1
|
Machine Learning Applied to the Modeling of Pharmacological and ADMET Endpoints. Methods Mol Biol 2021. [PMID: 34731464 DOI: 10.1007/978-1-0716-1787-8_2] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/26/2023]
Abstract
The well-known concept of quantitative structure-activity relationships (QSAR) has been gaining significant interest in the recent years. Data, descriptors, and algorithms are the main pillars to build useful models that support more efficient drug discovery processes with in silico methods. Significant advances in all three areas are the reason for the regained interest in these models. In this book chapter we review various machine learning (ML) approaches that make use of measured in vitro/in vivo data of many compounds. We put these in context with other digital drug discovery methods and present some application examples.
Collapse
|
2
|
Abstract
Beyond finding inhibitors that show high binding affinity to the respective target, there is the challenge of optimizing their properties with respect to metabolic and toxicological issues, as well as further off-target effects. To reduce the experimental effort of synthesizing and testing actual substances in corresponding assays, virtual screening has become an indispensable toolbox in preclinical development. The scope of application covers the prediction of molecular properties including solubility, metabolic liability and binding to antitargets, such as the hERG channel. Furthermore, prediction of binding sites and drugable targets are emerging aspects of virtual screening. Issues involved with the currently applied computational models including machine learning algorithms are outlined, such as limitations to the accuracy of prediction and overfitting.
Collapse
|
3
|
Schyman P, Liu R, Wallqvist A. General Purpose 2D and 3D Similarity Approach to Identify hERG Blockers. J Chem Inf Model 2016; 56:213-22. [DOI: 10.1021/acs.jcim.5b00616] [Citation(s) in RCA: 14] [Impact Index Per Article: 1.8] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/05/2023]
Affiliation(s)
- Patric Schyman
- DoD Biotechnology
High Performance
Computing Software Applications Institute, Telemedicine and Advanced
Technology Research Center, U.S. Army Medical Research and Materiel Command, 2405 Whittier Drive, Frederick, Maryland 21702, United States
| | - Ruifeng Liu
- DoD Biotechnology
High Performance
Computing Software Applications Institute, Telemedicine and Advanced
Technology Research Center, U.S. Army Medical Research and Materiel Command, 2405 Whittier Drive, Frederick, Maryland 21702, United States
| | - Anders Wallqvist
- DoD Biotechnology
High Performance
Computing Software Applications Institute, Telemedicine and Advanced
Technology Research Center, U.S. Army Medical Research and Materiel Command, 2405 Whittier Drive, Frederick, Maryland 21702, United States
| |
Collapse
|
4
|
Fraczkiewicz R, Lobell M, Göller AH, Krenz U, Schoenneis R, Clark RD, Hillisch A. Best of Both Worlds: Combining Pharma Data and State of the Art Modeling Technology To Improve in Silico pKa Prediction. J Chem Inf Model 2014; 55:389-97. [DOI: 10.1021/ci500585w] [Citation(s) in RCA: 71] [Impact Index Per Article: 7.1] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 02/01/2023]
Affiliation(s)
- Robert Fraczkiewicz
- Simulations Plus, Inc. 42505 10th
Street West, Lancaster, California 93534, United States
| | - Mario Lobell
- Global
Drug Discovery, Bayer Pharma AG, Wuppertal, Germany
| | | | - Ursula Krenz
- Global
Drug Discovery, Bayer Pharma AG, Wuppertal, Germany
| | | | - Robert D. Clark
- Simulations Plus, Inc. 42505 10th
Street West, Lancaster, California 93534, United States
| | | |
Collapse
|
5
|
Kramer C, Fuchs JE, Whitebread S, Gedeck P, Liedl KR. Matched Molecular Pair Analysis: Significance and the Impact of Experimental Uncertainty. J Med Chem 2014; 57:3786-802. [DOI: 10.1021/jm500317a] [Citation(s) in RCA: 46] [Impact Index Per Article: 4.6] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/02/2023]
Affiliation(s)
- Christian Kramer
- Department
of Theoretical Chemistry, Faculty for Chemistry and Pharmacy, Center
for Molecular Biosciences Innsbruck (CMBI), Leopold-Franzens University Innsbruck, Innrain 80/82, A-6020 Innsbruck, Austria
| | - Julian E. Fuchs
- Department
of Theoretical Chemistry, Faculty for Chemistry and Pharmacy, Center
for Molecular Biosciences Innsbruck (CMBI), Leopold-Franzens University Innsbruck, Innrain 80/82, A-6020 Innsbruck, Austria
| | - Steven Whitebread
- Preclinical
Safety Profiling, Center for Proteomic Chemistry, Novartis Institutes for BioMedical Research, 250 Massachusetts Avenue, Cambridge, Massachusetts 02139, United States
| | - Peter Gedeck
- Novartis Institute for Tropical Diseases, 10 Biopolis Road, No. 05-01 Chromos, Singapore 138670, Singapore
| | - Klaus R. Liedl
- Department
of Theoretical Chemistry, Faculty for Chemistry and Pharmacy, Center
for Molecular Biosciences Innsbruck (CMBI), Leopold-Franzens University Innsbruck, Innrain 80/82, A-6020 Innsbruck, Austria
| |
Collapse
|
6
|
Nonlinear Dimensionality Reduction for Visualizing Toxicity Data: Distance-Based Versus Topology-Based Approaches. ChemMedChem 2014; 9:1047-59. [DOI: 10.1002/cmdc.201400027] [Citation(s) in RCA: 9] [Impact Index Per Article: 0.9] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/10/2014] [Indexed: 01/11/2023]
|
7
|
Hu Y, Bajorath J. Follow up: Compound data sets and software tools for chemoinformatics and medicinal chemistry applications: update and data transfer. F1000Res 2014; 3:69. [PMID: 25520777 PMCID: PMC4264635 DOI: 10.12688/f1000research.3713.1] [Citation(s) in RCA: 4] [Impact Index Per Article: 0.4] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Accepted: 03/07/2014] [Indexed: 12/12/2022] Open
Abstract
In 2012, we reported 30 compound data sets and/or programs developed in our laboratory in a data article and made them freely available to the scientific community to support chemoinformatics and computational medicinal chemistry applications. These data sets and computational tools were provided for download from our website. Since publication of this data article, we have generated 13 new data sets with which we further extend our collection of publicly available data and tools. Due to changes in web servers and website architectures, data accessibility has recently been limited at times. Therefore, we have also transferred our data sets and tools to a public repository to ensure full and stable accessibility. To aid in data selection, we have classified the data sets according to scientific subject areas. Herein, we describe new data sets, introduce the data organization scheme, summarize the database content and provide detailed access information in ZENODO (doi: 10.5281/zenodo.8451 and doi:10.5281/zenodo.8455).
Collapse
Affiliation(s)
- Ye Hu
- Department of Life Science Informatics, B-IT, LIMES Program Unit Chemical Biology and Medicinal Chemistry, Rheinische Friedrich-Wilhelms University, Bonn, D-53113, Germany
| | - Jürgen Bajorath
- Department of Life Science Informatics, B-IT, LIMES Program Unit Chemical Biology and Medicinal Chemistry, Rheinische Friedrich-Wilhelms University, Bonn, D-53113, Germany
| |
Collapse
|
8
|
Impact of distance-based metric learning on classification and visualization model performance and structure–activity landscapes. J Comput Aided Mol Des 2014; 28:61-73. [DOI: 10.1007/s10822-014-9719-1] [Citation(s) in RCA: 6] [Impact Index Per Article: 0.6] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/15/2013] [Accepted: 01/24/2014] [Indexed: 10/25/2022]
|
9
|
Ekins S. Progress in computational toxicology. J Pharmacol Toxicol Methods 2013; 69:115-40. [PMID: 24361690 DOI: 10.1016/j.vascn.2013.12.003] [Citation(s) in RCA: 62] [Impact Index Per Article: 5.6] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/08/2013] [Accepted: 12/08/2013] [Indexed: 01/02/2023]
Abstract
INTRODUCTION Computational methods have been widely applied to toxicology across pharmaceutical, consumer product and environmental fields over the past decade. Progress in computational toxicology is now reviewed. METHODS A literature review was performed on computational models for hepatotoxicity (e.g. for drug-induced liver injury (DILI)), cardiotoxicity, renal toxicity and genotoxicity. In addition various publications have been highlighted that use machine learning methods. Several computational toxicology model datasets from past publications were used to compare Bayesian and Support Vector Machine (SVM) learning methods. RESULTS The increasing amounts of data for defined toxicology endpoints have enabled machine learning models that have been increasingly used for predictions. It is shown that across many different models Bayesian and SVM perform similarly based on cross validation data. DISCUSSION Considerable progress has been made in computational toxicology in a decade in both model development and availability of larger scale or 'big data' models. The future efforts in toxicology data generation will likely provide us with hundreds of thousands of compounds that are readily accessible for machine learning models. These models will cover relevant chemistry space for pharmaceutical, consumer product and environmental applications.
Collapse
Affiliation(s)
- Sean Ekins
- Collaborations in Chemistry, 5616 Hilltop Needmore Road, Fuquay Varina, NC 27526, USA; Department of Pharmaceutical Sciences, University of Maryland, 20 Penn Street, Baltimore, MD 21201, USA; Department of Pharmacology, Rutgers University-Robert Wood Johnson Medical School, 675 Hoes Lane, Piscataway, NJ 08854, USA; Division of Chemical Biology and Medicinal Chemistry, UNC Eshelman School of Pharmacy, University of North Carolina at Chapel Hill, NC 27599-7355, USA.
| |
Collapse
|
10
|
Classification of blocker and non-blocker of hERG potassium ion channel using a support vector machine. Sci China Chem 2013. [DOI: 10.1007/s11426-013-4946-1] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/26/2022]
|
11
|
Tanabe K, Kurita T, Nishida K, Lučić B, Amić D, Suzuki T. Improvement of carcinogenicity prediction performances based on sensitivity analysis in variable selection of SVM models. SAR AND QSAR IN ENVIRONMENTAL RESEARCH 2013; 24:565-580. [PMID: 23350528 DOI: 10.1080/1062936x.2012.762425] [Citation(s) in RCA: 11] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/01/2023]
Abstract
A new sensitivity analysis (SA) method for variable selection in support vector machine (SVM) was proposed to improve the performance level of the QSAR model to predict carcinogenicity based on the correlation coefficient (CC) method used in our preceding study. The performances of both methods were also compared with that of the F-score (FS) method proposed by Chang and Lin. The 911 non-congeneric chemicals were classified into 20 mutually overlapping groups according to contained substructures, and a specific SVM model created on chemicals belonging to each group was optimized by searching the best set of SVM parameters while successively omitting descriptors of lower absolute values of sensitivity, CC or FS until the maximum predictive performance was obtained. The SA method improves the overall accuracy from 80% of CC and FS to 84%, which is considerably higher than those of existing models for predicting the carcinogenicity of non-congeneric chemicals. It selects the optimum sets of effective descriptors fewer than the CC and FS methods, and is not time-consuming and can be applied to a large set of initial descriptors. It is concluded that SA is superior as a variable selection method in SVM models.
Collapse
Affiliation(s)
- K Tanabe
- Neuroscience Research Institute, National Institute of Advanced Industrial Science and Technology, Tsukuba, Japan.
| | | | | | | | | | | |
Collapse
|
12
|
Abstract
Frequent failure of drug candidates during development stages remains the major deterrent for an early introduction of new drug molecules. The drug toxicity is the major cause of expensive late-stage development failures. An early identification/optimization of the most favorable molecule will naturally save considerable cost, time, human efforts and minimize animal sacrifice. (Quantitative) Structure Activity Relationships [(Q)SARs] represent statistically derived predictive models correlating biological activity (including desirable therapeutic effect and undesirable side effects) of chemicals (drugs/toxicants/environmental pollutants) with molecular descriptors and/or properties. (Q)SAR models which categorize the available data into two or more groups/classes are known as classification models. Numerous techniques of diverse nature are being presently employed for development of classification models. Though there is an increasing use of classification models for prediction of either biological activity or toxicity, the future trend will naturally be towards the development of classification models capable of simultaneous prediction of biological activity, toxicity, and pharmacokinetic parameters so as to accelerate development of bioavailable safe drug molecules.
Collapse
|
13
|
Prediction of hERG Potassium Channel Blocking Actions Using Combination of Classification and Regression Based Models: A Mixed Descriptors Approach. Mol Inform 2012; 31:879-94. [DOI: 10.1002/minf.201200039] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/11/2012] [Accepted: 11/15/2012] [Indexed: 11/07/2022]
|
14
|
Kireeva N, Kuznetsov SL, Bykov AA, Tsivadze AY. Towards in silico identification of the human ether-a-go-go-related gene channel blockers: discriminative vs. generative classification models. SAR AND QSAR IN ENVIRONMENTAL RESEARCH 2012; 24:103-117. [PMID: 23152964 DOI: 10.1080/1062936x.2012.742135] [Citation(s) in RCA: 13] [Impact Index Per Article: 1.1] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/01/2023]
Abstract
HERG potassium channels have a critical role in the normal electrical activity of the heart. The blockade of hERG channels in heart cells can result in a potentially fatal disorder called long QT syndrome. HERG channels can be blocked by compounds with diverse structures belonging to several drug classes. Presented herein are generative (Generative Topographic Maps) and discriminative (Support Vector Machines) classification models to categorize the compounds in silico into active and inactive classes by using different types of descriptors. The predictive performance of discriminative and generative classification models has been compared. Here, the possibility of using Generative Topographic Maps as an approach for applicability domain analysis and to generate probability-based descriptors was demonstrated to our knowledge for the first time. Comparison of obtained results with the models developed by other teams on the same data set has been performed.
Collapse
Affiliation(s)
- N Kireeva
- Frumkin Institute of Physical Chemistry and Electrochemistry RAS, Moscow, Russia.
| | | | | | | |
Collapse
|
15
|
Wang Z, Mussa HY, Lowe R, Glen RC, Yan A. Probability Based hERG Blocker Classifiers. Mol Inform 2012; 31:679-85. [DOI: 10.1002/minf.201200011] [Citation(s) in RCA: 4] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/02/2012] [Accepted: 07/03/2012] [Indexed: 11/11/2022]
|
16
|
Hu Y, Bajorath J. Freely available compound data sets and software tools for chemoinformatics and computational medicinal chemistry applications. F1000Res 2012; 1:11. [PMID: 24358818 PMCID: PMC3782340 DOI: 10.12688/f1000research.1-11.v1] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Accepted: 08/07/2012] [Indexed: 01/22/2023] Open
Abstract
We have generated a number of compound data sets and programs for different types of applications in pharmaceutical research. These data sets and programs were originally designed for our research projects and are made publicly available. Without consulting original literature sources, it is difficult to understand specific features of data sets and software tools, basic ideas underlying their design, and applicability domains. Currently, 30 different entries are available for download from our website. In this data article, we provide an overview of the data and tools we make available and designate the areas of research for which they should be useful. For selected data sets and methods/programs, detailed descriptions are given. This article should help interested readers to select data and tools for specific computational investigations.
Collapse
Affiliation(s)
- Ye Hu
- Department of Life Science Informatics, Rheinische Friedrich-Wilhelms-Universität, Dahlmannstr, Bonn, D-53113, Germany
| | - Jurgen Bajorath
- Department of Life Science Informatics, Rheinische Friedrich-Wilhelms-Universität, Dahlmannstr, Bonn, D-53113, Germany
| |
Collapse
|
17
|
Vandenberg JI, Perry MD, Perrin MJ, Mann SA, Ke Y, Hill AP. hERG K+ Channels: Structure, Function, and Clinical Significance. Physiol Rev 2012; 92:1393-478. [DOI: 10.1152/physrev.00036.2011] [Citation(s) in RCA: 463] [Impact Index Per Article: 38.6] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 02/08/2023] Open
Abstract
The human ether-a-go-go related gene (hERG) encodes the pore-forming subunit of the rapid component of the delayed rectifier K+ channel, Kv11.1, which are expressed in the heart, various brain regions, smooth muscle cells, endocrine cells, and a wide range of tumor cell lines. However, it is the role that Kv11.1 channels play in the heart that has been best characterized, for two main reasons. First, it is the gene product involved in chromosome 7-associated long QT syndrome (LQTS), an inherited disorder associated with a markedly increased risk of ventricular arrhythmias and sudden cardiac death. Second, blockade of Kv11.1, by a wide range of prescription medications, causes drug-induced QT prolongation with an increase in risk of sudden cardiac arrest. In the first part of this review, the properties of Kv11.1 channels, including biogenesis, trafficking, gating, and pharmacology are discussed, while the second part focuses on the pathophysiology of Kv11.1 channels.
Collapse
Affiliation(s)
- Jamie I. Vandenberg
- Mark Cowley Lidwill Research Programme in Cardiac Electrophysiology, Victor Chang Cardiac Research Institute, Sydney, New South Wales, Australia; St Vincent's Clinical School, University of New South Wales, New South Wales, Australia; and University of Ottawa Heart Institute, Ottawa, Canada
| | - Matthew D. Perry
- Mark Cowley Lidwill Research Programme in Cardiac Electrophysiology, Victor Chang Cardiac Research Institute, Sydney, New South Wales, Australia; St Vincent's Clinical School, University of New South Wales, New South Wales, Australia; and University of Ottawa Heart Institute, Ottawa, Canada
| | - Mark J. Perrin
- Mark Cowley Lidwill Research Programme in Cardiac Electrophysiology, Victor Chang Cardiac Research Institute, Sydney, New South Wales, Australia; St Vincent's Clinical School, University of New South Wales, New South Wales, Australia; and University of Ottawa Heart Institute, Ottawa, Canada
| | - Stefan A. Mann
- Mark Cowley Lidwill Research Programme in Cardiac Electrophysiology, Victor Chang Cardiac Research Institute, Sydney, New South Wales, Australia; St Vincent's Clinical School, University of New South Wales, New South Wales, Australia; and University of Ottawa Heart Institute, Ottawa, Canada
| | - Ying Ke
- Mark Cowley Lidwill Research Programme in Cardiac Electrophysiology, Victor Chang Cardiac Research Institute, Sydney, New South Wales, Australia; St Vincent's Clinical School, University of New South Wales, New South Wales, Australia; and University of Ottawa Heart Institute, Ottawa, Canada
| | - Adam P. Hill
- Mark Cowley Lidwill Research Programme in Cardiac Electrophysiology, Victor Chang Cardiac Research Institute, Sydney, New South Wales, Australia; St Vincent's Clinical School, University of New South Wales, New South Wales, Australia; and University of Ottawa Heart Institute, Ottawa, Canada
| |
Collapse
|
18
|
Cavalli A, Buonfiglio R, Ianni C, Masetti M, Ceccarini L, Caves R, Chang MWY, Mitcheson JS, Roberti M, Recanatini M. Computational Design and Discovery of “Minimally Structured” hERG Blockers. J Med Chem 2012; 55:4010-4. [DOI: 10.1021/jm201194q] [Citation(s) in RCA: 28] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/30/2022]
Affiliation(s)
- Andrea Cavalli
- Department of Pharmaceutical Sciences, University of Bologna, Via Belmeloro 6, I-40126 Bologna, Italy
- Department of Drug Discovery and Development, Istituto Italiano di Tecnologia, Via Morego 30, I-16163 Genova, Italy
| | - Rosa Buonfiglio
- Department of Pharmaceutical Sciences, University of Bologna, Via Belmeloro 6, I-40126 Bologna, Italy
| | - Cristina Ianni
- Department of Pharmaceutical Sciences, University of Bologna, Via Belmeloro 6, I-40126 Bologna, Italy
| | - Matteo Masetti
- Department of Pharmaceutical Sciences, University of Bologna, Via Belmeloro 6, I-40126 Bologna, Italy
| | - Luisa Ceccarini
- Department of Pharmaceutical Sciences, University of Bologna, Via Belmeloro 6, I-40126 Bologna, Italy
| | - Rachel Caves
- Department of Cell Physiology and Pharmacology, University of Leicester, University Road, Leicester LE1 9HN, United Kingdom
| | - Michael W. Y. Chang
- Department of Cell Physiology and Pharmacology, University of Leicester, University Road, Leicester LE1 9HN, United Kingdom
| | - John S. Mitcheson
- Department of Cell Physiology and Pharmacology, University of Leicester, University Road, Leicester LE1 9HN, United Kingdom
| | - Marinella Roberti
- Department of Pharmaceutical Sciences, University of Bologna, Via Belmeloro 6, I-40126 Bologna, Italy
| | - Maurizio Recanatini
- Department of Pharmaceutical Sciences, University of Bologna, Via Belmeloro 6, I-40126 Bologna, Italy
| |
Collapse
|
19
|
Wang S, Li Y, Wang J, Chen L, Zhang L, Yu H, Hou T. ADMET evaluation in drug discovery. 12. Development of binary classification models for prediction of hERG potassium channel blockage. Mol Pharm 2012; 9:996-1010. [PMID: 22380484 DOI: 10.1021/mp300023x] [Citation(s) in RCA: 118] [Impact Index Per Article: 9.8] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/29/2022]
Abstract
Inhibition of the human ether-a-go-go related gene (hERG) potassium channel may result in QT interval prolongation, which causes severe cardiac side effects and is a major problem in clinical studies of drug candidates. The development of in silico tools to filter out potential hERG potassium channel blockers in early stages of the drug discovery process is of considerable interest. Here, a diverse set of 806 compounds with hERG inhibition data was assembled, and the binary hERG classification models using naive Bayesian classification and recursive partitioning (RP) techniques were established and evaluated. The naive Bayesian classifier based on molecular properties and the ECFP_8 fingerprints yielded 84.8% accuracy for the training set using the leave-one-out (LOO) cross-validation procedure and 85% accuracy for the test set of 120 molecules. For the two additional test sets, the model achieved 89.4% accuracy for the WOMBAT-PK test set, and 86.1% accuracy for the PubChem test set. The naive Bayesian classifiers gave better predictions than the RP classifiers. Moreover, the Bayesian classifier, employing molecular fingerprints, highlights the important structural fragments favorable or unfavorable for hERG potassium channel blockage, which offers extra valuable information for the design of compounds avoiding undesirable hERG activity.
Collapse
Affiliation(s)
- Sichao Wang
- Institute of Functional Nano & Soft Materials-FUNSOM and Jiangsu Key Laboratory for Carbon-Based Functional Materials & Devices, Soochow University, Suzhou, Jiangsu 215123, China
| | | | | | | | | | | | | |
Collapse
|
20
|
Zhang S. Application of Machine Leaning in Drug Discovery and Development. Mach Learn 2012. [DOI: 10.4018/978-1-60960-818-7.ch517] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/09/2022]
Abstract
Machine learning techniques have been widely used in drug discovery and development, particularly in the areas of cheminformatics, bioinformatics and other types of pharmaceutical research. It has been demonstrated they are suitable for large high dimensional data, and the models built with these methods can be used for robust external predictions. However, various problems and challenges still exist, and new approaches are in great need. In this Chapter, the authors will review the current development of machine learning techniques, and especially focus on several machine learning techniques they developed as well as their application to model building, lead discovery via virtual screening, integration with molecular docking, and prediction of off-target properties. The authors will suggest some potential different avenues to unify different disciplines, such as cheminformatics, bioinformatics and systems biology, for the purpose of developing integrated in silico drug discovery and development approaches.
Collapse
Affiliation(s)
- Shuxing Zhang
- The University of Texas at M.D. Anderson Cancer Center, USA
| |
Collapse
|
21
|
Tan Y, Chen Y, You Q, Sun H, Li M. Predicting the potency of hERG K+ channel inhibition by combining 3D-QSAR pharmacophore and 2D-QSAR models. J Mol Model 2011; 18:1023-36. [DOI: 10.1007/s00894-011-1136-y] [Citation(s) in RCA: 22] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/05/2011] [Accepted: 05/23/2011] [Indexed: 02/06/2023]
|
22
|
Klon AE. Machine learning algorithms for the prediction of hERG and CYP450 binding in drug development. Expert Opin Drug Metab Toxicol 2011; 6:821-33. [PMID: 20465523 DOI: 10.1517/17425255.2010.489550] [Citation(s) in RCA: 14] [Impact Index Per Article: 1.1] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/29/2023]
Abstract
IMPORTANCE OF THE FIELD The cost of developing new drugs is estimated at approximately $1 billion; the withdrawal of a marketed compound due to toxicity can result in serious financial loss for a pharmaceutical company. There has been a greater interest in the development of in silico tools that can identify compounds with metabolic liabilities before they are brought to market. AREAS COVERED IN THIS REVIEW The two largest classes of machine learning (ML) models, which will be discussed in this review, have been developed to predict binding to the human ether-a-go-go related gene (hERG) ion channel protein and the various CYP isoforms. Being able to identify potentially toxic compounds before they are made would greatly reduce the number of compound failures and the costs associated with drug development. WHAT THE READER WILL GAIN This review summarizes the state of modeling hERG and CYP binding towards this goal since 2003 using ML algorithms. TAKE HOME MESSAGE A wide variety of ML algorithms that are comparable in their overall performance are available. These ML methods may be applied regularly in discovery projects to flag compounds with potential metabolic liabilities.
Collapse
Affiliation(s)
- Anthony E Klon
- Ansaris, Computational Chemistry, Four Valley Square, 512 East Township Line Road, Blue Bell, PA 19422, USA.
| |
Collapse
|
23
|
Raschi E, Ceccarini L, De Ponti F, Recanatini M. hERG-related drug toxicity and models for predicting hERG liability and QT prolongation. Expert Opin Drug Metab Toxicol 2009; 5:1005-21. [PMID: 19572824 DOI: 10.1517/17425250903055070] [Citation(s) in RCA: 57] [Impact Index Per Article: 3.8] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/02/2023]
Abstract
BACKGROUND hERG K(+) channels have been recognized as a primary antitarget in safety pharmacology. Their blockade, caused by several drugs with different therapeutic indications, may lead to QT prolongation and, eventually, to potentially fatal arrhythmia, namely torsade de pointes. Therefore, a number of preclinical models have been developed to predict hERG liability early in the drug development process. OBJECTIVE The aim of this review is to outline the present state of the art on drug-induced hERG blockade, providing insights on the predictive value of in vitro and in silico models for hERG liability. METHODS On the basis of latest reports, high-throughput preclinical models have been discussed outlining advantages and limitations. CONCLUSION Although no single model has an absolute value, an integrated risk assessment is recommended to predict the pro-arrhythmic risk of a given drug. This prediction requires expertise from different areas and should encompass emerging issues such as interference with hERG trafficking and QT shortening.
Collapse
Affiliation(s)
- Emanuel Raschi
- University of Bologna, Department of Pharmacology, Italy
| | | | | | | |
Collapse
|
24
|
Hansen K, Rathke F, Schroeter T, Rast G, Fox T, Kriegl JM, Mika S. Bias-Correction of Regression Models: A Case Study on hERG Inhibition. J Chem Inf Model 2009; 49:1486-96. [DOI: 10.1021/ci9000794] [Citation(s) in RCA: 22] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/13/2023]
Affiliation(s)
- Katja Hansen
- University of Technology, Berlin, Germany, Departments of Drug Discovery Support and Lead Discovery, Boehringer Ingelheim Pharma GmbH & Co. KG, Biberach a.d. Riss, Germany, and idalab GmbH, Berlin, Germany
| | - Fabian Rathke
- University of Technology, Berlin, Germany, Departments of Drug Discovery Support and Lead Discovery, Boehringer Ingelheim Pharma GmbH & Co. KG, Biberach a.d. Riss, Germany, and idalab GmbH, Berlin, Germany
| | - Timon Schroeter
- University of Technology, Berlin, Germany, Departments of Drug Discovery Support and Lead Discovery, Boehringer Ingelheim Pharma GmbH & Co. KG, Biberach a.d. Riss, Germany, and idalab GmbH, Berlin, Germany
| | - Georg Rast
- University of Technology, Berlin, Germany, Departments of Drug Discovery Support and Lead Discovery, Boehringer Ingelheim Pharma GmbH & Co. KG, Biberach a.d. Riss, Germany, and idalab GmbH, Berlin, Germany
| | - Thomas Fox
- University of Technology, Berlin, Germany, Departments of Drug Discovery Support and Lead Discovery, Boehringer Ingelheim Pharma GmbH & Co. KG, Biberach a.d. Riss, Germany, and idalab GmbH, Berlin, Germany
| | - Jan M. Kriegl
- University of Technology, Berlin, Germany, Departments of Drug Discovery Support and Lead Discovery, Boehringer Ingelheim Pharma GmbH & Co. KG, Biberach a.d. Riss, Germany, and idalab GmbH, Berlin, Germany
| | - Sebastian Mika
- University of Technology, Berlin, Germany, Departments of Drug Discovery Support and Lead Discovery, Boehringer Ingelheim Pharma GmbH & Co. KG, Biberach a.d. Riss, Germany, and idalab GmbH, Berlin, Germany
| |
Collapse
|