Bioinformatics and Drug Discovery

Larson, Richard S.

doi:10.1007/978-1-61779-965-5

Methods in Molecular Biology

2012

DOI: 10.1007/978-1-61779-965-5

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Bioinformatics and Drug Discovery

Richard S. Larson¹

Abstract: Bioinformatic analysis can not only accelerate drug target identification and drug candidate screening and refinement, but also facilitate characterization of side effects and predict drug resistance. High-throughput data such as genomic, epigenetic, genome architecture, cistromic, transcriptomic, proteomic, and ribosome profiling data have all made significant contribution to mechanismbased drug discovery and drug repurposing. Accumulation of protein and RNA structures, as well as development of homology mode… Show more

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Cited by 8 publications

References 112 publications

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Computational classification models for predicting the interaction of drugs with P-glycoprotein and breast cancer resistance protein

Erić

Kalinić

Ilic

et al. 2014

SAR and QSAR in Environmental Research

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P-glycoprotein (P-gp/ABCB1) and breast cancer resistance protein (BCRP/ABCG2) are two members of the adenosine triphosphate (ATP) binding cassette (ABC) family of transporters which function as membrane efflux transporters and display considerable substrate promiscuity. Both are known to significantly influence the absorption, distribution and elimination of drugs, mediate drug-drug interactions and contribute to multiple drug resistance (MDR) of cancer cells. Correspondingly, timely characterization of the interaction of novel leads and drug candidates with these two transporters is of great importance. In this study, several computational classification models for prediction of transport and inhibition of P-gp and BCRP, respectively, were developed based on newly compiled and critically evaluated experimental data. Artificial neural network (ANN) and support vector machine (SVM) ensemble based models were explored, as well as knowledge-based approaches to descriptor selection. The average overall classification accuracy of best performing models was 82% for P-gp transport, 88% for BCRP transport, 89% for P-gp inhibition and 87% for BCRP inhibition, determined across an array of different test sets. An analysis of substrate overlap between P-gp and BCRP was also performed. The accuracy, simplicity and interpretability of the proposed models suggest that they could be of significant utility in the drug discovery and development settings.

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Computational classification models for predicting the interaction of drugs with P-glycoprotein and breast cancer resistance protein

Erić

Kalinić

Ilic

et al. 2014

SAR and QSAR in Environmental Research

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In Silico Discovery and Virtual Screening of Multi-Target Inhibitors for Proteins in Mycobacterium tuberculosis

Speck‐Planche¹,

Kleandrova²,

Luan³

et al. 2012

CCHTS

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Mycobacterium tuberculosis (MTB) is the principal pathogen which causes tuberculosis (TB), a disease that remains as one of the most alarming health problems worldwide. An active area for the search of new anti-TB therapies is concerned with the use of computational approaches based on Chemoinformatics and/or Bioinformatics toward the discovery of new and potent anti-TB agents. These approaches consider only small series of structurally related compounds and the studies are generally realized for only one target like a protein. This fact constitutes an important limitation. The present work is an effort to overcome this problem. We introduce here the first chemo-bioinformatic approach by developing a multi-target (mt) QSAR discriminant model, for the in silico design and virtual screening of anti-TB agents against six proteins in MTB. The mt-QSAR model was developed by employing a large and heterogeneous database of compounds and substructural descriptors. The model correctly classified more than 90% of active and inactive compounds in both, training and prediction series. Some fragments were extracted from the molecules and their contributions to anti-TB activity through inhibition of the six proteins, were calculated. Several fragments were identified as responsible for anti-TB activity and new molecular entities were designed from those fragments with positive contributions, being suggested as possible anti-TB agents.

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Computer-Aided Drug Design Methodologies Toward the Design of Anti-Hepatitis C Agents

Speck‐Planche

Cordeiro²

2012

CTMC

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Hepatitis C constitutes an infectious disease that causes severe damages to the liver, and is caused by hepatitis C virus. There is no vaccine against this type of disease and the number of people infected continues to grow worldwide. The anti-viral therapy which is currently used is a mixture of interferon alpha-2a with ribavirin, but approximately half of the patients do not respond to therapy. Therefore, it is necessary to search for new compounds with anti-hepatitis C activity. Computer-aided drug design methodologies have been vital in the discovery of candidates to drugs. This review is dedicated to the role of computer-aided drug design methodologies for the development of new anti-hepatitis C agents. In addition, we introduce a QSAR model based on substructural approaches in order to model the anti-hepatitis C activity in vivo.

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Bioinformatics and Drug Discovery

Cited by 8 publications

References 112 publications

Computational classification models for predicting the interaction of drugs with P-glycoprotein and breast cancer resistance protein

Computational classification models for predicting the interaction of drugs with P-glycoprotein and breast cancer resistance protein

In Silico Discovery and Virtual Screening of Multi-Target Inhibitors for Proteins in Mycobacterium tuberculosis

Computer-Aided Drug Design Methodologies Toward the Design of Anti-Hepatitis C Agents

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