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An Almost Optimal Algorithm for Generalized Threshold Group Testing with Inhibitors

Hong-Bin Chen, Annalisa De Bonis
2011 Journal of Computational Biology  
We derive lower bounds on the minimum number of tests required to determine the defective items under this model and present an algorithm that uses an almost optimal number of tests.  ...  In group testing with inhibitors (GTI), identifying the defective items is more difficult due to the presence of elements called inhibitors that interfere with the queries so that the answer to a query  ...  . & AN ALMOST OPTIMAL ALGORITHM The algorithm proceeds along two different paths, namely algorithm A or algorithm B, according to whether the given set S tests positive or negative, respectively.  ... 
doi:10.1089/cmb.2010.0030 pmid:21210744 fatcat:tbpmhodgkfhvdkqfhc324eby54

An Application of Fit Quality to Screen MDM2/p53 Protein-Protein Interaction Inhibitors

Xin Xue, Gang Bao, Hai-Qing Zhang, Ning-Yi Zhao, Yuan Sun, Yue Zhang, Xiao-Long Wang
2018 Molecules  
The enrichment factor (EF) for screening was calculated based on a decoy set to define the screening threshold.  ...  After the second round of VS, six compounds with the FQ > 0.8 were picked out for assessing their antitumor activity.  ...  Hence, identifying a suitable PPII for further optimization to a lead in the early stages of drug discovery is an important step.  ... 
doi:10.3390/molecules23123174 fatcat:i5r3ogj4k5amrh3mtvxdu7x7kq

VoteDock: Consensus docking method for prediction of protein-ligand interactions

Dariusz Plewczynski, Michał Łażniewski, Marcin Von Grotthuss, Leszek Rychlewski, Krzysztof Ginalski
2010 Journal of Computational Chemistry  
The scaling parameters together with the empirical functions are trained on the selected dataset of complexes with known binding affinity for which scaling factors for each energy term can be optimized  ...  We select a set of representative conformations from each docking algorithm to efficiently inspect different guided search algorithms for correct conformation of a protein-ligand complex.  ...  Iwona Wawer for fruitful discussions and Kamil Steczkiewicz for his help during the preparation of the article.  ... 
doi:10.1002/jcc.21642 pmid:20812324 pmcid:PMC4510457 fatcat:kew2uusgdzchndmdbntkn3xtmq

Semi-automated workflow for molecular pair analysis and QSAR-assisted transformation space expansion

Zi-Yi Yang, Li Fu, Ai-Ping Lu, Shao Liu, Ting-Jun Hou, Dong-Sheng Cao
2021 Journal of Cheminformatics  
Especially, the integration of MMPA with QSAR modeling can further strengthen the utility of MMPA in molecular optimization navigation.  ...  Matched molecular pair analysis (MMPA), a promising tool to efficiently extract and summarize the relationship between structural transformation and property change, is suitable for local structural optimization  ...  Table 4 reveals that most prediction models performed well for both the training and test sets, with an average accuracy of 0.858 and an average AUC of 0.925 for the fivefold cross-validation, and an  ... 
doi:10.1186/s13321-021-00564-6 pmid:34774096 pmcid:PMC8590336 fatcat:p2m2jyihjrcsjghapbondjcgpm

Partial Molecular Alignment via Local Structure Analysis

Daniel D. Robinson, Paul D. Lyne, W. Graham Richards
2000 Journal of chemical information and computer sciences  
Finally, in the most challenging test, a large protein-based inhibitor is matched with a smallmolecule mimic.  ...  The flexibility of the method is illustrated with a series of test sets.  ...  ACKNOWLEDGMENT D.D.R. is supported by an EPSRC CASE studentship held in conjunction with Oxford Molecular Group PLC.  ... 
doi:10.1021/ci990272p pmid:10761157 fatcat:mp4lelfblveknhtxxq2j436hn4

Increased detection of structural templates using alignments of designed sequences

Stefan M. Larson, Amit Garg, John R. Desjarlais, Vijay S. Pande
2003 Proteins: Structure, Function, and Bioinformatics  
A large-scale massively parallelized application of an all-atom protein design algorithm, including a simple model of peptide backbone flexibility, has allowed us to generate 500 diverse, non-native, high-quality  ...  sequences for each of 264 protein structures in our test set.  ...  The authors thank the members of the Pande Group for helpful discussions. S.M.L. is a James Clark Fellow of the SGF program. A.G. thanks the Bing Fellowship Committee for funding his research.  ... 
doi:10.1002/prot.10346 pmid:12696050 fatcat:ewqtcppoingt7dxixiusgrbyga

Computed Axial Lithography (CAL): Toward Single Step 3D Printing of Arbitrary Geometries [article]

Brett Kelly, Indrasen Bhattacharya, Maxim Shusteff, Robert M. Panas, Hayden K. Taylor, Christopher M. Spadaccini
2017 arXiv   pre-print
The approach, termed Computed Axial Lithgography (CAL), is based on tomographic reconstruction, with mathematical optimization to generate a set of projections to optically define an arbitrary dose distribution  ...  It could also vastly increase print speed for 3D parts. In this work, we develop the principles for an approach for single exposure 3D printing of arbitrarily defined geometries.  ...  We would like to thank both the Design for Nanomanufacturing group at the University of California, Berkeley and the Center for Engineered Materials and Manufacturing at Lawrence Livermore National Laboratory  ... 
arXiv:1705.05893v1 fatcat:hlkvcubiubfzdcccpiauvkwpji

Machine Learning Models to Predict Inhibition of the Bile Salt Export Pump [article]

Kevin S. McLoughlin, Claire G. Jeong, Thomas D. Sweitzer, Amanda J. Minnich, Margaret J. Tse, Brian J. Bennion, Jonathan E. Allen, Stacie Calad-Thomson, Thomas S. Rush, James M. Brase
2020 arXiv   pre-print
Our best performing classification model was a neural network with ROC AUC = 0.88 on our internal test dataset and 0.89 on an independent external compound set.  ...  In the course of model development, we assessed a variety of schemes for chemical featurization, dataset partitioning and class labeling, and identified those producing models that generalized best to  ...  The optimal threshold was 50 µM, which yielded a kappa value of 0.62.  ... 
arXiv:2002.12541v1 fatcat:zjg2tasx5bd5xpiyuxw4zunnfu

ADMET evaluation in drug discovery. 20. Prediction of breast cancer resistance protein inhibition through machine learning

Dejun Jiang, Tailong Lei, Zhe Wang, Chao Shen, Dongsheng Cao, Tingjun Hou
2020 Journal of Cheminformatics  
The optimal feature subset was determined by a wrapper feature selection method named rfSA (simulated annealing algorithm coupled with random forest), and the classification models were established by  ...  the test set).  ...  Ensemble learning Ensemble learning is a group of popular algorithms that can produce a strong learner in the form of an ensemble of weak learners.  ... 
doi:10.1186/s13321-020-00421-y pmid:33430990 fatcat:yqr5fg5ixvgxni5rymyvyoicnu

Development of Predictive Models for Identifying Potential S100A9 Inhibitors Based on Machine Learning Methods

Jihyeun Lee, Surendra Kumar, Sang-Yoon Lee, Sung Jean Park, Mi-hyun Kim
2019 Frontiers in Chemistry  
Notably, optimal feature sets were obtained after the reduction of 2,798 features into dozens of features with the chopping of fingerprint bits.  ...  The models were optimized through feature selectors as well as classifiers to produce the top eight random forest models with robust predictability and high cost-effectiveness.  ...  A general observation is that the merit improved with an increase in the activity threshold.  ... 
doi:10.3389/fchem.2019.00779 pmid:31824919 pmcid:PMC6886474 fatcat:kv6eo6p7ffeophupcny4atyqxq

Homologous control of protein signaling networks

D. Napoletani, M. Signore, T. Sauer, L. Liotta, E. Petricoin
2011 Journal of Theoretical Biology  
The most significant consequence of this observed homology is that a nearly optimal combinatorial dosage of kinase inhibitors can be inferred, for many nodes, from the reconstructed network, a result potentially  ...  We show this result using a large in-silico model of the epidermal growth factor receptor (EGF-R) driven signaling cascade to generate the data used in the reconstruction algorithm.  ...  Struppa for many useful discussions and the anonymous referees for their constructive remarks.  ... 
doi:10.1016/j.jtbi.2011.03.020 pmid:21439301 fatcat:zpuzd44fsfawxhijrn4u7xwwhy

Weka Machine Learning for Predicting the Phospholipidosis Inducing Potential

Ovidiu Ivanciuc
2008 Current Topics in Medicinal Chemistry  
Here we present structure-activity relationships (SAR) computed with a wide variety of machine learning algorithms trained to identify drugs that have phospholipidosis inducing potential.  ...  All SAR models are developed with the machine learning software Weka, and include both classical algorithms, such as k-nearest neighbors and decision trees, as well as recently introduced methods, such  ...  After training the system for G generations, the MAP group of antigens represents the solution of the CLONALG classifier.  ... 
doi:10.2174/156802608786786589 pmid:19075775 fatcat:6ytu27uwrzbg3nruc7o6bnoxra

An analysis of non-cultivable bacteria using WEKA

Preeti Mangesh Patil, Department of Bioinformatics, Bharati Vidyapeeth Deemed University
2020 Bioinformation  
Therefore, we report an analysis of metagenome data generated using T-RFLP followed by using the SMO (Sequential minimal optimization) algorithm in WEKA to identify the total amount of cultured and uncultured  ...  The study of metagenomics from high throughput sequencing data processed through Waikato Environment for Knowledge Analysis (WEKA) is gaining momentum in recent years.  ...  Fast SVM training speed with SMO algorithm is an important goal for practitioners.  ... 
doi:10.6026/97320630016620 fatcat:zfswjzqqrrdfve5lpmaeptjtdi

A Comparative Study of Different Optimization Algorithms for Molecular Docking

Alexander P. Afanasiev, Igor Oferkin, Mikhail Posypkin, Anton Rubtsov, Alexey V. Sulimov, Vladimir B. Sulimov
2011 International Workshop on Science Gateways  
We present experimental results for different optimization algorithms and draw conclusions about their efficiency.  ...  The core part of this modeling is a resolution of a global unconstrained optimization problem.  ...  ACKNOWLEDGEMENTS This study was supported in part by grant 10-07-00595 from the Russian Foundation for Basic Research and the DEGISCO FP/7 European (contract no. 261561).  ... 
dblp:conf/iwsg/AfanasievOPRSS11 fatcat:d3rqd3pygbf4dmbp5tzdotwf5a

Impact of between-tissue differences on pan-cancer predictions of drug sensitivity

John P. Lloyd, Matthew B. Soellner, Sofia D. Merajver, Jun Z. Li, Ewa Szczurek
2021 PLoS Computational Biology  
Here, we built a series of pan-cancer models using two datasets containing 346 and 504 cell lines, each with MEK inhibitor (MEKi) response and mRNA expression, point mutation, and copy number variation  ...  response is almost entirely due to the sample size advantage.  ...  C Optimal model parameters for regularized and logistic regression algorithms. Table D. Optimal model parameters for random forest algorithms.  ... 
doi:10.1371/journal.pcbi.1008720 pmid:33630864 fatcat:rxedf4bo7rdgrggkninwsts5l4
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