scholarly journals A Multi-Objective Approach for Drug Repurposing in Preeclampsia

Molecules ◽  
2021 ◽  
Vol 26 (4) ◽  
pp. 777
Author(s):  
Eduardo Tejera ◽  
Yunierkis Pérez-Castillo ◽  
Andrea Chamorro ◽  
Alejandro Cabrera-Andrade ◽  
Maria Eugenia Sanchez
Keyword(s):  
Virtual Screening ◽  
Complex Disease ◽  
Performance Metrics ◽  
Antihypertensive Drugs ◽  
Desirability Function ◽  
Drug Repurposing ◽  
Hypertensive Disorder ◽  
Average Accuracy ◽  
Screening Performance ◽  
Virtual Screening Performance

Preeclampsia is a hypertensive disorder that occurs during pregnancy. It is a complex disease with unknown pathogenesis and the leading cause of fetal and maternal mortality during pregnancy. Using all drugs currently under clinical trial for preeclampsia, we extracted all their possible targets from the DrugBank and ChEMBL databases and labeled them as “targets”. The proteins labeled as “off-targets” were extracted in the same way but while taking all antihypertensive drugs which are inhibitors of ACE and/or angiotensin receptor antagonist as query molecules. Classification models were obtained for each of the 55 total proteins (45 targets and 10 off-targets) using the TPOT pipeline optimization tool. The average accuracy of the models in predicting the external dataset for targets and off-targets was 0.830 and 0.850, respectively. The combinations of models maximizing their virtual screening performance were explored by combining the desirability function and genetic algorithms. The virtual screening performance metrics for the best model were: the Boltzmann-Enhanced Discrimination of ROC (BEDROC)α=160.9 = 0.258, the Enrichment Factor (EF)1% = 31.55 and the Area Under the Accumulation Curve (AUAC) = 0.831. The most relevant targets for preeclampsia were: AR, VDR, SLC6A2, NOS3 and CHRM4, while ABCG2, ERBB2, CES1 and REN led to the most relevant off-targets. A virtual screening of the DrugBank database identified estradiol, estriol, vitamins E and D, lynestrenol, mifrepristone, simvastatin, ambroxol, and some antibiotics and antiparasitics as drugs with potential application in the treatment of preeclampsia.

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.

scholarly journals CompScore: Boosting Structure-Based Virtual Screening Performance by Incorporating Docking Scoring Function Components into Consensus Scoring

2019 ◽  
Vol 59 (9) ◽  
pp. 3655-3666 ◽  
Author(s):  
Yunierkis Perez-Castillo ◽  
Stellamaris Sotomayor-Burneo ◽  
Karina Jimenes-Vargas ◽  
Mario Gonzalez-Rodriguez ◽  
Maykel Cruz-Monteagudo ◽  
...  
Keyword(s):  
Virtual Screening ◽  
Scoring Function ◽  
Screening Performance ◽  
Consensus Scoring ◽  
Virtual Screening Performance

Impact of different protonation states on virtual screening performance against cruzain

2021 ◽  
Author(s):  
Viviane Corrêa Santos ◽  
Augusto César Broilo Campos ◽  
Birgit J. Waldner ◽  
Klaus R. Liedl ◽  
Rafaela Salgado Ferreira
Keyword(s):  
Virtual Screening ◽  
Screening Performance ◽  
Virtual Screening Performance

Improved virtual screening performance through docking scoring fusion in the discovery of dual target ligands for Parkinson’s disease

2015 ◽  
Author(s):  
Yunierkis Pérez-Castillo ◽  
Aliuska Morales-Helguera ◽  
M. Natália D. S. Cordeiro ◽  
Eduardo Tejera ◽  
Cesar Paz-y-Miño ◽  
...  
Keyword(s):  
Virtual Screening ◽  
Screening Performance ◽  
Virtual Screening Performance ◽  
Dual Target

scholarly journals Virtual Screening with Gnina 1.0

2021 ◽  
Author(s):  
Jocelyn Sunseri ◽  
David Koes
Keyword(s):  
Virtual Screening ◽  
Scoring Function ◽  
Compound Library ◽  
Autodock Vina ◽  
Convolutional Networks ◽  
Development Costs ◽  
Screening Performance ◽  
Computationally Intensive ◽  
Speed And Accuracy ◽  
Virtual Screening Performance

Virtual screening - predicting which compounds within a specified compound library bind to a target molecule, typically a protein - is a fundamental task in the field of drug discovery. Doing virtual screening well provides tangible practical benefits, including reduced drug development costs, faster time to therapeutic viability, and fewer unforeseen side effects. As with most applied computational tasks, the algorithms currently used to perform virtual screening feature inherent tradeoffs between speed and accuracy. Furthermore, even theoretically rigorous, computationally intensive methods may fail to account for important effects relevant to whether a given compound will ultimately be usable as a drug. Here we investigate the virtual screening performance of the recently released Gnina molecular docking software, which uses deep convolutional networks to score protein-ligand structures. We find, on average, that Gnina outperforms conventional empirical scoring. The default scoring in Gnina outperforms the empirical AutoDock Vina scoring function on 89 of the 117 targets of the DUD-E and LIT-PCBA virtual screening benchmarks with a median 1% early enrichment factor that is more than twice that of Vina. However, we also find that issues of bias linger in these sets, even when not used directly to train models, and this bias obfuscates to what extent machine learning models are achieving their performance through a sophisticated interpretation of molecular interactions versus fitting to non-informative simplistic property distributions.

scholarly journals The effect of structural redundancy in validation sets on virtual screening performance

2009 ◽  
Vol 23 (9) ◽  
pp. 471-478 ◽  
Author(s):  
Robert D. Clark ◽  
Jennifer K. Shepphird ◽  
John Holliday
Keyword(s):  
Virtual Screening ◽  
Structural Redundancy ◽  
Screening Performance ◽  
Virtual Screening Performance
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