Showing papers on "Chemical library published in 2023"

Modeling the expansion of virtual screening libraries

Abstract: Recently, 'tangible' virtual libraries have made billions of molecules readily available. Prioritizing these molecules for synthesis and testing demands computational approaches, such as docking. Their success may depend on library diversity, their similarity to bio-like molecules and how receptor fit and artifacts change with library size. We compared a library of 3 million 'in-stock' molecules with billion-plus tangible libraries. The bias toward bio-like molecules in the tangible library decreases 19,000-fold versus those 'in-stock'. Similarly, thousands of high-ranking molecules, including experimental actives, from five ultra-large-library docking campaigns are also dissimilar to bio-like molecules. Meanwhile, better-fitting molecules are found as the library grows, with the score improving log-linearly with library size. Finally, as library size increases, so too do rare molecules that rank artifactually well. Although the nature of these artifacts changes from target to target, the expectation of their occurrence does not, and simple strategies can minimize their impact.

Accelerating inhibitor discovery for deubiquitinating enzymes

Abstract: Deubiquitinating enzymes (DUBs) are an emerging drug target class of ~100 proteases that cleave ubiquitin from protein substrates to regulate many cellular processes. A lack of selective chemical probes impedes pharmacologic interrogation of this important gene family. DUBs engage their cognate ligands through a myriad of interactions. We embrace this structural complexity to tailor a chemical diversification strategy for a DUB-focused covalent library. Pairing our library with activity-based protein profiling as a high-density primary screen, we identify selective hits against 23 endogenous DUBs spanning four subfamilies. Optimization of an azetidine hit yields a probe for the understudied DUB VCPIP1 with nanomolar potency and in-family selectivity. Our success in identifying good chemical starting points as well as structure-activity relationships across the gene family from a modest but purpose-build library challenges current paradigms that emphasize ultrahigh throughput in vitro or virtual screens against an ever-increasing scope of chemical space.

Isolation of novel chemical components and their plant target proteins under selenium stress.

Abstract: Selenium is recognized as a beneficial nutrient in living organisms. Excessive amounts of selenium, however, can have a significant negative impact on organisms. Screening of novel chemical compounds that regulate and/or moderate selenium in plants was conducted. The present chapter discusses (1) the design of a chemical screening strategy, (2) methods used to identify and select candidate chemicals, and (3) the identification of chemical-binding target proteins. We identified a novel chemical compound, C9H8N2OS2, in our screening program that enhances selenate accumulation and stress tolerance. The target protein, beta-glucosidase 23, in Arabidopsis was found to regulate selenium accumulation, as well as plant response to selenate stress.

Chemical Library Space: Definition and DNA-Encoded Library Comparison Study Case.

Abstract: The development of DNA-encoded library (DEL) technology introduced new challenges for the analysis of chemical libraries. It is often useful to consider a chemical library as a stand-alone chemoinformatic object─represented both as a collection of independent molecules, and yet an individual entity─in particular, when they are inseparable mixtures, like DELs. Herein, we introduce the concept of chemical library space (CLS), in which resident items are individual chemical libraries. We define and compare four vectorial library representations obtained using generative topographic mapping. These allow for an effective comparison of libraries, with the ability to tune and chemically interpret the similarity relationships. In particular, property-tuned CLS encodings enable to simultaneously compare libraries with respect to both property and chemotype distributions. We apply the various CLS encodings for the selection problem of DELs that optimally "match" a reference collection (here ChEMBL28), showing how the choice of the CLS descriptors may help to fine-tune the "matching" (overlap) criteria. Hence, the proposed CLS may represent a new efficient way for polyvalent analysis of thousands of chemical libraries. Selection of an easily accessible compound collection for drug discovery, as a substitute for a difficult to produce reference library, can be tuned for either primary or target-focused screening, also considering property distributions of compounds. Alternatively, selection of libraries covering novel regions of the chemical space with respect to a reference compound subspace may serve for library portfolio enrichment.

Illumina Dark Chemical Matter using the Cell Painting Assay

Abstract: The identification of bioactive small molecules is at the heart of chemical biology and medicinal research. The screening for modulators of disease-relevant targets and phenotypes is the first step on the way to new drugs. Therefore, large compound libraries have been synthesized and employed by academia and, particularly, pharmaceutical companies to meet the need for chemical entities that are as diverse as possible. Extensive screening of these compound libraries revealed a portion of small molecules that is inactive in more than 100 different assays and was therefore termed ‘dark chemical matter’ (DCM). Deorphanization of DCM promises to yield very selective compounds as they, by definition, should have less off-target effects. We employed morphological profiling using the Cell painting assay (CPA) to detect bioactive DCM compounds. CPA is not biased to a given target or phenotype and can detect various unrelated mechanisms and modes of action. Within the DCM collection, we identified bioactive compounds and confirmed several modulators of microtubules, DNA synthesis and pyrimidine biosynthesis. Profiling approaches are therefore powerful tools to probe compound collections for bioactivity in an unbiased manner and particularly suitable for deorphanization of DCM.

Structure-based virtual screening and molecular dynamics simulations for detecting novel candidates for allosteric inhibition of EGFRT790M.

Abstract: Structure-based virtual screening (SBVS) was applied to predict lead compounds for the allosteric inhibition of epidermal growth factor receptor (EGFR) by screening the library of chemical compounds prepared from the e-molecules chemical database. The library of chemical compounds consisting of 133,083 ligands was composed by evaluating the chemical and physical properties of e-molecules chemicals. The prepared library was screened by CCDC Gold software in the allosteric binding site of EGFRT790M using the library and virtual screening default parameters to filter out, respectively. The GOLD fitness scores 75 and 80 were selected as threshold values for the library and virtual screening processes, respectively. After the docking study, molecular dynamics simulations (MDS) of the top 25 compounds were built for calculating binding free energies from their MDS trajectories. MM-GBSA binding free energies for the compounds were computed from 20 ns MDS, 50 ns MDS and 200 ns MDS trajectories to filter out the candidates. Following MM-GBSA/MM-PBSA binding free energy calculations, six compounds were detected as the most promising candidates for allosteric inhibition of EGFRT790M. The dynamic behaviors of final compounds inside EGFR T790M were searched using structure stability, binding modes and energy decomposition analysis. Besides, the estimated inhibitors were exposed to docking study and MM-GBSA/MM-PBSA binding free energy calculations inside wild-type EGFR, respectively, to be determined their selectivity towards mutant form. Five of the estimated inhibitors displayed estimated selectivity towards EGFRT790M. Besides the ADMET properties of the estimated inhibitors were predicted by PreAdmet tools.Communicated by Ramaswamy H. Sarma.

Combining pharmacophore models derived from DNA-encoded chemical libraries with structure-based exploration to predict Tankyrase 1 inhibitors

Abstract: DNA-encoded chemical libraries (DECLs) interrogate the interactions of a target of interest with vast numbers of molecules. DECLs hence provide abundant information about the chemical ligand space for therapeutic targets, and there is considerable interest in methods for exploiting DECL screening data to predict novel ligands. Here we introduce one such approach and demonstrate its feasibility using the cancer-related poly-(ADP-ribose)transferase tankyrase 1 (TNKS1) as a model target. First, DECL affinity selections resulted in structurally diverse TNKS1 inhibitors with high potency including compound 2 with an IC50 value of 0.8 nM. Additionally, TNKS1 hits from four DECLs were translated into pharmacophore models, which were exploited in combination with docking-based screening to identify TNKS1 ligand candidates in databases of commercially available compounds. This computational strategy afforded TNKS1 inhibitors that are outside the chemical space covered by the DECLs and yielded the drug-like lead compound 12 with an IC50 value of 22 nM. The study further provided insights in the reliability of screening data and the effect of library design on hit compounds. In particular, the study revealed that while in general DECL screening data are in good agreement with off-DNA ligand binding, unpredictable interactions of the DNA-attachment linker with the target protein contribute to the noise in the affinity selection data.

Molecular Filters in Medicinal Chemistry

Abstract: Efficient chemical library design for high-throughput virtual screening and drug design requires a pre-screening filter pipeline capable of labeling aggregators, pan-assay interference compounds (PAINS), and rapid elimination of swill (REOS); identifying or excluding covalent binders; flagging moieties with specific bio-evaluation data; and incorporating physicochemical and pharmacokinetic properties early in the design without compromising the diversity of chemical moieties present in the library. This adaptation of the chemical space results in greater enrichment of hit lists, identified compounds with greater potential for further optimization, and efficient use of computational time. A number of medicinal chemistry filters have been implemented in the Konstanz Information Miner (KNIME) software and analyzed their impact on testing representative libraries with chemoinformatic analysis. It was found that the analyzed filters can effectively tailor chemical libraries to a lead-like chemical space, identify protein–protein inhibitor-like compounds, prioritize oral bioavailability, identify drug-like compounds, and effectively label unwanted scaffolds or functional groups. However, one should be cautious in their application and carefully study the chemical space suitable for the target and general medicinal chemistry campaign, and review passed and labeled compounds before taking further in silico steps.