Advances and Challenges in Computational Target Prediction

Sydow, Dominique; Burggraaff, Lindsey; Szengel, Angelika; Vlijmen, Herman van; IJzerman, Adriaan P.; Westen, Gerard J. P. van; Volkamer, Andrea

doi:10.1021/acs.jcim.8b00832

Cited by 99 publications

(69 citation statements)

References 136 publications

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“…Whitish solid. mp: 142-144 C. 1 3-(4-Fluorobenzyl)-1-(4-(trifluoromethyl)benzyl)-6,7-dimethoxyquinazolin-2,4(1H,3H)-dione (10). Reagents: 3-(4-fluorobenzyl)-6,7-dimethoxyquinazolin-2,4(1H,3H)-dione (1.2 mmol) obtained by method A from methyl 2-amino-4,5-dimethoxibenzoate and 1-fluoro-4-(isocyanatomethyl)benzene), 4-(trifluoromethyl)benzyl chloride (266 mL, 1.8 mmol), NaHCO 3 (302 mg, 3.6 mmol) and DMF (6.0 mL).…”

Section: Chemical Proceduresmentioning

confidence: 99%

“…Particular advantages and disadvantages of phenotypic vs. target-based approaches are well known, and the combination of both strategies is logically the best way to move forward and optimise the drug discovery process 8,9 . Recognising the pivotal role of target identification and the challenging task of identifying the MOA for bioactive small molecules, significant progress has been made to develop a number of computational strategies to unveil the MOA of phenotypic hits 10 . In silico target identification offers chances for drug repurposing and for the detection of new links between disease and known targets.…”

Section: Introductionmentioning

confidence: 99%

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Deciphering the enzymatic target of a new family of antischistosomal agents bearing a quinazoline scaffold using complementary computational tools

Sebastián-Pérez

García‐Rubia

el-Din

et al. 2020

Journal of Enzyme Inhibition and Medicinal Chemistry

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A previous phenotypic screening campaign led to the identification of a quinazoline derivative with promising in vitro activity against Schistosoma mansoni. Follow-up studies of the antischistosomal potential of this candidate are presented here. The in vivo studies in a S. mansoni mouse model show a significant reduction of total worms and a complete disappearance of immature eggs when administered concomitantly with praziquantel in comparison with the administration of praziquantel alone. This fact is of utmost importance because eggs are responsible for the pathology and transmission of the disease. Subsequently, the chemical optimisation of the structure in order to improve the metabolic stability of the parent compound was carried out leading to derivatives with improved drug-like properties. Additionally, the putative target of this new class of antischistosomal compounds was envisaged by using computational tools and the binding mode to the target enzyme, aldose reductase, was proposed.

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Section: Chemical Proceduresmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Deciphering the enzymatic target of a new family of antischistosomal agents bearing a quinazoline scaffold using complementary computational tools

Sebastián-Pérez

García‐Rubia

el-Din

et al. 2020

Journal of Enzyme Inhibition and Medicinal Chemistry

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“…Some of the most popular methods are random forest [11] (RF) and support vector machine [12] (SVM) models. A comprehensive overview and review of drug target prediction methods is given by Sydow et al [13].…”

Section: Introductionmentioning

confidence: 99%

Industry-scale application and evaluation of deep learning for drug target prediction

et al. 2020

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Artificial intelligence (AI) is undergoing a revolution thanks to the breakthroughs of machine learning algorithms in computer vision, speech recognition, natural language processing and generative modelling. Recent works on publicly available pharmaceutical data showed that AI methods are highly promising for Drug Target prediction. However, the quality of public data might be different than that of industry data due to different labs reporting measurements, different measurement techniques, fewer samples and less diverse and specialized assays. As part of a European funded project (ExCAPE), that brought together expertise from pharmaceutical industry, machine learning, and high-performance computing, we investigated how well machine learning models obtained from public data can be transferred to internal pharmaceutical industry data. Our results show that machine learning models trained on public data can indeed maintain their predictive power to a large degree when applied to industry data. Moreover, we observed that deep learning derived machine learning models outperformed comparable models, which were trained by other machine learning algorithms, when applied to internal pharmaceutical company datasets. To our knowledge, this is the first large-scale study evaluating the potential of machine learning and especially deep learning directly at the level of industry-scale settings and moreover investigating the transferability of publicly learned target prediction models towards industrial bioactivity prediction pipelines.

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“…The latter can be achieved by a so-called “inverse VS” utilizing techniques like 2D-similarity searches [ 7 ], 3D-similarity searches [ 8 ], and pharmacophore-based VS [ 9 , 10 ]. Many such tools have been made public in the past decade [ 11 , 12 , 13 ], aiming to boost both drug repurposing efforts and drug discovery as a whole.…”

Section: Introductionmentioning

confidence: 99%

Finding New Molecular Targets of Familiar Natural Products Using In Silico Target Prediction

Mayr

Möller

Garscha

et al. 2020

IJMS

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Natural products comprise a rich reservoir for innovative drug leads and are a constant source of bioactive compounds. To find pharmacological targets for new or already known natural products using modern computer-aided methods is a current endeavor in drug discovery. Nature’s treasures, however, could be used more effectively. Yet, reliable pipelines for the large-scale target prediction of natural products are still rare. We developed an in silico workflow consisting of four independent, stand-alone target prediction tools and evaluated its performance on dihydrochalcones (DHCs)—a well-known class of natural products. Thereby, we revealed four previously unreported protein targets for DHCs, namely 5-lipoxygenase, cyclooxygenase-1, 17β-hydroxysteroid dehydrogenase 3, and aldo-keto reductase 1C3. Moreover, we provide a thorough strategy on how to perform computational target predictions and guidance on using the respective tools.

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Advances and Challenges in Computational Target Prediction

Cited by 99 publications

References 136 publications

Deciphering the enzymatic target of a new family of antischistosomal agents bearing a quinazoline scaffold using complementary computational tools

Deciphering the enzymatic target of a new family of antischistosomal agents bearing a quinazoline scaffold using complementary computational tools

Industry-scale application and evaluation of deep learning for drug target prediction

Finding New Molecular Targets of Familiar Natural Products Using In Silico Target Prediction

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