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Design of chemical libraries with potentially bioactive molecules applying a maximum common substructure concept.

ABSTRACT: Success in small molecule screening relies heavily on the preselection of compounds. Here, we present a strategy for the enrichment of chemical libraries with potentially bioactive compounds integrating the collected knowledge of medicinal chemistry. Employing a genetic algorithm, substructures typically occurring in bioactive compounds were identified using the World Drug Index. Availability of compounds containing the selected substructures was analysed in vendor libraries, and the substructure-specific sublibraries were assembled. Compounds containing reactive, undesired functional groups were omitted. Using a diversity filter for both physico-chemical properties and the substructure composition, the compounds of all the sublibraries were ranked. Accordingly, a screening collection of 16,671 compounds was selected. Diversity and chemical space coverage of the collection indicate that it is highly diverse and well-placed in the chemical space spanned by bioactive compounds. Furthermore, secondary assay-validated hits presented in this study show the practical relevance of our library design strategy.

SUBMITTER: Lisurek M

PROVIDER: S-EPMC7089384 | biostudies-literature | 2010 May

REPOSITORIES: biostudies-literature

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Design of chemical libraries with potentially bioactive molecules applying a maximum common substructure concept.

Lisurek Michael M Rupp Bernd B Wichard Jörg J Neuenschwander Martin M von Kries Jens Peter JP Frank Ronald R Rademann Jörg J Kühne Ronald R

Molecular diversity 20090815 2

Success in small molecule screening relies heavily on the preselection of compounds. Here, we present a strategy for the enrichment of chemical libraries with potentially bioactive compounds integrating the collected knowledge of medicinal chemistry. Employing a genetic algorithm, substructures typically occurring in bioactive compounds were identified using the World Drug Index. Availability of compounds containing the selected substructures was analysed in vendor libraries, and the substructur ...[more]

PMID: 19685275

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Project description:The prediction of biological targets of bioactive molecules from machine-readable materials can be routinely performed by computational target prediction tools (CTPTs). However, the prediction of biological targets of bioactive molecules from non-digital materials (e.g., printed or handwritten documents) has not been possible due to the complex nature of bioactive molecules and impossibility of employing computations. Improving the target prediction accuracy is the most important challenge for computational target prediction. A minimum structure is identified for each group of neighbor molecules in the proposed method. Each group of neighbor molecules represents a distinct structural class of molecules with the same function in relation to the target. The minimum structure is employed as a query to search for molecules that perfectly satisfy the minimum structure of what is guessed crucial for the targeted activity. The proposed method is based on chemical similarity, but only molecules that perfectly satisfy the minimum structure are considered. Structurally related bioactive molecules found with the same minimum structure were considered as neighbor molecules of the query molecule. The known target of the neighbor molecule is used as a reference for predicting the target of the neighbor molecule with an unknown target. A lot of information is needed to identify the minimum structure, because it is necessary to know which part(s) of the bioactive molecule determines the precise target or targets responsible for the observed phenotype. Therefore, the predicted target based on the minimum structure without employing the statistical significance is considered as a reliable prediction. Since only molecules that perfectly (and not partly) satisfy the minimum structure are considered, the minimum structure can be used without similarity calculations in non-digital materials and with similarity calculations (perfect similarity) in machine-readable materials. Nine tools (PASS online, PPB, SEA, TargetHunter, PharmMapper, ChemProt, HitPick, SuperPred, and SPiDER), which can be used for computational target prediction, are compared with the proposed method for 550 target predictions. The proposed method, SEA, PPB, and PASS online, showed the best quality and quantity for the accurate predictions.

Project description:Fungal metabolites, proteins, and enzymes have been rich sources of therapeutics so far. Therefore, in this study, the hypha extract of a newly identified noble fungus (Alternaria sp. with NCBI Accession number: MT982648) was used to synthesize silver nanoparticles (F-AgNPs) to utilize against bacteria, fungi, and lung cancer. F-AgNPs were characterized by using physical techniques, including UV-visible spectroscopy, zeta potential, DLS, XRD, TEM, and HR-TEM. The particles were found to be polydispersed and quasi-spherical in shape under TEM. They had an average size of ~15 nm. The well dispersed particles were found to have consistent crystallinity with cubic phase geometry under XRD and HR-TEM. The presence of different functional groups on the surfaces of biosynthesized F-AgNPs was confirmed by FTIR. The particle distribution index was found to be 0.447 with a hydrodynamic diameter of ~47 d.nm, and the high value of zeta potential (-20.3 mV) revealed the stability of the nanoemulsion. These particles were found to be active against Staphylococcus aureus (multidrug resistance-MDR), Klebsiella pneumonia, Salmonella abony, and Escherichia coli (MDR) with MIC50 10.3, 12.5, 22.69, and 16.25 µg/mL, respectively. Particles also showed inhibition against fungal strains, including A. flavus, A. niger, T. viridens, and F. oxysporium. Their inhibition of biofilm formation by the same panel of bacteria was also found to be very promising and ranged from 16.66 to 64.81%. F-AgNPs also showed anticancer potential (IC50-21.6 µg/mL) with respect to methotrexate (IC50-17.7 µg/mL) against lung cancer cell line A549, and they did not result in any significant inhibition of the normal cell line BEAS-2. The particles were found to alter the mitochondrial membrane potential, thereby disturbing ATP synthesis and leading to high ROS formation, which are responsible for cell membrane damage and release of LDH, intracellular proteins, lipids, and DNA. A high level of ROS also elicits pro-inflammatory signaling cascades that lead to programmed cell death by either apoptosis or necrosis.

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Design of chemical libraries with potentially bioactive molecules applying a maximum common substructure concept.

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Design of chemical libraries with potentially bioactive molecules applying a maximum common substructure concept.

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