Oklahoma Chemical Libraries

Compound Collections

The current Oklahoma Center for Therapeutic Sciences contains 90,000 compounds with the goal of building the library size to 230,000 compounds. A summary of our collections is described below. Our stock compounds are stored as 10mM DMSO stocks.  The compounds are stored in 384-well plates with 320 compounds per plate. The outer columns 1/2 and 23/24 are available for the inclusion of positive and negative controls that are critical in generating formats suitable for high throughput screening.   The libraries are replica-plated into acoustic compatible plates at concentrations of 0.5mM and 2mM.  The library plates are sealed in the presence of argon gas and stored at minus 30o C freezers.  Our standard plating protocol is to dispense a total of 80 compounds in 96-well plates, 320 compounds in 384-well plates or 1280 compounds in 1536-well plates. Users can pick compounds to screen only in blocks of whole plates.  We do not allow users to choose compounds at the well level.

TargetMol Bioactive Library: custom selected 3,840 compounds that modify the activities of approximately 900 validated disease targets or signaling pathways. Some of the pathways or targets affected include apoptosis, autophagy, mTOR signaling, MAPK cascades, epigenetics, GPCR signaling, cell stress, nuclear receptors, PI-3 kinase signaling, tyrosine kinase signaling, inflammation, ion channel activity, cell cycle regulation, DNA damage responses, angiogenesis, ubiquitinylation and metabolism.

The TargetMol FDA-Approved library contains 1,443 small molecules, 100% being marketed drugs. The active compounds were selected for their high chemical and pharmacological diversity as well as for their known bioavailability and safety in humans. The FDA-approved library targets a broad distribution of therapeutic areas (Figure 1).  There is little overlap in chemical identity between the Bioactives and FDA-Approved libraries.

Figure 1. Therapeutic distribution of FDA-approved library

Life Chemical Fsp3-enriched library:

The Life Chemical Fsp3-enriched library contains 10,560 compounds. Fsp3 carbon bond saturation is defined by the fraction of sp3 carbon bonds (Fsp3 = (number of sp3 hybridized carbons/total carbon count) (1,2). To create the library of drug-like sp3-enriched molecules, compounds with increased Fsp3 fraction > 0.47, improved physicochemical properties, higher solubility and high chemical and structural diversity were selected. The Fsp3-enriched compounds occupy greater 3-D chemical space, resulting in an improved potential to better complement the spatial subtleties of target proteins (3). Importantly, the three-dimensionality that saturation imparts may also result in greater selectivity, resulting in fewer off-target effects. In addition, because of the reduced off-target effects, Fsp3 compounds tend to have fewer toxicity problems.

References

1. Lovering F, Bikker J, Humblet C. Escape from flatland: increasing saturation as an approach to improving clinical success. J Med Chem. 2009 Nov 12;52(21):6752-6. PMID: 19827778.
2. Wei W, Cherukupalli S, Jing L, Liu X, Zhan P. Fsp3: A new parameter for drug-likeness. Drug Discov Today. 2020 Oct;25(10):1839-1845. PMID: 32712310.
3.  Hamilton DJ, Dekker T, Klein HF, Janssen GV, Wijtmans M, O'Brien P, de Esch IJP. Escape from planarity in fragment-based drug discovery: A physicochemical and 3D property analysis of synthetic 3D fragment libraries. Drug Discov Today Technol. 2020 Dec; 38:77-90 PMID: 34895643.

Life Chemical 3D Diversity Library

The 3D-shape of a ligand is a critical feature its interaction with a binding site (1). Non-planar 3-dimensional screening compounds occupy a novel chemical space that may have greater tendency to occupy important pockets present in target proteins. Increased three-dimensionality has been shown to correlate with improved clinical development (2). Complex 3D-like sp3-rich screening compounds are advantageous in exploring a large variety of biological targets. The 3-dimensionality is best defined by principal moments of inertia (PMI) parameters thresholds (3,4). In addition to PMI thresholds, a set of criteria (Table 1) was applied, as well as PAINS and Life Chemical developed toxicophore filters to select 18,250 compounds that compose our 3D diversity library.

Table 1. Examples of filters used in selection of 3D compounds

References

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. Sliwoski G, Kothiwale S, Meiler J, Lowe EW Jr. Computational methods in drug discovery. Pharmacol Rev. 2013;66(1):334-395. doi:10.1124/pr.112.007336
3. Firth NC, Brown N, Blagg J. Plane of best fit: a novel method to characterize the three-dimensionality of molecules. J Chem Inf Model. 2012;52(10):2516-2525. doi:10.1021/ci300293f
4.  Meyers J, Carter M, Mok NY, Brown N. On the origins of three-dimensionality in drug-like molecules. Future Med Chem. 2016;8(14):1753-1767. doi:10.4155/fmc-2016-0095

Life Chemical Cysteine-focused covalent inhibitor library

Small molecules that form covalent bonds to inhibit enzymatic activity or modify protein-protein interactions can be effective and specific drugs.  Covalent small molecules can also function as important probes for biological research. Interest in the identification of covalent inhibitors has increased recently due to the success of targeting tyrosine kinases and the Ras oncogene (KRASG12C). The renewed interest in covalent drugs is due their ability to impact “undruggable” targets as well the long-term pharmaceutical activities.     

The Cysteine-focused Screening Compound Library was created on the basis of specific structure moieties that could react reversibly or irreversibly with cysteine residues of a drug target.

The Cysteine-focused covalent inhibitor library contains 3200 small molecules. Covalent inhibitors were selected based on the following warheads (Figure 1):

• α,β-unsaturated ketones
• α-chloracetamides
• activated acetylenes
• acrylonitriles
• acrylamides
• epoxides
• methyl vinylsulfones
• phenylsulphonate esters
• aminomethyl methyl acrylathes
• primary haloalkanes

The compounds were pre-filtered with the Rule of Five restrictions:

• MW 150 - 500
• ClogP -1 - 5
• H-donors 0 - 5
• H-acceptors 0 - 10
• Rotatable bonds ≤ 10
• Diversity filtering

Figure 1. Covalent warheads distribution for compounds in the Cysteine-focused Covalent Inhibitor Library.

References

1. Grams RJ, Hsu KL. Reactive chemistry for covalent probe and therapeutic development. Trends Pharmacol Sci. 2022 Mar;43(3):249-262. PMID: 34998611.
2. Spradlin JN, Zhang E, Nomura DK. Reimagining Druggability Using Chemoproteomic Platforms. Acc Chem Res. 2021 Apr 6;54(7):1801-1813. PMID: 33733731.
3. Tuley A, Fast W. The Taxonomy of Covalent Inhibitors. Biochemistry. 2018 Jun 19;57(24):3326-3337. PMID: 29689165
4. Mukherjee H, Grimster NP. Beyond cysteine: recent developments in the area of targeted covalent inhibition. Curr Opin Chem Biol. 2018 Jun;44:30-38. PMID: 29857316.

Life Chemical CNS Screening Library

CNS Screening Library contains 7,100 structurally diverse screening compounds with properties favorable for BBB-penetration aimed for drug development of CNS-active pharmaceuticals.

Life Chemical Diversity Set

The Life Chemical Diversity Set (PS6) contains 50,240 novel screening compounds with optimal physicochemical properties selected through dissimilarity searches of the Life Chemicals collection of 1.0 million compounds. The library contains a wide range of dissimilar chemical structures with drug-like and lead-like properties. To further ensure a diverse set of compounds, filtering methods also included following Lipinski’s guidelines for druglike-ness, removal of undesirable chemical groups (e.g. Michael acceptors, crown-ether and analogs, disulfides, epoxides, azides, etc) and removal of salt and tautomeric duplicates.

Key features:

• Mean Tanimoto similarity value of the Diversity Set is 0.409 with even lower values in the subsets (calculated with ECFP fingerprints) (1)
• Lipinski Rule of Five and Veber criteria compliant (2,3)
• No reagents. No reactive and unstable molecules
• PAINS filters families A, B, C applied (4)
• Lilly MedChem Rules compliant (5)

References.

1. Jasial S, Hu Y, Vogt M, Bajorath J. Activity-relevant similarity values for fingerprints and implications for similarity searching. F1000Res. 2016 Apr 6;5:Chem Inf Sci-591. doi: 10.12688/f1000research.8357.2. PMID: 27127620; PMCID: PMC4830209.
2. Lipinski CA, Lombardo F, Dominy BW, Feeney PJ. Experimental and computational approaches to estimate solubility and permeability in drug discovery and development settings. Adv Drug Deliv Rev. 2001 Mar 1;46(1-3):3-26. PMID: 11259830.
3. Veber DF, Johnson SR, Cheng HY, Smith BR, Ward KW, Kopple KD. Molecular properties that influence the oral bioavailability of drug candidates. J Med Chem. 2002 Jun 6;45(12):2615-23. doi: 10.1021/jm020017n. PMID: 12036371
4. Baell JB, Nissink JWM. Seven Year Itch: Pan-Assay Interference Compounds (PAINS) in 2017-Utility and Limitations. ACS Chem Biol. 2018 Jan 19;13(1):36-44. PMID: 29202222
5. Bruns RF, Watson IA. Rules for identifying potentially reactive or promiscuous compounds. J Med Chem. 2012 Nov 26;55(22):9763-72. PMID: 23061697.