Abstract
The interest in the application of machine learning techniques (MLT) as drug design tools is growing in the last decades. The reason for this is related to the fact that the drug design is very complex and requires the use of hybrid techniques. A brief review of some MLT such as self-organizing maps, multilayer perceptron, bayesian neural networks, counter-propagation neural network and support vector machines is described in this paper. A comparison between the performance of the described methods and some classical statistical methods (such as partial least squares and multiple linear regression) shows that MLT have significant advantages. Nowadays, the number of studies in medicinal chemistry that employ these techniques has considerably increased, in particular the use of support vector machines. The state of the art and the future trends of MLT applications encompass the use of these techniques to construct more reliable QSAR models. The models obtained from MLT can be used in virtual screening studies as well as filters to develop/discovery new chemicals. An important challenge in the drug design field is the prediction of pharmacokinetic and toxicity properties, which can avoid failures in the clinical phases. Therefore, this review provides a critical point of view on the main MLT and shows their potential ability as a valuable tool in drug design.
Keywords: Machine learning, drug design, QSAR, medicinal chemistry, hybrid techniques, multilayer perceptron, bayesian neural networks, pharmacokinetic, toxicity properties, MLT
Current Medicinal Chemistry
Title:Machine Learning Techniques and Drug Design
Volume: 19 Issue: 25
Author(s): J.C. Gertrudes, V.G. Maltarollo, R.A. Silva, P.R. Oliveira, K.M. Honorio and A.B.F. da Silva
Affiliation:
Keywords: Machine learning, drug design, QSAR, medicinal chemistry, hybrid techniques, multilayer perceptron, bayesian neural networks, pharmacokinetic, toxicity properties, MLT
Abstract: The interest in the application of machine learning techniques (MLT) as drug design tools is growing in the last decades. The reason for this is related to the fact that the drug design is very complex and requires the use of hybrid techniques. A brief review of some MLT such as self-organizing maps, multilayer perceptron, bayesian neural networks, counter-propagation neural network and support vector machines is described in this paper. A comparison between the performance of the described methods and some classical statistical methods (such as partial least squares and multiple linear regression) shows that MLT have significant advantages. Nowadays, the number of studies in medicinal chemistry that employ these techniques has considerably increased, in particular the use of support vector machines. The state of the art and the future trends of MLT applications encompass the use of these techniques to construct more reliable QSAR models. The models obtained from MLT can be used in virtual screening studies as well as filters to develop/discovery new chemicals. An important challenge in the drug design field is the prediction of pharmacokinetic and toxicity properties, which can avoid failures in the clinical phases. Therefore, this review provides a critical point of view on the main MLT and shows their potential ability as a valuable tool in drug design.
Export Options
About this article
Cite this article as:
Gertrudes J.C., Maltarollo V.G., Silva R.A., Oliveira P.R., Honorio K.M. and da Silva A.B.F., Machine Learning Techniques and Drug Design, Current Medicinal Chemistry 2012; 19 (25) . https://dx.doi.org/10.2174/092986712802884259
DOI https://dx.doi.org/10.2174/092986712802884259 |
Print ISSN 0929-8673 |
Publisher Name Bentham Science Publisher |
Online ISSN 1875-533X |
Call for Papers in Thematic Issues
Advances in Medicinal Chemistry: From Cancer to Chronic Diseases.
The broad spectrum of the issue will provide a comprehensive overview of emerging trends, novel therapeutic interventions, and translational insights that impact modern medicine. The primary focus will be diseases of global concern, including cancer, chronic pain, metabolic disorders, and autoimmune conditions, providing a broad overview of the advancements in ...read more
Cellular and Molecular Mechanisms of Non-Infectious Inflammatory Diseases: Focus on Clinical Implications
The Special Issue covers the results of the studies on cellular and molecular mechanisms of non-infectious inflammatory diseases, in particular, autoimmune rheumatic diseases, atherosclerotic cardiovascular disease and other age-related disorders such as type II diabetes, cancer, neurodegenerative disorders, etc. Review and research articles as well as methodology papers that summarize ...read more
Chalcogen-modified nucleic acid analogues
Chalcogen-modified nucleosides, nucleotides and oligonucleotides have been of great interest to scientific research for many years. The replacement of oxygen in the nucleobase, sugar or phosphate backbone by chalcogen atoms (sulfur, selenium, tellurium) gives these biomolecules unique properties resulting from their altered physical and chemical properties. The continuing interest in ...read more
Current advances in inherited cardiomyopathy
Describe in detail all novel advances in multimodality imaging related to inherited cardiomyopathy diagnosis and prognosis. Shed light to deeper phenotypic characterization. Acknowledge recent advances in genetics, genomics and precision medicineread more
- Author Guidelines
- Graphical Abstracts
- Fabricating and Stating False Information
- Research Misconduct
- Post Publication Discussions and Corrections
- Publishing Ethics and Rectitude
- Increase Visibility of Your Article
- Archiving Policies
- Peer Review Workflow
- Order Your Article Before Print
- Promote Your Article
- Manuscript Transfer Facility
- Editorial Policies
- Allegations from Whistleblowers
- Announcements
Related Articles
-
Target Genetic Abnormalities for the Treatment of Colon Cancer and Its Progression to Metastasis
Current Drug Targets Involvement of Orbital Structures in Rheumatic Disease
Current Rheumatology Reviews Therapeutic Strategies for Targeting BRAF in Human Cancer
Reviews on Recent Clinical Trials Microfluidic Methods for Non-Viral Gene Delivery
Current Gene Therapy CNS Cancer Cell Line Cytotoxicity Profiles of Some 2, 6, 9-Substituted Purines: A Comparative Five-Dose Testing Study
Letters in Drug Design & Discovery ZIP4 is a Novel Diagnostic and Prognostic Marker in Human Pancreatic Cancer: A Systemic Comparison Between EUS-FNA and Surgical Specimens
Current Molecular Medicine Cdc20: A Potential Novel Therapeutic Target for Cancer Treatment
Current Pharmaceutical Design p75NTR as a Therapeutic Target for Neuropsychiatric Diseases
Current Molecular Pharmacology Physico-chemical and Biological Evaluation of Flavonols: Fisetin, Quercetin and Kaempferol Alone and Incorporated in beta Cyclodextrins
Anti-Cancer Agents in Medicinal Chemistry Novel Aspects of Natural and Modified Vinca Alkaloids
Current Medicinal Chemistry - Anti-Cancer Agents Vitamins in the Prevention or Delay of Cognitive Disability of Aging
Current Aging Science Expression Profiling of Estrogen Responsive Genes Using Genomic and Proteomic Techniques for the Evaluation of Endocrine Disruptors
Current Pharmacogenomics Novel Drugs for Chronic Lymphoid Leukemias: Mechanism of Action and Therapeutic Activity
Current Medicinal Chemistry The Role of Foxp3 in Regulatory T Cell Differentiation and Function
Current Immunology Reviews (Discontinued) Phytosterols: Perspectives in Human Nutrition and Clinical Therapy
Current Medicinal Chemistry Targeting DNA Minor Groove by Hybrid Molecules as Anticancer Agents
Current Medicinal Chemistry IgG4-Related Disease (IgG4+MOLPS) – Diagnostic Criteria and Diagnostic Problems
Current Immunology Reviews (Discontinued) Role of Lycopene in the Control of ROS-Mediated Cell Growth: Implications in Cancer Prevention
Current Medicinal Chemistry The Paths to Neurodegeneration in Genetic Parkinson's Disease
CNS & Neurological Disorders - Drug Targets Development of Novel Therapeutics Targeting the Urokinase Plasminogen Activator Receptor (uPAR) and Their Translation Toward the Clinic
Current Pharmaceutical Design