Discovery of New Anti-Schistosomal Hits by Integration of QSAR-Based Virtual Screening and High Content Screening

Neves, Bruno J.; Dantas, Rafael Ferreira; Senger, Mário Roberto; Melo-Filho, Cleber C.; Valente, Walter C. G.; Almeida, Ana C. M. de; Rezende-Neto, João M.; Lima, Elid F. C.; Paveley, Ross A.; Furnham, Nicholas; Muratov, Eugene; Kamentsky, Lee; Carpenter, Anne E.; Braga, Rodolpho C.; Silva, Floriano Paes; Andrade, Carolina Horta

doi:10.1021/acs.jmedchem.5b02038

Cited by 69 publications

(48 citation statements)

References 99 publications

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“…13 Similarly, lead compounds resulting from experimental screening campaigns have typically been deprioritized for follow-up studies if they contained PAINS alerts. 14 Furthermore, scientific journals have begun to recommend that all hit compounds, virtual or otherwise, should be passed through one of the publicly available PAINS filters before the manuscript is considered for publication.…”

Section: Introductionmentioning

confidence: 99%

Phantom PAINS: Problems with the Utility of Alerts for Pan-Assay INterference CompoundS

Capuzzi

Muratov

Tropsha

2017

J. Chem. Inf. Model.

205

213

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The use of substructural alerts to identify Pan-Assay INterference compoundS (PAINS) has become a common component of the triage process in biological screening campaigns. These alerts, however, were originally derived from a proprietary library tested in just six assays measuring protein–protein interaction (PPI) inhibition using the AlphaScreen detection technology only; moreover, 68% (328 out of the 480 alerts) were derived from four or fewer compounds. In an effort to assess the reliability of these alerts as indicators of pan-assay interference, we performed a large-scale analysis of the impact of PAINS alerts on compound promiscuity in bioassays using publicly available data in PubChem. We found that the majority (97%) of all compounds containing PAINS alerts were actually infrequent hitters in AlphaScreen assays measuring PPI inhibition. We also found that the presence of PAINS alerts, contrary to expectations, did not reflect any heightened assay activity trends across all assays in PubChem including AlphaScreen, luciferase, beta-lactamase, or fluorescence-based assays. In addition, 109 PAINS alerts were present in 3570 extensively assayed, but consistently inactive compounds called Dark Chemical Matter. Finally, we observed that 87 small molecule FDA-approved drugs contained PAINS alerts and profiled their bioassay activity. Based on this detailed analysis of PAINS alerts in nonproprietary compound libraries, we caution against the blind use of PAINS filters to detect and triage compounds with possible PAINS liabilities and recommend that such conclusions should be drawn only by conducting orthogonal experiments.

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Section: Introductionmentioning

confidence: 99%

Phantom PAINS: Problems with the Utility of Alerts for Pan-Assay INterference CompoundS

Capuzzi

Muratov

Tropsha

2017

J. Chem. Inf. Model.

205

213

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“…Therefore, we decided to balance the dataset using linear under-sampling strategy developed by Braga, R.C. (Neves et al, 2016) [86]. Unlike the traditional under-sampling methods which randomly balance the dataset, this strategy retains the most representative inactive compounds in the balanced dataset, thus assuring as high as possible coverage of original chemical space.…”

Section: Methodsmentioning

confidence: 99%

QSAR-driven design, synthesis and discovery of potent chalcone derivatives with antitubercular activity

Gomes

Braga

Grzelak

et al. 2017

European Journal of Medicinal Chemistry

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New anti-tuberculosis (anti-TB) drugs are urgently needed to battle drug-resistant Mycobacterium tuberculosis strains and to shorten the current 6–12-month treatment regimen. In this work, we have continued the efforts to develop chalcone-based anti-TB compounds by using an in silico design and QSAR-driven approach. Initially, we developed SAR rules and binary QSAR models using literature data for targeted design of new chalcone-like compounds with anti-TB activity. Using these models, we prioritized 33 compounds for synthesis and biological evaluation. As a result, 10 chalcones-like compounds (4, 8, 9, 11, 13, 17–20, and 23) were found to exhibit nanomolar activity against replicating micobacteria, low micromolar activity against nonreplicating bacteria, and nanomolar and micromolar against rifampin (RMP) and isoniazid (INH) monoresistant strains (rRMP and rINH) (<1 µM and <10 µM, respectively). The series also show low activity against commensal bacteria and generally show good selectivity toward M. tuberculosis, with very low cytotoxicity against Vero cells (SI = 11–545). Our results suggest that our designed chalcone-like compounds, due to their high potency and selectivity, are promising anti-TB agents.

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“…This analysis showed high concordance between duplicate records for P. falciparum dataset (92%), and cytotoxicity dataset (100%), revealing the high quality of these datasets. Considering the different size of classes in P. falciparum dataset (789 actives and 581 inactives), and cytotoxicity dataset (635 toxic compounds and 933 nontoxic compounds), the curated datasets were balanced using a linear under-sampling approach (i.e., reducing the size of the majority class) [29]. The under-sampling strategy used here retains most of the representative molecules of the majority class in balanced dataset, ensuring the structural diversity of original chemical space [29].…”

Section: Data Curationmentioning

confidence: 99%

“…Considering the different size of classes in P. falciparum dataset (789 actives and 581 inactives), and cytotoxicity dataset (635 toxic compounds and 933 nontoxic compounds), the curated datasets were balanced using a linear under-sampling approach (i.e., reducing the size of the majority class) [29]. The under-sampling strategy used here retains most of the representative molecules of the majority class in balanced dataset, ensuring the structural diversity of original chemical space [29]. Initially, the Euclidean distances between each compound in majority class and whole set of minority class are measured using k-Nearest Neighbor (k-NN) algorithm [56].…”

Section: Data Curationmentioning

confidence: 99%

“…The availability of large datasets of chemical compounds with at least one biological property measured [25,26], associated with thousands of molecular descriptors paired with the popularization of in silico approaches resulted in the widespread use of QSAR for a diverse array of biological properties relevant to drug discovery [27][28][29][30][31][32]. However, dealing with big datasets has posed a challenge to model biological properties using classical machine learning algorithms [33,34].…”

Section: Introductionmentioning

confidence: 99%

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Deep Learning-driven research for drug discovery: Tackling Malaria

Neves

Braga²,

Alves

et al. 2020

PLoS Comput Biol

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Malaria is an infectious disease that affects over 216 million people worldwide, killing over 445,000 patients annually. Due to the constant emergence of parasitic resistance to the current antimalarial drugs, the discovery of new drug candidates is a major global health priority. Aiming to make the drug discovery processes faster and less expensive, we developed binary and continuous Quantitative Structure-Activity Relationships (QSAR) models implementing deep learning for predicting antiplasmodial activity and cytotoxicity of untested compounds. Then, we applied the best models for a virtual screening of a large database of chemical compounds. The top computational predictions were evaluated experimentally against asexual blood stages of both sensitive and multi-drug-resistant Plasmodium falciparum strains. Among them, two compounds, LabMol-149 and LabMol-152, showed potent antiplasmodial activity at low nanomolar concentrations (EC 50 <500 nM) and low cytotoxicity in mammalian cells. Therefore, the computational approach employing deep learning developed here allowed us to discover two new families of potential next generation antimalarial agents, which are in compliance with the guidelines and criteria for antimalarial target candidates. Author summaryMalaria is a serious infectious disease caused by parasites of the genus Plasmodium. The recommended treatment is a combination of antimalarial drugs. However, the rise of parasites resistant to the current antimalarial drugs means that new therapeutics are continually required. To meet this challenge, we developed and applied models using deep learning, a powerful artificial intelligence method, supported by experimental validation PLOS Computational Biology | https://doi.to identify new drug candidates against malaria. We used the developed computational models to prioritize novel, active, and nontoxic compounds from virtual chemical libraries for experimental evaluation. Then, the predicted antimalarial compounds were experimentally validated in assays on Plasmodium falciparum culture. This allowed us to discover two new potential antimalarial candidates. The use of computational approaches is an attractive route to expedite the discovery of new therapeutics, especially to infectious tropical diseases, as it can reduce time and development costs. Future directions include in vivo studies on animal models.Deep Learning-driven research for tackling Malaria PLOS Computational Biology | https://doi.

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Discovery of New Anti-Schistosomal Hits by Integration of QSAR-Based Virtual Screening and High Content Screening

Cited by 69 publications

References 99 publications

Phantom PAINS: Problems with the Utility of Alerts for Pan-Assay INterference CompoundS

Phantom PAINS: Problems with the Utility of Alerts for Pan-Assay INterference CompoundS

QSAR-driven design, synthesis and discovery of potent chalcone derivatives with antitubercular activity

Deep Learning-driven research for drug discovery: Tackling Malaria

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