36 research outputs found
Analysis of Iterative Screening with Stepwise Compound Selection Based on Novartis In-house HTS Data.
No full textWith increased automation and larger compound collections, the development of high-throughput screening (HTS) started replacing previous approaches in drug discovery from around the 1980s onward. However, even today it is not always appropriate, or even feasible, to screen large collections of compounds in a particular assay. Here, we present an efficient method for iterative screening of small subsets of compound libraries. With this method, the retrieval of active compounds is optimized using their structural information and biological activity fingerprints. We validated this approach retrospectively on 34 Novartis in-house HTS assays covering a wide range of assay biology, including cell proliferation, antibacterial activity, gene expression, and phosphorylation. This method was employed to retrieve subsets of compounds for screening, where selected hits from any given round of screening were used as starting points to select chemically and biologically similar compounds for the next iteration. By only screening ∼1% of the full screening collection (∼15 000 compounds), the method consistently retrieves diverse compounds belonging to the top 0.5% of the most active compounds for the HTS campaign. For most of the assays, over half of the compounds selected by the method were found to be among the 5% most active compounds of the corresponding full-deck HTS. In addition, the stringency of the iterative method can be modified depending on the number of compounds one can afford to screen, making it a flexible tool to discover active compounds efficiently
Data-Driven Derivation of an "Informer Compound Set" for Improved Selection of Active Compounds in High-Throughput Screening.
No full textDespite the usefulness of high-throughput screening (HTS) in drug discovery, for some systems, low assay throughput or high screening cost can prohibit the screening of large numbers of compounds. In such cases, iterative cycles of screening involving active learning (AL) are employed, creating the need for smaller "informer sets" that can be routinely screened to build predictive models for selecting compounds from the screening collection for follow-up screens. Here, we present a data-driven derivation of an informer compound set with improved predictivity of active compounds in HTS, and we validate its benefit over randomly selected training sets on 46 PubChem assays comprising at least 300,000 compounds and covering a wide range of assay biology. The informer compound set showed improvement in BEDROC( = 100), PRAUC, and ROCAUC values averaged over all assays of 0.024, 0.014, and 0.016, respectively, compared to randomly selected training sets, all with paired -test p-values <10. A per-assay assessment showed that the BEDROC( = 100), which is of particular relevance for early retrieval of actives, improved for 38 out of 46 assays, increasing the success rate of smaller follow-up screens. Overall, we showed that an informer set derived from historical HTS activity data can be employed for routine small-scale exploratory screening in an assay-agnostic fashion. This approach led to a consistent improvement in hit rates in follow-up screens without compromising scaffold retrieval. The informer set is adjustable in size depending on the number of compounds one intends to screen, as performance gains are realized for sets with more than 3,000 compounds, and this set is therefore applicable to a variety of situations. Finally, our results indicate that random sampling may not adequately cover descriptor space, drawing attention to the importance of the composition of the training set for predicting actives
Revised classification of kinases based on bioactivity data: the importance of data density and choice of visualization
No full textAre phylogenetic trees suitable for chemogenomics analyses of bioactivity data sets: the importance of shared active compounds and choosing a suitable data embedding method, as exemplified on Kinases
No full textProteochemometric modelling coupled to in silico target prediction: an integrated approach for the simultaneous prediction of polypharmacology and binding affinity/potency of small molecules
No full textInternational audienceThe rampant increase of public bioactivity databases has fostered the development of computational chemogenomics methodologies to evaluate potential ligand-target interactions (polypharmacology) both in a qualitative and quantitative way. Bayesian target prediction algorithms predict the probability of an interaction between a compound and a panel of targets, thus assessing compound polypharmacology qualitatively, whereas structure-activity relationship techniques are able to provide quantitative bioactivity predictions. We propose an integrated drug discovery pipeline combining in silico target prediction and proteochemometric modelling (PCM) for the respective prediction of compound polypharmacology and potency/affinity. The proposed pipeline was evaluated on the retrospective discovery of Plasmodium falciparum DHFR inhibitors. The qualitative in silico target prediction model comprised 553,084 ligand-target associations (a total of 262,174 compounds), covering 3,481 protein targets and used protein domain annotations to extrapolate predictions across species. The prediction of bioactivities for plasmodial DHFR led to a recall value of 79% and a precision of 100%, where the latter high value arises from the structural similarity of plasmodial DHFR inhibitors and T. gondii DHFR inhibitors in the training set. Quantitative PCM models were then trained on a dataset comprising 20 eukaryotic, protozoan and bacterial DHFR sequences, and 1,505 distinct compounds (in total 3,099 data points). The most predictive PCM model exhibited R 2 0 test and RMSE test values of 0.79 and 0.59 pIC 50 units respectively, which was shown to outperform models based exclusively on compound (R 2 0 test /RMSE test = 0.63/0.78) and target information (R 2 0 test /RMSE test = 0.09/1.22), as well as inductive transfer knowledge between targets, with respective R 2 0 test and RMSE test values of 0.76 and 0.63 pIC 50 units. Finally, both methods were integrated to predict the protein targets and the potency on plasmodial DHFR for the GSK TCAMS dataset, which comprises 13,533 compounds displaying strong anti-malarial activity. 534 of those compounds were identified as DHFR inhibitors by the target prediction algorithm, while the PCM algorithm identified 25 compounds, and 23 compounds (predicted pIC 50 > 7) were identified by both methods. Overall, this integrated approach simultaneously provides target and potency/affinity predictions for small molecules
Data-Driven Derivation of an “Informer Compound Set” for Improved Selection of Active Compounds in High-Throughput Screening
No full textDespite the usefulness of high-throughput
screening (HTS) in drug
discovery, for some systems, low assay throughput or high screening
cost can prohibit the screening of large numbers of compounds. In
such cases, iterative cycles of screening involving active learning
(AL) are employed, creating the need for smaller “informer
sets” that can be routinely screened to build predictive models
for selecting compounds from the screening collection for follow-up
screens. Here, we present a data-driven derivation of an informer
compound set with improved predictivity of active compounds in HTS,
and we validate its benefit over randomly selected training sets on
46 PubChem assays comprising at least 300,000 compounds and covering
a wide range of assay biology. The informer compound set showed improvement
in BEDROC(α = 100), PRAUC, and ROCAUC values averaged over all
assays of 0.024, 0.014, and 0.016, respectively, compared to randomly
selected training sets, all with paired t-test p-values
–15. A per-assay assessment showed that the
BEDROC(α = 100), which is of particular relevance for early
retrieval of actives, improved for 38 out of 46 assays, increasing
the success rate of smaller follow-up screens. Overall, we showed
that an informer set derived from historical HTS activity data can
be employed for routine small-scale exploratory screening in an assay-agnostic
fashion. This approach led to a consistent improvement in hit rates
in follow-up screens without compromising scaffold retrieval. The
informer set is adjustable in size depending on the number of compounds
one intends to screen, as performance gains are realized for sets
with more than 3,000 compounds, and this set is therefore applicable
to a variety of situations. Finally, our results indicate that random
sampling may not adequately cover descriptor space, drawing attention
to the importance of the composition of the training set for predicting
actives
A nano-MgO and ionic liquid-catalyzed 'green' synthesis protocol for the development of adamantyl-imidazolo-thiadiazoles as anti-tuberculosis agents targeting sterol 14α-demethylase (CYP51)
No full textIn this work, we describe the 'green' synthesis of novel 6-(adamantan-1-yl)-2-substitutedimidazo2,1-b1,3,4thiadiazoles (AITs) by ring formation reactions using 1-(adamantan-1-yl)-2-bromoethanone and 5-alkyl/aryl-2-amino1,3,4-thiadiazoles on a nano material base in ionic liquid media. Given the established activity of imidazothiadiazoles against M. tuberculosis, we next examined the anti-TB activity of AITs against the H37Rv strain using Alamar blue assay. Among the tested compounds 6-(adamantan-1-yl)-2-(4-methoxyphenyl)imidazo2,1-b1,3,4thiadiazole (3f) showed potent inhibitory activity towards M. tuberculosis with an MIC value of 8.5 μM. The inhibitory effect of this molecule against M. tuberculosis was comparable to the standard drugs such as Pyrazinamide, Streptomycin, and Ciprofloxacin drugs. Mechanistically, an in silico analysis predicted sterol 14α-demethylase (CYP51) as the likely target and experimental activity of 3f in this system corroborated the in silico target prediction. In summary, we herein report the synthesis and biological evaluation of novel AITs against M. tuberculosis that likely target CYP51 to induce their antimycobacterial activity. © 2015 Anusha et al. This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited
How Diverse Are Diversity Assessment Methods? A Comparative Analysis and Benchmarking of Molecular Descriptor Space
No full textSynthesis, biological evaluation and in silico and in vitro mode-of-action analysis of novel dihydropyrimidones targeting PPAR-gamma
No full textHepatocellular carcinoma, a fatal liver cancer, affects 600 000 people annually and ranks third in cancer-related lethality. In this work we report the synthesis and related biological activity of novel dihydropyrimidones. Among the tested compounds, 5-acetyl-4-(1H-indol- 3-yl)-6-methyl-3,4-dihydropyrimidin-2(1H)-one (4g) was found to be most active towards the HepG2 cell line (IC50 = 17.9 mu M), being at the same time 7.6-fold selective over normal (LO2) liver cells (IC50 = 136.9 mu M). Subsequently, we identified peroxisome proliferator-activated receptor gamma as a target of compound 4g using an in silico approach, and confirmed this mode-of-action experimentally
Nano-MoO3-mediated synthesis of bioactive thiazolidin-4-ones acting as anti-bacterial agents and their mode-of-action analysis using in silico target prediction, docking and similarity searching
No full textThe efficacy of thiazolidin-4-ones as synthons for diverse biological small molecules has given impetus to anti-bacterial studies. Our work aims to synthesize novel bioactive thiazolidin-4-ones using nano-MoO3 for the first time. The compelling advantage of using nano-MoO3 is that the recovered nano-MoO3 can be reused thrice without considerable loss of its catalytic activity. The synthesized thiazolidin-4-ones were tested for anti-bacterial activity against two strains of pathogenic bacteria, namely, Salmonella typhi and Klebsiella pneumoniae. Our results indicated that 3-(benzodisoxazol-3-yl)-2-(3-methoxyphenyl)thiazolidine-4-one (compound 3b) showed significant inhibitory activity towards Salmonella typhi{,} in comparison with gentamicin. Furthermore{,} in silico target prediction presented the target of compound 3b as the FtsK motor domain of DNA translocase of Salmonella typhi. Hence{,} our hypothesis is that compound 3b may disrupt chromosomal segregation and thereby inhibit the division of Salmonella typhi. In addition{,} similarity searching showed that 34 compounds with a chemical similarity of 70% or higher to compound 3b{,} which were retrieved from ChEMBL{,} bound to targets associated with biological processes related to cell development in 36% of the cases. In summary{,} our work details novel usage of nano-MoO3 for the synthesis of novel thiazolidin-4-ones possessing anti-bacterial activity{,} and presents a mode-of-action hypothesis
