1
|
Mqawass G, Popov P. graphLambda: Fusion Graph Neural Networks for Binding Affinity Prediction. J Chem Inf Model 2024; 64:2323-2330. [PMID: 38366974 DOI: 10.1021/acs.jcim.3c00771] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 02/19/2024]
Abstract
Predicting the binding affinity of protein-ligand complexes is crucial for computer-aided drug discovery (CADD) and the identification of potential drug candidates. The deep learning-based scoring functions have emerged as promising predictors of binding constants. Building on recent advancements in graph neural networks, we present graphLambda for protein-ligand binding affinity prediction, which utilizes graph convolutional, attention, and isomorphism blocks to enhance the predictive capabilities. The graphLambda model exhibits superior performance across CASF16 and CSAR HiQ NRC benchmarks and demonstrates robustness with respect to different types of train-validation set partitions. The development of graphLambda underscores the potential of graph neural networks in advancing binding affinity prediction models, contributing to more effective CADD methodologies.
Collapse
Affiliation(s)
- Ghaith Mqawass
- Faculty of Computer Science, University of Vienna, Vienna A-1090, Austria
- UniVie Doctoral School Computer Science, University of Vienna, Vienna A-1090, Austria
| | - Petr Popov
- Tetra-d, Rheinweg 9, Schaffhausen 8200, Switzerland
- School of Science, Constructor University Bremen gGmbH, Bremen 28759, Germany
| |
Collapse
|
2
|
Yang Y, Hsieh CY, Kang Y, Hou T, Liu H, Yao X. Deep Generation Model Guided by the Docking Score for Active Molecular Design. J Chem Inf Model 2023; 63:2983-2991. [PMID: 37163364 DOI: 10.1021/acs.jcim.3c00572] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 05/12/2023]
Abstract
A deep generation model, as a novel drug design and discovery tool, shows obvious advantages in generating compounds with novel backbones and has been applied successfully in the field of drug discovery. However, it is still a challenge to generate molecules with expected properties, especially high activity. Here, to obtain compounds both with novelty and high activity to a target, we proposed a conditional molecular generation model COMG by considering the docking score and 3D pharmacophore matching during molecular generation. The proposed model was based on the conditional variational autoencoder architecture constrained by the pharmacophore matching score. During Bayesian optimization, the docking score was applied to enhance the target relevance of generated compounds. Furthermore, to overcome the problem of high structural similarity caused by Bayesian optimization, the idea of the scaffold memory unit was also introduced. The evaluation results of COMG show that our model not only can improve the structural diversity of generated molecules but also can effectively improve the proportion of target-related drug-active molecules. The obtained results indicate that our proposed model COMG is a useful drug design tool.
Collapse
Affiliation(s)
- Yuwei Yang
- Faculty of Applied Sciences, Macao Polytechnic University, Macao (SAR) 999078, P. R. China
- School of Pharmacy, Lanzhou University, Lanzhou 730000, Gansu, P. R. China
| | - Chang-Yu Hsieh
- College of Pharmaceutical Sciences, Zhejiang University, Hangzhou 310058, P. R. China
| | - Yu Kang
- College of Pharmaceutical Sciences, Zhejiang University, Hangzhou 310058, P. R. China
| | - Tingjun Hou
- College of Pharmaceutical Sciences, Zhejiang University, Hangzhou 310058, P. R. China
| | - Huanxiang Liu
- Faculty of Applied Sciences, Macao Polytechnic University, Macao (SAR) 999078, P. R. China
| | - Xiaojun Yao
- State Key Laboratory of Quality Research in Chinese Medicine, Macau University of Science and Technology, Taipa, 999078 Macau (SAR), P. R. China
| |
Collapse
|
3
|
Zhao L, Zhu Y, Wang J, Wen N, Wang C, Cheng L. A brief review of protein-ligand interaction prediction. Comput Struct Biotechnol J 2022; 20:2831-2838. [PMID: 35765652 PMCID: PMC9189993 DOI: 10.1016/j.csbj.2022.06.004] [Citation(s) in RCA: 10] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/14/2022] [Revised: 05/30/2022] [Accepted: 06/01/2022] [Indexed: 01/21/2023] Open
Abstract
The task of identifying protein–ligand interactions (PLIs) plays a prominent role in the field of drug discovery. However, it is infeasible to identify potential PLIs via costly and laborious in vitro experiments. There is a need to develop PLI computational prediction approaches to speed up the drug discovery process. In this review, we summarize a brief introduction to various computation-based PLIs. We discuss these approaches, in particular, machine learning-based methods, with illustrations of different emphases based on mainstream trends. Moreover, we analyzed three research dynamics that can be further explored in future studies.
Collapse
Affiliation(s)
- Lingling Zhao
- Faculty of Computing, Harbin Institute of Technology, Harbin, China
| | - Yan Zhu
- Faculty of Computing, Harbin Institute of Technology, Harbin, China
| | - Junjie Wang
- Department of Medical Informatics, School of Biomedical Engineering and Informatics, Nanjing Medical University, Nanjing, China
| | - Naifeng Wen
- School of Mechanical and Electrical Engineering, Dalian Minzu University, Dalian, China
| | - Chunyu Wang
- Faculty of Computing, Harbin Institute of Technology, Harbin, China
- Corresponding authors.
| | - Liang Cheng
- College of Bioinformatics Science and Technology, Harbin Medical University, Harbin, China
- NHC and CAMS Key Laboratory of Molecular Probe and Targeted Theranostics, Harbin Medical University, Harbin, China
- Corresponding authors.
| |
Collapse
|
4
|
Yu D, Wang L, Wang Y. Recent Advances in Application of Computer-Aided Drug Design in Anti-Influenza A Virus Drug Discovery. Int J Mol Sci 2022; 23:ijms23094738. [PMID: 35563129 PMCID: PMC9105300 DOI: 10.3390/ijms23094738] [Citation(s) in RCA: 8] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/13/2022] [Revised: 04/22/2022] [Accepted: 04/23/2022] [Indexed: 02/06/2023] Open
Abstract
Influenza A is an acute respiratory infectious disease caused by the influenza A virus, which seriously threatens global human health and causes substantial economic losses every year. With the emergence of new viral strains, anti-influenza drugs remain the most effective treatment for influenza A. Research on traditional, innovative small-molecule drugs faces many challenges, while computer-aided drug design (CADD) offers opportunities for the rapid and effective development of innovative drugs. This literature review describes the general process of CADD, the viral proteins that play an essential role in the life cycle of the influenza A virus and can be used as therapeutic targets for anti-influenza drugs, and examples of drug screening of viral target proteins by applying the CADD approach. Finally, the main limitations of current CADD strategies in anti-influenza drug discovery and the field's future directions are discussed.
Collapse
Affiliation(s)
| | | | - Ye Wang
- Correspondence: ; Tel.: +86-431-8515-5249
| |
Collapse
|
5
|
Casadio R, Martelli PL, Savojardo C. Machine learning solutions for predicting protein–protein interactions. WIRES COMPUTATIONAL MOLECULAR SCIENCE 2022. [DOI: 10.1002/wcms.1618] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 10/18/2022]
Affiliation(s)
- Rita Casadio
- Biocomputing Group University of Bologna Bologna Italy
| | | | | |
Collapse
|
6
|
Big data and artificial intelligence (AI) methodologies for computer-aided drug design (CADD). Biochem Soc Trans 2022; 50:241-252. [PMID: 35076690 PMCID: PMC9022974 DOI: 10.1042/bst20211240] [Citation(s) in RCA: 13] [Impact Index Per Article: 6.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/01/2021] [Revised: 12/23/2021] [Accepted: 12/23/2021] [Indexed: 12/18/2022]
Abstract
There have been numerous advances in the development of computational and statistical methods and applications of big data and artificial intelligence (AI) techniques for computer-aided drug design (CADD). Drug design is a costly and laborious process considering the biological complexity of diseases. To effectively and efficiently design and develop a new drug, CADD can be used to apply cutting-edge techniques to various limitations in the drug design field. Data pre-processing approaches, which clean the raw data for consistent and reproducible applications of big data and AI methods are introduced. We include the current status of the applicability of big data and AI methods to drug design areas such as the identification of binding sites in target proteins, structure-based virtual screening (SBVS), and absorption, distribution, metabolism, excretion and toxicity (ADMET) property prediction. Data pre-processing and applications of big data and AI methods enable the accurate and comprehensive analysis of massive biomedical data and the development of predictive models in the field of drug design. Understanding and analyzing biological, chemical, or pharmaceutical architectures of biomedical entities related to drug design will provide beneficial information in the biomedical big data era.
Collapse
|
7
|
Rezaei MA, Li Y, Wu D, Li X, Li C. Deep Learning in Drug Design: Protein-Ligand Binding Affinity Prediction. IEEE/ACM TRANSACTIONS ON COMPUTATIONAL BIOLOGY AND BIOINFORMATICS 2022; 19:407-417. [PMID: 33360998 PMCID: PMC8942327 DOI: 10.1109/tcbb.2020.3046945] [Citation(s) in RCA: 19] [Impact Index Per Article: 9.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/12/2023]
Abstract
Computational drug design relies on the calculation of binding strength between two biological counterparts especially a chemical compound, i.e., a ligand, and a protein. Predicting the affinity of protein-ligand binding with reasonable accuracy is crucial for drug discovery, and enables the optimization of compounds to achieve better interaction with their target protein. In this paper, we propose a data-driven framework named DeepAtom to accurately predict the protein-ligand binding affinity. With 3D Convolutional Neural Network (3D-CNN) architecture, DeepAtom could automatically extract binding related atomic interaction patterns from the voxelized complex structure. Compared with the other CNN based approaches, our light-weight model design effectively improves the model representational capacity, even with the limited available training data. We carried out validation experiments on the PDBbind v.2016 benchmark and the independent Astex Diverse Set. We demonstrate that the less feature engineering dependent DeepAtom approach consistently outperforms the other baseline scoring methods. We also compile and propose a new benchmark dataset to further improve the model performances. With the new dataset as training input, DeepAtom achieves Pearson's R=0.83 and RMSE=1.23 pK units on the PDBbind v.2016 core set. The promising results demonstrate that DeepAtom models can be potentially adopted in computational drug development protocols such as molecular docking and virtual screening.
Collapse
Affiliation(s)
- Mohammad A. Rezaei
- Department of Medicinal Chemistry, Center for Natural Products, Drug Discovery and Development (CNPD3), University of Florida
| | - Yanjun Li
- Large-scale Intelligent Systems Laboratory, NSF Center for Big Learning, University of Florida Gainesville, FL, USA
| | - Dapeng Wu
- Large-scale Intelligent Systems Laboratory, NSF Center for Big Learning, University of Florida Gainesville, FL, USA
| | - Xiaolin Li
- Cognization Lab, Palo Alto, California, USA
| | - Chenglong Li
- Department of Medicinal Chemistry, Center for Natural Products, Drug Discovery and Development (CNPD3), University of Florida
- Large-scale Intelligent Systems Laboratory, NSF Center for Big Learning, University of Florida Gainesville, FL, USA
| |
Collapse
|
8
|
Veit-Acosta M, de Azevedo Junior WF. Computational Prediction of Binding Affinity for CDK2-ligand Complexes. A Protein Target for Cancer Drug Discovery. Curr Med Chem 2021; 29:2438-2455. [PMID: 34365938 DOI: 10.2174/0929867328666210806105810] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Key Words] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/25/2021] [Revised: 06/15/2021] [Accepted: 06/22/2021] [Indexed: 11/22/2022]
Abstract
BACKGROUND CDK2 participates in the control of eukaryotic cell-cycle progression. Due to the great interest in CDK2 for drug development and the relative easiness in crystallizing this enzyme, we have over 400 structural studies focused on this protein target. This structural data is the basis for the development of computational models to estimate CDK2-ligand binding affinity. OBJECTIVE This work focuses on the recent developments in the application of supervised machine learning modeling to develop scoring functions to predict the binding affinity of CDK2. METHOD We employed the structures available at the protein data bank and the ligand information accessed from the BindingDB, Binding MOAD, and PDBbind to evaluate the predictive performance of machine learning techniques combined with physical modeling used to calculate binding affinity. We compared this hybrid methodology with classical scoring functions available in docking programs. RESULTS Our comparative analysis of previously published models indicated that a model created using a combination of a mass-spring system and cross-validated Elastic Net to predict the binding affinity of CDK2-inhibitor complexes outperformed classical scoring functions available in AutoDock4 and AutoDock Vina. CONCLUSION All studies reviewed here suggest that targeted machine learning models are superior to classical scoring functions to calculate binding affinities. Specifically for CDK2, we see that the combination of physical modeling with supervised machine learning techniques exhibits improved predictive performance to calculate the protein-ligand binding affinity. These results find theoretical support in the application of the concept of scoring function space.
Collapse
Affiliation(s)
- Martina Veit-Acosta
- Western Michigan University, 1903 Western, Michigan Ave, Kalamazoo, MI 49008. United States
| | | |
Collapse
|
9
|
Bojarska J, New R, Borowiecki P, Remko M, Breza M, Madura ID, Fruziński A, Pietrzak A, Wolf WM. The First Insight Into the Supramolecular System of D,L-α-Difluoromethylornithine: A New Antiviral Perspective. Front Chem 2021; 9:679776. [PMID: 34055746 PMCID: PMC8155678 DOI: 10.3389/fchem.2021.679776] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/12/2021] [Accepted: 04/26/2021] [Indexed: 12/28/2022] Open
Abstract
Targeting the polyamine biosynthetic pathway by inhibiting ornithine decarboxylase (ODC) is a powerful approach in the fight against diverse viruses, including SARS-CoV-2. Difluoromethylornithine (DFMO, eflornithine) is the best-known inhibitor of ODC and a broad-spectrum, unique therapeutical agent. Nevertheless, its pharmacokinetic profile is not perfect, especially when large doses are required in antiviral treatment. This article presents a holistic study focusing on the molecular and supramolecular structure of DFMO and the design of its analogues toward the development of safer and more effective formulations. In this context, we provide the first deep insight into the supramolecular system of DFMO supplemented by a comprehensive, qualitative and quantitative survey of non-covalent interactions via Hirshfeld surface, molecular electrostatic potential, enrichment ratio and energy frameworks analysis visualizing 3-D topology of interactions in order to understand the differences in the cooperativity of interactions involved in the formation of either basic or large synthons (Long-range Synthon Aufbau Modules, LSAM) at the subsequent levels of well-organized supramolecular self-assembly, in comparison with the ornithine structure. In the light of the drug discovery, supramolecular studies of amino acids, essential constituents of proteins, are of prime importance. In brief, the same amino-carboxy synthons are observed in the bio-system containing DFMO. DFT calculations revealed that the biological environment changes the molecular structure of DFMO only slightly. The ADMET profile of structural modifications of DFMO and optimization of its analogue as a new promising drug via molecular docking are discussed in detail.
Collapse
Affiliation(s)
- Joanna Bojarska
- Chemistry Department, Institute of Ecological and Inorganic Chemistry, Technical University of Lodz, Lodz, Poland
| | - Roger New
- Faculty of Science & Technology, Middlesex University, London, United Kingdom
| | - Paweł Borowiecki
- Faculty of Chemistry, Department of Drugs Technology and Biotechnology, Laboratory of Biocatalysis and Biotransformation, Warsaw University of Technology, Warsaw, Poland
| | | | - Martin Breza
- Department of Physical Chemistry, Slovak Technical University, Bratislava, Slovakia
| | - Izabela D. Madura
- Faculty of Chemistry, Warsaw University of Technology, Warsaw, Poland
| | - Andrzej Fruziński
- Chemistry Department, Institute of Ecological and Inorganic Chemistry, Technical University of Lodz, Lodz, Poland
| | - Anna Pietrzak
- Chemistry Department, Institute of Ecological and Inorganic Chemistry, Technical University of Lodz, Lodz, Poland
| | - Wojciech M. Wolf
- Chemistry Department, Institute of Ecological and Inorganic Chemistry, Technical University of Lodz, Lodz, Poland
| |
Collapse
|