scholarly journals Efficient Hit-Finding Approaches for Histone Methyltransferases

2011 ◽  
Vol 17 (1) ◽  
pp. 85-98 ◽  
Author(s):  
Thomas Ahrens ◽  
Andreas Bergner ◽  
David Sheppard ◽  
Doris Hafenbradl
Keyword(s):  
Virtual Screening ◽  
Structural Information ◽  
Screening Methods ◽  
Success Rates ◽  
Protein Targets ◽  
Histone Methyltransferases ◽  
Primary Screening ◽  
Compound Screening ◽  
Individual Success ◽  
Selection Of

For many novel epigenetics targets the chemical ligand space and structural information were limited until recently and are still largely unknown for some targets. Hit-finding campaigns are therefore dependent on large and chemically diverse libraries. In the specific case of the histone methyltransferase G9a, the authors have been able to apply an efficient process of intelligent selection of compounds for primary screening, rather than screening the full diverse deck of 900 000 compounds to identify hit compounds. A number of different virtual screening methods have been applied for the compound selection, and the results have been analyzed in the context of their individual success rates. For the primary screening of 2112 compounds, a FlashPlate assay format and full-length histone H3.1 substrate were employed. Validation of hit compounds was performed using the orthogonal fluorescence lifetime technology. Rated by purity and IC50 value, 18 compounds (0.9% of compound screening deck) were finally considered validated primary G9a hits. The hit-finding approach has led to novel chemotypes being identified, which can facilitate hit-to-lead projects. This study demonstrates the power of virtual screening technologies for novel, therapeutically relevant epigenetics protein targets.

Rapid Elaboration of Fragments into Leads Applied to Bromodomain-3 Extra Terminal Domain

2020 ◽  
Author(s):  
Luke Adams ◽  
Lorna E. Wilkinson-White ◽  
Menachem J. Gunzburg ◽  
Stephen J. Headey ◽  
Martin J. Scanlon ◽  
...  
Keyword(s):  
Binding Site ◽  
Systematic Approach ◽  
Structural Information ◽  
Peptide Binding ◽  
Chemical Diversity ◽  
Chemical Probes ◽  
Protein Targets ◽  
Structure Activity ◽  
Peptide Binding Site ◽  
Selection Of

The development of low-affinity fragment hits into higher affinity leads is a major hurdle in fragment-based drug design. Here we demonstrate an approach for the Rapid Elaboration of Fragments into Leads (REFiL) applying an integrated workflow that provides a systematic approach to generate higher-affinity binders without the need for structural information. The workflow involves the selection of commercial analogues of fragment hits to generate preliminary structure-activity relationships. This is followed by parallel microscale chemistry using chemoinformatically designed reagent libraries to rapidly explore chemical diversity. Upon completion of a fragment screen against Bromodomain-3 extra terminal (BRD3-ET) domain we applied the REFiL workflow, which allowed us to develop a series of tetrahydrocarbazole ligands that bind to the peptide binding site of BRD3-ET. With REFiL we were able to rapidly improve binding affinity >30-fold. The REFiL workflow can be applied readily to a broad range of protein targets without the need of a structure, allowing the efficient evolution of low-affinity fragments into higher affinity leads and chemical probes.<br>

Rapid Elaboration of Fragments into Leads Applied to Bromodomain-3 Extra Terminal Domain

2020 ◽  
Author(s):  
Luke Adams ◽  
Lorna E. Wilkinson-White ◽  
Menachem J. Gunzburg ◽  
Stephen J. Headey ◽  
Martin J. Scanlon ◽  
...  
Keyword(s):  
Binding Site ◽  
Systematic Approach ◽  
Structural Information ◽  
Peptide Binding ◽  
Chemical Diversity ◽  
Chemical Probes ◽  
Protein Targets ◽  
Structure Activity ◽  
Peptide Binding Site ◽  
Selection Of

The development of low-affinity fragment hits into higher affinity leads is a major hurdle in fragment-based drug design. Here we demonstrate an approach for the Rapid Elaboration of Fragments into Leads (REFiL) applying an integrated workflow that provides a systematic approach to generate higher-affinity binders without the need for structural information. The workflow involves the selection of commercial analogues of fragment hits to generate preliminary structure-activity relationships. This is followed by parallel microscale chemistry using chemoinformatically designed reagent libraries to rapidly explore chemical diversity. Upon completion of a fragment screen against Bromodomain-3 extra terminal (BRD3-ET) domain we applied the REFiL workflow, which allowed us to develop a series of tetrahydrocarbazole ligands that bind to the peptide binding site of BRD3-ET. With REFiL we were able to rapidly improve binding affinity >30-fold. The REFiL workflow can be applied readily to a broad range of protein targets without the need of a structure, allowing the efficient evolution of low-affinity fragments into higher affinity leads and chemical probes.<br>

Integration of Lead Discovery Tactics and the Evolution of the Lead Discovery Toolbox

2018 ◽  
Vol 23 (9) ◽  
pp. 881-897 ◽  
Author(s):  
Melanie Leveridge ◽  
Chun-Wa Chung ◽  
Jeffrey W. Gross ◽  
Christopher B. Phelps ◽  
Darren Green
Keyword(s):  
Protein Kinase ◽  
Virtual Screening ◽  
Phenotypic Screening ◽  
Library Screening ◽  
Success Rates ◽  
Domain Family ◽  
Lead Discovery ◽  
Fragment Screening ◽  
Screening Strategies ◽  
Selection Of

There has been much debate around the success rates of various screening strategies to identify starting points for drug discovery. Although high-throughput target-based and phenotypic screening has been the focus of this debate, techniques such as fragment screening, virtual screening, and DNA-encoded library screening are also increasingly reported as a source of new chemical equity. Here, we provide examples in which integration of more than one screening approach has improved the campaign outcome and discuss how strengths and weaknesses of various methods can be used to build a complementary toolbox of approaches, giving researchers the greatest probability of successfully identifying leads. Among others, we highlight case studies for receptor-interacting serine/threonine-protein kinase 1 and the bromo- and extra-terminal domain family of bromodomains. In each example, the unique insight or chemistries individual approaches provided are described, emphasizing the synergy of information obtained from the various tactics employed and the particular question each tactic was employed to answer. We conclude with a short prospective discussing how screening strategies are evolving, what this screening toolbox might look like in the future, how to maximize success through integration of multiple tactics, and scenarios that drive selection of one combination of tactics over another.

scholarly journals Principled approach to the selection of the embedding dimension of networks

2021 ◽  
Vol 12 (1) ◽  
Author(s):  
Weiwei Gu ◽  
Aditya Tandon ◽  
Yong-Yeol Ahn ◽  
Filippo Radicchi
Keyword(s):  
Real World ◽  
Structural Information ◽  
General Purpose ◽  
Embedding Dimension ◽  
Network Embedding ◽  
Machine Learning Technique ◽  
Learning Technique ◽  
Low Dimensional ◽  
Large Corpus ◽  
Selection Of

AbstractNetwork embedding is a general-purpose machine learning technique that encodes network structure in vector spaces with tunable dimension. Choosing an appropriate embedding dimension – small enough to be efficient and large enough to be effective – is challenging but necessary to generate embeddings applicable to a multitude of tasks. Existing strategies for the selection of the embedding dimension rely on performance maximization in downstream tasks. Here, we propose a principled method such that all structural information of a network is parsimoniously encoded. The method is validated on various embedding algorithms and a large corpus of real-world networks. The embedding dimension selected by our method in real-world networks suggest that efficient encoding in low-dimensional spaces is usually possible.

scholarly journals Practical Model Selection for Prospective Virtual Screening

2018 ◽  
Author(s):  
Shengchao Liu ◽  
Moayad Alnammi ◽  
Spencer S. Ericksen ◽  
Andrew F. Voter ◽  
Gene E. Ananiev ◽  
...  
Keyword(s):  
Random Forest ◽  
Virtual Screening ◽  
Protein Interactions ◽  
High Throughput Screening ◽  
Screening Methods ◽  
Protein Protein Interactions ◽  
Screening Algorithm ◽  
Screening Performance ◽  
Wide Range ◽  
Public Datasets

AbstractVirtual (computational) high-throughput screening provides a strategy for prioritizing compounds for experimental screens, but the choice of virtual screening algorithm depends on the dataset and evaluation strategy. We consider a wide range of ligand-based machine learning and docking-based approaches for virtual screening on two protein-protein interactions, PriA-SSB and RMI-FANCM, and present a strategy for choosing which algorithm is best for prospective compound prioritization. Our workflow identifies a random forest as the best algorithm for these targets over more sophisticated neural network-based models. The top 250 predictions from our selected random forest recover 37 of the 54 active compounds from a library of 22,434 new molecules assayed on PriA-SSB. We show that virtual screening methods that perform well in public datasets and synthetic benchmarks, like multi-task neural networks, may not always translate to prospective screening performance on a specific assay of interest.

scholarly journals Problems and Complications During the Treatment of Infertility in Women with Polycystic Ovary Syndrome

2016 ◽  
Vol 22 (2) ◽  
Author(s):  
Müberra Namlı Kalem ◽  
Ziya Kalem ◽  
Timur Gürgan
Keyword(s):  
Polycystic Ovary Syndrome ◽  
Reproductive Medicine ◽  
Chromosomal Abnormalities ◽  
Polycystic Ovary ◽  
Wide Spectrum ◽  
Treatment Protocol ◽  
Success Rates ◽  
Ovary Syndrome ◽  
Detailed Evaluation ◽  
Selection Of

<p>Polycystic ovary syndrome (PCOS) is the most frequent endocrine disorder existing in women in their reproductive years and it is one of the most evaluated and discussed subjects of reproductive medicine with regard to its diagnosis and treatment.<br />Patients with PCOS constitute the most difficult population in the management of infertility. The factors that increase the success rates in the treatment of PCOS infertility are: pretreatment changes in life style, dietetic and psychological support, a detailed evaluation of the couple and the appropriate selection of the treatment protocol, a wide-spectrum approach to maintaining ovarian and endometrial synchronization in the management of the cycle, and well-developed laboratory conditions to support embryonic quality. However, even if these conditions are provided, OHSS, cancellations of the cycle, poor oocyte, and embryo qualities, unsuccessful fertilization and implantation, chromosomal abnormalities and early losses may still be experienced.</p>

scholarly journals Generating Property-Matched Decoy Molecules Using Deep Learning

2020 ◽  
Author(s):  
Fergus Imrie ◽  
Anthony R. Bradley ◽  
Charlotte M. Deane
Keyword(s):  
Deep Learning ◽  
Virtual Screening ◽  
Method Development ◽  
Screening Method ◽  
Screening Methods ◽  
Additional Risk ◽  
Link Type ◽  
Screening Performance ◽  
And Training ◽  
Virtual Screening Performance

An essential step in the development of virtual screening methods is the use of established sets of actives and decoys for benchmarking and training. However, the decoy molecules in commonly used sets are biased meaning that methods often exploit these biases to separate actives and decoys, rather than learning how to perform molecular recognition. This fundamental issue prevents generalisation and hinders virtual screening method development. We have developed a deep learning method (DeepCoy) that generates decoys to a user’s preferred specification in order to remove such biases or construct sets with a defined bias. We validated DeepCoy using two established benchmarks, DUD-E and DEKOIS 2.0. For all DUD-E targets and 80 of the 81 DEKOIS 2.0 targets, our generated decoy molecules more closely matched the active molecules’ physicochemical properties while introducing no discernible additional risk of false negatives. The DeepCoy decoys improved the Deviation from Optimal Embedding (DOE) score by an average of 81% and 66%, respectively, decreasing from 0.163 to 0.032 for DUD-E and from 0.109 to 0.038 for DEKOIS 2.0. Further, the generated decoys are harder to distinguish than the original decoy molecules via docking with Autodock Vina, with virtual screening performance falling from an AUC ROC of 0.71 to 0.63. The code is available at https://github.com/oxpig/DeepCoy. Generated molecules can be downloaded from http://opig.stats.ox.ac.uk/resources.

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