, the remaining ca. 4,000 structures were used for substructure search within The Life Chemicals Fragment Collection. In addition to that, fragments derived from scaffolds showing at least 85 % similarity with the natural product-derived scaffold set mentioned above were included as well. The resulting database was subject to additional structural filtering to give more than 3,200 natural product-like fragments.
Although nowadays many companies have their own FBDD programs, still quite a few vendors offer their fragment libraries on the market. However, most of commercial fragment libraries are obtained by filtering or extension of already available collections that leads to uneven coverage of the chemical space by these compounds.
In response to the present market challenge Life Chemicals introduces its Library of 5,200 synthetically feasible compounds designed specifically for fragment-based drug discovery. The Library is generated by virtual coupling of carefully picked out sets of building blocks and proprietary reagents, with this approach enabling to obtain the resulting library with the features shown below.
The cornerstone of the design is an excellent quality of the fragments ensured by hard- line control of physico-chemical properties: Ro3 extension, PAINS and as well as by a number of in-house developed structural filters applied:
• High fraction of sp³-carbons in the compounds: average Fsp³ is kept above 0.6, whereas most commercial libraries have average Fsp³ 0.3–0.4;
• Diversity of the library is controlled by a number of methods: linear fingerprint, pharmacophore and 3D analysis;
• Obvious novelty of chemical structures is provided by dissimilarity analysis versus databases of eMolecules and those of most well-known vendors;
• Rapid SAR generation potential: prompt molecule identification and synthesis for follow-up testing analogues.
Based on the design approaches and selection criteria described in this brochure, synthesis of our original and innovative fragment library is currently in progress. Over 1,000 compounds have already been synthesized in 100 mg+ scale, with our laboratory capacity allowing us to rapidly increase this number to meet present-day professional demand.
3rd Step: Novelty Verification 98 % Tanimoto diversity to eMolecules database
98 % diversity to major competitors’ fragment databases 90 % diversity to The Life Chemicals Stock Collection
2nd Step: Physico-chemical Parameters
1st Step: Virtual Coupling 1,139 building blocks - carefully selected novel Fsp3-enriched molecules
1,246 decorating reagents - pharmacologically privileged and selected by physico-chemical parameters structures
463,000 compounds
62,000 compounds
Library Generation Strategy
“Rule of Three”-like restrictions: 150 < MW < 300 –2 < ClogP < 3
Hdon ≤ 3 Hacc ≤ 4 RotB ≤ 3
Other requirements: TPSA ≤ 70 Å2
logSw > –3 Heavy atoms (HAC) ≤ 20
PAINS and in-house developed filters for undesired and reactive functionalities At least one ring in structure
No more than two fused aromatic rings No more than one S, Cl, Br
No more than three F Min one additional atom of N and O apart from the main functional group
No N-arylsulfonamides, no tetrazoles, no more than 5 % of compounds with isolated benzene ring
21,200 compounds
5th Step: Final Set Formation Chemotype control. Possibility of evolution (ideally 2 points)
Synthetic considerations Scaffold population analysis
4th Step: Complex Diversity Approach Tanimoto diversity selection 87 %
3D shape analysis Pharmacophore diversity analysis
Complexity optimization
PPI Fragment Library (page 12)
1. Reynes C. et al. PLoSComput Biol. 2010, Mar 5; 6 (3): e1000695. 2. Fry D. C. Biopolymers 2006, 84, 535–52. 3. Carr R. A. et al. Drug Discov. Today 2005, 10, 987–992.
Fragment Libraries from Life Chemicals (page 1)
1. Erlanson D. A.; McDowell R. S.; O’Brien T. J. Med. Chem. 2004, 47, 3463-3482 2. Rees D. C.; Congreve M.; Murray C. W.; Carr R. Nat. Rev. Drug Discovery 2004, 3, 660-
General Fragment Library (page 2)
1. Bruns R. F.; Watson I. A. J. Med. Chem. 2012, 55, 9763–9772. 2. Baell J. B.; Holloway G. A. J. Med. Chem. 2010, 53, 2719–2740.
Low MW Frafment Library (page 4)
1. Wenlock M. C. et al. J. Med. Chem. 2003, 46, 1250–1256. 2. Hann M. M. Part of the Series: NATO Science for Peace and Security, Series A: Chemistry
and Biology. 2015, 183–196. 3. Bruns R. F.; Watson I. A. J. Med. Chem. 2012, 55, 9763–9772. 4. Baell J. B.; Holloway G. A. J. Med. Chem. 2010, 53, 2719–2740.
Bromine Frahment Library (page 6)
1. Tiefenbrunn T. et al. Chem Biol Drug Des. 2014, 83, 141–148.
Fluorine Fragment Library (page 7)
1. Jordan J. B. et al. J. Med. Chem. 2012, 55, 678−687.
Fsp3-enriched Fragment Library (page 8)
1. Yang Y. et al. J. Med. Chem. 2010, 53, 7709–7714. 2. Ritchie T. J. et al. Drug Discov. Today 2011, 16 (3-4), 164-171. 3. Clemons P. A. et al. PNAS 2010, 107 (44), 18787–18792. 4. Baell J. B.; Holloway G. A. J. Med. Chem. 2010, 53, 2719–2740. 5. Lovering F.; Bikker J.; Humblet C. J. Med. Chem. 2009, 52, 6752–6756.
3D Fragment Library (page 14)
1. Morley A. D. et al. Drug Discov. Today, 2013, 18 (23-24), 1221-1227. 2. Alvin W. Hung A. W. et al. PNAS, 2011, 108 (17), 6799 – 6804. 3. Joseph-McCarthy D. et al. J. Chem. Inf. Model. 2014, 54 (3), 693−704. 4. Carballo R. M. et al. J. Chem. Inf. Comput. Sci. 2003, 43 (3), 987 – 1003. 5. Galloway W. R. J. D.; Isidro-Llobet A.; Spring D. R. Nat. Commun. 2010, 1, 80. 6. Koutsoukas A. et al. J. Chem. Inf. Model. 2014, 54 (1), 230−242.
Covalent Fragment Library (page 15)
1. Mah R.; Thomas J. R.; Shafer C. M. Bioorg. Med. Chem. Lett. 2014, 24, 33–39. 2. Johnson D. S.; Weerapana E.; Cravatt B. F. Future Med. Chem. 2010, 2 (6), 949–964. 3. Weerapana E., Simon G., Cravatt B. F. Nature Chemical Bioogyl., Vol. 4, 2008, pp.
405–407. 4. Liu Q. et. al. Chem. Biol., 2013, 20 (2), 146–159. 5. Kathman S.; Ziyang X.; Statsyk A. V. J. Med. Chem. 2014, 57 (11), 4969–4974. 6. Zhu K. et. al. J. Chem. Inf. Model. 2014, 54 (7), 1932-1940. 7. Warshaviak D. T. et al. J. Chem. Inf. Model. 2014, 54 (7), 1941–1950 8. Mah R.; Thomas J. R.; Shafer C. M. Bioorg. Med. Chem. Lett. 2014, 24, 33–39.
Natural Product-like Fragment Library (page 16)
1. Grabowski K.; Schneider G. Curr. Chem. Biol. 2007, 10, 115–127 2. Rizzo S.; Waldmann H. D. Chem. Rev. 2014, 114, 4621–4639. 3. Rodrigues T. et al. Nature Chem. 2016, 8, 531-541. 4. Over, B. et al. Nature Chem. 2013, 5, 21–28. 5. Schuffenhauer A. et al. J Chem Inf Model. 2007, 47, 47–58. 6. Gu J.; Gui Y.; Chen L.; Yuan G.; Lu H.-Z.; Xu X. PLoS ONE, 2013, 8, e62839.
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