scholarly journals Pharmacophore-Based Virtual Screening to Discover New Active Compounds for Human Choline Kinase α1

2015 ◽  
Vol 34 (6-7) ◽  
pp. 458-466 ◽  
Author(s):  
Lucía Serrán-Aguilera ◽  
Roberto Nuti ◽  
Luisa C. López-Cara ◽  
Miguel Á. Gallo Mezo ◽  
Antonio Macchiarulo ◽  
...  
Keyword(s):  
Virtual Screening ◽  
Choline Kinase ◽  
Active Compounds

Virtual Screening in Chinese Herbs with Multi-Target Effect on Alzheimer's Disease

2013 ◽  
Vol 765-767 ◽  
pp. 256-260
Author(s):  
Yan Ling Zhang ◽  
Yuan Ming Wang ◽  
Yan Jiang Qiao
Keyword(s):  
Alzheimer’S Disease ◽  
Alzheimer's Disease ◽  
Virtual Screening ◽  
Glycogen Synthase ◽  
Chinese Herbs ◽  
Multiple Targets ◽  
Active Compounds ◽  
Ache Inhibitors ◽  
Gsk 3Β ◽  
Pharmacophore Models

Multiple targets which closely related to Alzheimer's disease (AD) pathogenesis were selected for pharmacophore models generation and virtual screening in Chinese herbs. The targets comprised Acetylcholinesterase (AchE), muscarinic receptor 1 (M1), γ-secretase and glycogen synthase kinase 3β (GSK-3β). The pharmacophore models, which of AchE inhibitors, M1 agonists, γ-secretase inhibitors and GSK-3β inhibitors, were constructed by distance comparison method. Four testing databases for the evaluation of pharmacophore models were constructed with the active compounds with clearly marked activity on each target. The metric CAI (Comprehensive Appraisal Index) was then used to evaluate and obtain the best pharmacophore models of each target, which were then applied to screen the Traditional Chinese Medicine Database for potential active compounds in Chinese herbs. Four common used herbs were obtained, which contain the active compounds and can act on multiple targets, and were expected to have multiple activity of anti-AD disease.

scholarly journals Evaluation of QSAR Equations for Virtual Screening

2020 ◽  
Vol 21 (21) ◽  
pp. 7828
Author(s):  
Jacob Spiegel ◽  
Hanoch Senderowitz
Keyword(s):  
Virtual Screening ◽  
Quantitative Structure Activity Relationship ◽  
Screening Tests ◽  
Support Vector ◽  
Small Scale ◽  
Active Compounds ◽  
Qsar Models ◽  
Continuous Responses ◽  
Test Sets ◽  
Binary Classifiers

Quantitative Structure Activity Relationship (QSAR) models can inform on the correlation between activities and structure-based molecular descriptors. This information is important for the understanding of the factors that govern molecular properties and for designing new compounds with favorable properties. Due to the large number of calculate-able descriptors and consequently, the much larger number of descriptors combinations, the derivation of QSAR models could be treated as an optimization problem. For continuous responses, metrics which are typically being optimized in this process are related to model performances on the training set, for example, R2 and QCV2. Similar metrics, calculated on an external set of data (e.g., QF1/F2/F32), are used to evaluate the performances of the final models. A common theme of these metrics is that they are context -” ignorant”. In this work we propose that QSAR models should be evaluated based on their intended usage. More specifically, we argue that QSAR models developed for Virtual Screening (VS) should be derived and evaluated using a virtual screening-aware metric, e.g., an enrichment-based metric. To demonstrate this point, we have developed 21 Multiple Linear Regression (MLR) models for seven targets (three models per target), evaluated them first on validation sets and subsequently tested their performances on two additional test sets constructed to mimic small-scale virtual screening campaigns. As expected, we found no correlation between model performances evaluated by “classical” metrics, e.g., R2 and QF1/F2/F32 and the number of active compounds picked by the models from within a pool of random compounds. In particular, in some cases models with favorable R2 and/or QF1/F2/F32 values were unable to pick a single active compound from within the pool whereas in other cases, models with poor R2 and/or QF1/F2/F32 values performed well in the context of virtual screening. We also found no significant correlation between the number of active compounds correctly identified by the models in the training, validation and test sets. Next, we have developed a new algorithm for the derivation of MLR models by optimizing an enrichment-based metric and tested its performances on the same datasets. We found that the best models derived in this manner showed, in most cases, much more consistent results across the training, validation and test sets and outperformed the corresponding MLR models in most virtual screening tests. Finally, we demonstrated that when tested as binary classifiers, models derived for the same targets by the new algorithm outperformed Random Forest (RF) and Support Vector Machine (SVM)-based models across training/validation/test sets, in most cases. We attribute the better performances of the Enrichment Optimizer Algorithm (EOA) models in VS to better handling of inactive random compounds. Optimizing an enrichment-based metric is therefore a promising strategy for the derivation of QSAR models for classification and virtual screening.

scholarly journals D3Similarity: A Ligand-Based Approach for Predicting Drug Targets and for Virtual Screening of Active Compounds Against COVID-19

2020 ◽  
Author(s):  
Zhengdan Zhu ◽  
Xiaoyu Wang ◽  
Yanqing Yang ◽  
Xinben Zhang ◽  
Kaijie Mu ◽  
...  
Keyword(s):  
Protein Structure ◽  
Virtual Screening ◽  
Drug Targets ◽  
False Negative ◽  
Three Dimensional ◽  
Target Prediction ◽  
Molecular Structures ◽  
Bioactive Molecules ◽  
Active Compounds ◽  
Target Proteins

<p>Discovering efficient drugs and identifying target proteins are still an unmet but urgent need for curing COVID-19. Protein structure based docking is a widely applied approach for discovering active compounds against drug targets and for predicting potential targets of active compounds. However, this approach has its inherent deficiency caused by, e.g., various different conformations with largely varied binding pockets adopted by proteins, or the lack of true target proteins in the database. This deficiency may result in false negative results. As a complementary approach to the protein structure based platform for COVID-19, termed as D3Docking in our recent work, we developed the ligand-based method, named D3Similarity, which is based on the molecular similarity evaluation between the submitted molecule(s) and those in an active compound database. The database is constituted by all the reported bioactive molecules against the coronaviruses SARS, MERS and SARS-CoV-2, some of which have target or mechanism information but some don’t. Based on the two-dimensional and three-dimensional similarity evaluation of molecular structures, virtual screening and target prediction could be performed according to similarity ranking results. With two examples, we demonstrated the reliability and efficiency of D3Similarity for drug discovery and target prediction against COVID-19. D3Similarity is available free of charge at <a href="https://www.d3pharma.com/D3Targets-2019-nCoV/D3Similarity/index.php">https://www.d3pharma.com/D3Targets-2019-nCoV/D3Similarity/index.php</a>.</p>

scholarly journals Prospective Assessment of Virtual Screening Heuristics Derived Using a Novel Fusion Score

2017 ◽  
Vol 22 (8) ◽  
pp. 995-1006 ◽  
Author(s):  
Dante A. Pertusi ◽  
Gregory O’Donnell ◽  
Michelle F. Homsher ◽  
Kelli Solly ◽  
Amita Patel ◽  
...  
Keyword(s):  
Machine Learning ◽  
Virtual Screening ◽  
High Throughput Screening ◽  
Screening Methods ◽  
Active Compounds ◽  
Active Chemical ◽  
Prospective Assessment ◽  
Novel Method ◽  
Early Drug ◽  
Chemical Matter

High-throughput screening (HTS) is a widespread method in early drug discovery for identifying promising chemical matter that modulates a target or phenotype of interest. Because HTS campaigns involve screening millions of compounds, it is often desirable to initiate screening with a subset of the full collection. Subsequently, virtual screening methods prioritize likely active compounds in the remaining collection in an iterative process. With this approach, orthogonal virtual screening methods are often applied, necessitating the prioritization of hits from different approaches. Here, we introduce a novel method of fusing these prioritizations and benchmark it prospectively on 17 screening campaigns using virtual screening methods in three descriptor spaces. We found that the fusion approach retrieves 15% to 65% more active chemical series than any single machine-learning method and that appropriately weighting contributions of similarity and machine-learning scoring techniques can increase enrichment by 1% to 19%. We also use fusion scoring to evaluate the tradeoff between screening more chemical matter initially in lieu of replicate samples to prevent false-positives and find that the former option leads to the retrieval of more active chemical series. These results represent guidelines that can increase the rate of identification of promising active compounds in future iterative screens.

scholarly journals A multiple classifier system identifies novel cannabinoid CB2 receptor ligands

2019 ◽  
Vol 11 (1) ◽  
Author(s):  
David Ruano-Ordás ◽  
Lindsey Burggraaff ◽  
Rongfang Liu ◽  
Cas van der Horst ◽  
Laura H. Heitman ◽  
...  
Keyword(s):  
Virtual Screening ◽  
High Performance ◽  
New Drugs ◽  
Multiple Classifier Systems ◽  
Classifier Systems ◽  
Active Compounds ◽  
Hit Rate ◽  
Highly Active ◽  
Multiple Classifier

Abstract Drugs have become an essential part of our lives due to their ability to improve people’s health and quality of life. However, for many diseases, approved drugs are not yet available or existing drugs have undesirable side effects, making the pharmaceutical industry strive to discover new drugs and active compounds. The development of drugs is an expensive process, which typically starts with the detection of candidate molecules (screening) after a protein target has been identified. To this end, the use of high-performance screening techniques has become a critical issue in order to palliate the high costs. Therefore, the popularity of computer-based screening (often called virtual screening or in silico screening) has rapidly increased during the last decade. A wide variety of Machine Learning (ML) techniques has been used in conjunction with chemical structure and physicochemical properties for screening purposes including (i) simple classifiers, (ii) ensemble methods, and more recently (iii) Multiple Classifier Systems (MCS). Here, we apply an MCS for virtual screening (D2-MCS) using circular fingerprints. We applied our technique to a dataset of cannabinoid CB2 ligands obtained from the ChEMBL database. The HTS collection of Enamine (1,834,362 compounds), was virtually screened to identify 48,232 potential active molecules using D2-MCS. Identified molecules were ranked to select 21 promising novel compounds for in vitro evaluation. Experimental validation confirmed six highly active hits (> 50% displacement at 10 µM and subsequent Ki determination) and an additional five medium active hits (> 25% displacement at 10 µM). Hence, D2-MCS provided a hit rate of 29% for highly active compounds and an overall hit rate of 52%.

Analysis of structure-based virtual screening studies and characterization of identified active compounds

2012 ◽  
Vol 4 (5) ◽  
pp. 603-613 ◽  
Author(s):  
Peter Ripphausen ◽  
Dagmar Stumpfe ◽  
Jürgen Bajorath
Keyword(s):  
Virtual Screening ◽  
Active Compounds
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