Abstract
Background: One of the main challenges in the early stages of drug discovery is the computational assessment of protein-ligand binding affinity. Machine learning techniques can contribute to predicting this type of interaction. We may apply these techniques following two approaches. Firstly, using the experimental structures for which affinity data is available. Secondly, using protein-ligand docking simulations.
Objective: In this review, we describe recently published machine learning models based on crystal structures, for which binding affinity and thermodynamic data are available.
Method: We used experimental structures available at the protein data bank and binding affinity and thermodynamic data was accessed through BindingDB, Binding MOAD, and PDBbind databases. We reviewed machine learning models to predict binding created using open source programs, such as SAnDReS and Taba.
Results: Analysis of machine learning models trained against datasets, composed of crystal structure complexes indicated the high predictive performance of these models when compared with classical scoring functions.
Conclusion: The rapid increase in the number of crystal structures of protein-ligand complexes created a favorable scenario for developing machine learning models to predict binding affinity. These models rely on experimental data from two sources, the structural and the affinity data. The combination of experimental data generates computational models that outperform the classical scoring functions.
Keywords: Crystal structures, machine learning, scoring function space, binding affinity, SAnDReS, Taba.
Current Medicinal Chemistry
Title:The Impact of Crystallographic Data for the Development of Machine Learning Models to Predict Protein-Ligand Binding Affinity
Volume: 28 Issue: 34
Author(s): Martina Veit-Acosta*Walter Filgueira de Azevedo Junior*
Affiliation:
- Western Michigan University, 1903 Western, Michigan Ave, Kalamazoo, MI49008,United States
- Pontifical Catholic University of Rio Grande do Sul (PUCRS); Av. Ipiranga, 6681 Porto Alegre/RS 90619-900,Brazil
Keywords: Crystal structures, machine learning, scoring function space, binding affinity, SAnDReS, Taba.
Abstract:
Background: One of the main challenges in the early stages of drug discovery is the computational assessment of protein-ligand binding affinity. Machine learning techniques can contribute to predicting this type of interaction. We may apply these techniques following two approaches. Firstly, using the experimental structures for which affinity data is available. Secondly, using protein-ligand docking simulations.
Objective: In this review, we describe recently published machine learning models based on crystal structures, for which binding affinity and thermodynamic data are available.
Method: We used experimental structures available at the protein data bank and binding affinity and thermodynamic data was accessed through BindingDB, Binding MOAD, and PDBbind databases. We reviewed machine learning models to predict binding created using open source programs, such as SAnDReS and Taba.
Results: Analysis of machine learning models trained against datasets, composed of crystal structure complexes indicated the high predictive performance of these models when compared with classical scoring functions.
Conclusion: The rapid increase in the number of crystal structures of protein-ligand complexes created a favorable scenario for developing machine learning models to predict binding affinity. These models rely on experimental data from two sources, the structural and the affinity data. The combination of experimental data generates computational models that outperform the classical scoring functions.
Export Options
About this article
Cite this article as:
Veit-Acosta Martina *, de Azevedo Junior Filgueira Walter *, The Impact of Crystallographic Data for the Development of Machine Learning Models to Predict Protein-Ligand Binding Affinity, Current Medicinal Chemistry 2021; 28 (34) . https://dx.doi.org/10.2174/0929867328666210210121320
DOI https://dx.doi.org/10.2174/0929867328666210210121320 |
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
Approaches to the treatment of chronic inflammation
Chronic inflammation is a hallmark of numerous diseases, significantly impacting global health. Although chronic inflammation is a hot topic, not much has been written about approaches to its treatment. This thematic issue aims to showcase the latest advancements in chronic inflammation treatment and foster discussion on future directions in this ...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
![](/images/wayfinder.jpg)
- 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 Non-Steroidal FXR Antagonists Development for Therapeutic Applications
Current Topics in Medicinal Chemistry Substance Abuse, HIV-1 and Hepatitis
Current HIV Research Recent Progress of Medicinal Chemistry Research on Peroxisome Proliferator-Activated Receptor (PPAR) Ligands for the Treatment of Metabolic Syndrome
Current Bioactive Compounds Newborn Screening through TREC, TREC/KREC System for Primary Immunodeficiency with limitation of TREC/KREC. Comprehensive Review
Anti-Inflammatory & Anti-Allergy Agents in Medicinal Chemistry The Use of Antiparkinsonian Agents in the Management of Drug-Induced Extrapyramidal Symptoms
Current Pharmaceutical Design Genetics of Cardiomyopathies: Novel Perspectives with Next Generation Sequencing
Current Pharmaceutical Design Vitamin D and the Metabolic Syndrome
Current Vascular Pharmacology Intestinal Immunomodulation. Role of Regulative Peptides and Promising Pharmacological Activities
Current Pharmaceutical Design Ribosomal Proteins and Colorectal Cancer
Current Genomics Impact on DNA Methylation in Cancer Prevention and Therapy by Bioactive Dietary Components
Current Medicinal Chemistry Decreasing the Metastatic Potential in Cancers - Targeting the Heparan Sulfate Proteoglycans
Current Drug Targets Hyperglycaemia and Vitamin D: A Systematic Overview
Current Diabetes Reviews Protein Tyrosine Phosphatases, New Targets for Cancer Therapy
Current Cancer Drug Targets Protein-Ligand Docking Simulations with AutoDock4 Focused on the Main Protease of SARS-CoV-2
Current Medicinal Chemistry Development of Mitotane Lipid Nanocarriers and Enantiomers: Two-in-One Solution to Efficiently Treat Adreno-Cortical Carcinoma
Current Medicinal Chemistry Leukotrienes, Antileukotrienes and Asthma
Mini-Reviews in Medicinal Chemistry Turner Syndrome : How Is It Made Up?
Current Genomics Intrinsic Disorder and Function of the HIV-1 Tat Protein
Protein & Peptide Letters Oncolytic Viruses for Induction of Anti-Tumor Immunity
Current Pharmaceutical Biotechnology Cancer Chemoprevention by Targeting the Epigenome
Current Drug Targets