Abstract
Drug discovery process many times encounters complex problems, which may be difficult to solve by human intelligence. Artificial Neural Networks (ANNs) are one of the Artificial Intelligence (AI) technologies used for solving such complex problems. ANNs are widely used for primary virtual screening of compounds, quantitative structure activity relationship studies, receptor modeling, formulation development, pharmacokinetics and in all other processes involving complex mathematical modeling. Despite having such advanced technologies and enough understanding of biological systems, drug discovery is still a lengthy, expensive, difficult and inefficient process with low rate of new successful therapeutic discovery. In this paper, author has discussed the drug discovery science and ANN from very basic angle, which may be helpful to understand the application of ANN for drug discovery to improve efficiency.
Keywords: Drug discovery, artificial neural network
Current Drug Discovery Technologies
Title:Science of the Science, Drug Discovery and Artificial Neural Networks
Volume: 10 Issue: 1
Author(s): Jigneshkumar Patel
Affiliation:
Keywords: Drug discovery, artificial neural network
Abstract: Drug discovery process many times encounters complex problems, which may be difficult to solve by human intelligence. Artificial Neural Networks (ANNs) are one of the Artificial Intelligence (AI) technologies used for solving such complex problems. ANNs are widely used for primary virtual screening of compounds, quantitative structure activity relationship studies, receptor modeling, formulation development, pharmacokinetics and in all other processes involving complex mathematical modeling. Despite having such advanced technologies and enough understanding of biological systems, drug discovery is still a lengthy, expensive, difficult and inefficient process with low rate of new successful therapeutic discovery. In this paper, author has discussed the drug discovery science and ANN from very basic angle, which may be helpful to understand the application of ANN for drug discovery to improve efficiency.
Export Options
About this article
Cite this article as:
Patel Jigneshkumar, Science of the Science, Drug Discovery and Artificial Neural Networks, Current Drug Discovery Technologies 2013; 10 (1) . https://dx.doi.org/10.2174/1570163811310010002
DOI https://dx.doi.org/10.2174/1570163811310010002 |
Print ISSN 1570-1638 |
Publisher Name Bentham Science Publisher |
Online ISSN 1875-6220 |
- 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
-
Recent Advances in Optimal Adjunctive Antithrombotic Therapy in STEMI Patients Undergoing Primary Angioplasty: An Overview
Current Vascular Pharmacology Obesity, Diabetes and Cardiovascular Diseases in India: Public Health Challenges
Current Diabetes Reviews From Molecular Footprints of Disease to New Therapeutic Interventions in Diabetic Nephropathy: A Detective Story
Current Drug Targets - Immune, Endocrine & Metabolic Disorders Nox Inhibitors & Therapies: Rational Design of Peptidic and Small Molecule Inhibitors
Current Pharmaceutical Design Hypothalamic Leptin and Ghrelin Signaling as Targets for Improvement in Metabolic Control
Current Pharmaceutical Design Stem Cell Therapy for the Treatment of Myocardial Infarction
Current Pharmaceutical Design Enhanced Free Radical Status of Cancer Cells Success and Failure of Prooxidant/Antioxidant Treatment
Current Signal Transduction Therapy Advances in Tissue and Organ Replacement
Current Stem Cell Research & Therapy Switching Dipeptidyl Peptidase-4 Inhibitors to Tofogliflozin, a Selective Inhibitor of Sodium-Glucose Cotransporter 2 Improve Arterial Stiffness Evaluated by Cardio-Ankle Vascular Index in Patients with Type 2 Diabetes: A Pilot Study
Current Vascular Pharmacology <i>In vitro</i>, <i>In vivo</i> and <i>In silico</i> Antihyperglycemic Activity of Some Semi-Synthetic Phytol Derivatives
Medicinal Chemistry Role of Vascular Endothelial Growth Factor in Kidney Disease
Current Vascular Pharmacology Does Erythropoietin Always Win?
Current Medicinal Chemistry Saxagliptin: A New Drug for the Treatment of Type 2 Diabetes
Mini-Reviews in Medicinal Chemistry The Prothrombotic State in Hypertension and the Effects of Antihypertensive Treatment
Current Pharmaceutical Design Boosting the Limited Use of Mineralocorticoid Receptor Antagonists Through New Agents for Hyperkalemia
Current Pharmaceutical Design Computational Modeling of Environmentally Responsive Hydrogels (ERH) for Drug Delivery System
Current Computer-Aided Drug Design Treatment of Leishmaniasis: A Review and Assessment of Recent Research
Current Pharmaceutical Design Vascular Stiffness: Measurements, Mechanisms and Implications
Current Vascular Pharmacology Pharmacological Strategies Against Glucocorticoid-mediated Brain Damage During Chronic Disorders
Recent Patents on CNS Drug Discovery (Discontinued) Regulation of Matrix Synthesis, Remodeling and Accumulation in Glomerulosclerosis
Current Pharmaceutical Design