Abstract
Machine learning (ML) computational methods for predicting compounds with pharmacological activity, specific pharmacodynamic and ADMET (absorption, distribution, metabolism, excretion and toxicity) properties are being increasingly applied in drug discovery and evaluation. Recently, machine learning techniques such as artificial neural networks, support vector machines and genetic programming have been explored for predicting inhibitors, antagonists, blockers, agonists, activators and substrates of proteins related to specific therapeutic targets. These methods are particularly useful for screening compound libraries of diverse chemical structures, “noisy” and high-dimensional data to complement QSAR methods, and in cases of unavailable receptor 3D structure to complement structure-based methods. A variety of studies have demonstrated the potential of machine-learning methods for predicting compounds as potential drug candidates. The present review is intended to give an overview of the strategies and current progress in using machine learning methods for drug design and the potential of the respective model development tools. We also regard a number of applications of the machine learning algorithms based on common classes of diseases.
Keywords: Machine learning, support vector machine, genetic programming, artificial neural network, QSAR.
Current Topics in Medicinal Chemistry
Title:In Silico Machine Learning Methods in Drug Development
Volume: 14 Issue: 16
Author(s): Dimitar A. Dobchev, Girinath G. Pillai and Mati Karelson
Affiliation:
Keywords: Machine learning, support vector machine, genetic programming, artificial neural network, QSAR.
Abstract: Machine learning (ML) computational methods for predicting compounds with pharmacological activity, specific pharmacodynamic and ADMET (absorption, distribution, metabolism, excretion and toxicity) properties are being increasingly applied in drug discovery and evaluation. Recently, machine learning techniques such as artificial neural networks, support vector machines and genetic programming have been explored for predicting inhibitors, antagonists, blockers, agonists, activators and substrates of proteins related to specific therapeutic targets. These methods are particularly useful for screening compound libraries of diverse chemical structures, “noisy” and high-dimensional data to complement QSAR methods, and in cases of unavailable receptor 3D structure to complement structure-based methods. A variety of studies have demonstrated the potential of machine-learning methods for predicting compounds as potential drug candidates. The present review is intended to give an overview of the strategies and current progress in using machine learning methods for drug design and the potential of the respective model development tools. We also regard a number of applications of the machine learning algorithms based on common classes of diseases.
Export Options
About this article
Cite this article as:
Dobchev A. Dimitar, Pillai G. Girinath and Karelson Mati, In Silico Machine Learning Methods in Drug Development, Current Topics in Medicinal Chemistry 2014; 14 (16) . https://dx.doi.org/10.2174/1568026614666140929124203
DOI https://dx.doi.org/10.2174/1568026614666140929124203 |
Print ISSN 1568-0266 |
Publisher Name Bentham Science Publisher |
Online ISSN 1873-4294 |
Call for Papers in Thematic Issues
AlphaFold in Medicinal Chemistry: Opportunities and Challenges
AlphaFold, a groundbreaking AI tool for protein structure prediction, is revolutionizing drug discovery. Its near-atomic accuracy unlocks new avenues for designing targeted drugs and performing efficient virtual screening. However, AlphaFold's static predictions lack the dynamic nature of proteins, crucial for understanding drug action. This is especially true for multi-domain proteins, ...read more
Artificial intelligence for Natural Products Discovery and Development
Our approach involves using computational methods to predict the potential therapeutic benefits of natural products by considering factors such as drug structure, targets, and interactions. We also employ multitarget analysis to understand the role of drug targets in disease pathways. We advocate for the use of artificial intelligence in predicting ...read more
Chemistry Based on Natural Products for Therapeutic Purposes
The development of new pharmaceuticals for a wide range of medical conditions has long relied on the identification of promising natural products (NPs). There are over sixty percent of cancer, infectious illness, and CNS disease medications that include an NP pharmacophore, according to the Food and Drug Administration. Since NP ...read more
Current Trends in Drug Discovery Based on Artificial Intelligence and Computer-Aided Drug Design
Drug development discovery has faced several challenges over the years. In fact, the evolution of classical approaches to modern methods using computational methods, or Computer-Aided Drug Design (CADD), has shown promising and essential results in any drug discovery campaign. Among these methods, molecular docking is one of the most notable ...read 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
-
Novel Therapeutic Potential of Mitogen-Activated Protein Kinase Activated Protein Kinase 2 (MK2) in Chronic Airway Inflammatory Disorders
Current Drug Targets Zebrafish as a Model for the Study of the Phase II Cytosolic Sulfotransferases
Current Drug Metabolism TGF-Beta: a Master Switch in Tumor Immunity
Current Pharmaceutical Design An Update on Overcoming MDR1-Mediated Multidrug Resistance in Cancer Chemotherapy
Current Pharmaceutical Design Functional Characteristic of Snake Venom Disintegrins: Potential Therapeutic Implication
Current Pharmaceutical Design Development and Assessment of Conventional and Targeted Drug Combinations for Use in the Treatment of Aggressive Breast Cancers
Current Cancer Drug Targets Patent Selections
Recent Patents on DNA & Gene Sequences Therapeutic Potential of Natural Compounds in Lung Cancer
Current Medicinal Chemistry Preface
Current Radiopharmaceuticals Editorial (Thematic Issue: New Insights into a Classical Pathway: Key Roles of the Mevalonate Cascade in Different Diseases (Part I))
Current Molecular Pharmacology Prognostic and Predictive Biomarkers in Colorectal Cancer. From the Preclinical Setting to Clinical Practice
Current Cancer Drug Targets Current Overview of Lung Cancer from Pathology, Screening to Treatment
Current Cancer Therapy Reviews Small Molecular Inhibitors of p-STAT3: Novel Agents for Treatment of Primary and Metastatic CNS Cancers
Recent Patents on CNS Drug Discovery (Discontinued) Therapy of Elderly/Comorbid Patients with Chronic Lymphocytic Leukemia
Current Pharmaceutical Design Established and In-trial GPCR Families in Clinical Trials: A Review for Target Selection
Current Drug Targets Inhibition of mTOR Signaling by Quercetin in Cancer Treatment and Prevention
Anti-Cancer Agents in Medicinal Chemistry Radiation Protection of the Child from Diagnostic Imaging
Current Pediatric Reviews Peptide-Receptor Ligands and Multivalent Approach
Anti-Cancer Agents in Medicinal Chemistry Microgravity Alters Cancer Growth and Progression
Current Cancer Drug Targets Histotype in Non-Small Cell Lung Cancer Therapy and Staging: The Emerging Role of an Old and Underrated Factor
Current Respiratory Medicine Reviews