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Journal articles on the topic 'Prediction of binding affinity'

Author: Grafiati

Published: 6 September 2023

Last updated: 19 July 2025

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1

Kondabala, Rajesh, Vijay Kumar, Amjad Ali, and Manjit Kaur. "A novel astrophysics-based framework for prediction of binding affinity of glucose binder." Modern Physics Letters B 34, no. 31 (2020): 2050346. http://dx.doi.org/10.1142/s0217984920503467.

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In this paper, a novel astrophysics-based prediction framework is developed for estimating the binding affinity of a glucose binder. The proposed framework utilizes the molecule properties for predicting the binding affinity. It also uses the astrophysics-learning strategy that incorporates the concepts of Kepler’s law during the prediction process. The proposed framework is compared with 10 regression algorithms over ZINC dataset. Experimental results reveal that the proposed framework provides 99.30% accuracy of predicting binding affinity. However, decision tree provides the prediction with

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Kwon, Yongbeom, Woong-Hee Shin, Junsu Ko, and Juyong Lee. "AK-Score: Accurate Protein-Ligand Binding Affinity Prediction Using an Ensemble of 3D-Convolutional Neural Networks." International Journal of Molecular Sciences 21, no. 22 (2020): 8424. http://dx.doi.org/10.3390/ijms21228424.

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Accurate prediction of the binding affinity of a protein-ligand complex is essential for efficient and successful rational drug design. Therefore, many binding affinity prediction methods have been developed. In recent years, since deep learning technology has become powerful, it is also implemented to predict affinity. In this work, a new neural network model that predicts the binding affinity of a protein-ligand complex structure is developed. Our model predicts the binding affinity of a complex using the ensemble of multiple independently trained networks that consist of multiple channels o

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Antunes, Dinler A., Jayvee R. Abella, Didier Devaurs, Maurício M. Rigo, and Lydia E. Kavraki. "Structure-based Methods for Binding Mode and Binding Affinity Prediction for Peptide-MHC Complexes." Current Topics in Medicinal Chemistry 18, no. 26 (2019): 2239–55. http://dx.doi.org/10.2174/1568026619666181224101744.

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Understanding the mechanisms involved in the activation of an immune response is essential to many fields in human health, including vaccine development and personalized cancer immunotherapy. A central step in the activation of the adaptive immune response is the recognition, by T-cell lymphocytes, of peptides displayed by a special type of receptor known as Major Histocompatibility Complex (MHC). Considering the key role of MHC receptors in T-cell activation, the computational prediction of peptide binding to MHC has been an important goal for many immunological applications. Sequence- based

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Wang, Debby D., Haoran Xie, and Hong Yan. "Proteo-chemometrics interaction fingerprints of protein–ligand complexes predict binding affinity." Bioinformatics 37, no. 17 (2021): 2570–79. http://dx.doi.org/10.1093/bioinformatics/btab132.

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Abstract Motivation Reliable predictive models of protein–ligand binding affinity are required in many areas of biomedical research. Accurate prediction based on current descriptors or molecular fingerprints (FPs) remains a challenge. We develop novel interaction FPs (IFPs) to encode protein–ligand interactions and use them to improve the prediction. Results Proteo-chemometrics IFPs (PrtCmm IFPs) formed by combining extended connectivity fingerprints (ECFPs) with the proteo-chemometrics concept. Combining PrtCmm IFPs with machine-learning models led to efficient scoring models, which were vali

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Han, Rong, Xiaohong Liu, Tong Pan, et al. "CoPRA: Bridging Cross-domain Pretrained Sequence Models with Complex Structures for Protein-RNA Binding Affinity Prediction." Proceedings of the AAAI Conference on Artificial Intelligence 39, no. 1 (2025): 246–54. https://doi.org/10.1609/aaai.v39i1.32001.

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Accurately measuring protein-RNA binding affinity is crucial in many biological processes and drug design. Previous computational methods for protein-RNA binding affinity prediction rely on either sequence or structure features, unable to capture the binding mechanisms comprehensively. The recent emerging pre-trained language models trained on massive unsupervised sequences of protein and RNA have shown strong representation ability for various in-domain downstream tasks, including binding site prediction. However, applying different-domain language models collaboratively for complex-level tas

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Nguyen, Austin, Abhinav Nellore, and Reid F. Thompson. "Discordant results among major histocompatibility complex binding affinity prediction tools." F1000Research 12 (June 7, 2023): 617. http://dx.doi.org/10.12688/f1000research.132538.1.

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Background: Human leukocyte antigen (HLA) alleles are critical components of the immune system’s ability to recognize and eliminate tumors and infections. A large number of machine learning-based major histocompatibility complex (MHC) binding affinity (BA) prediction tools have been developed and are widely used for both investigational and therapeutic applications, so it is important to explore differences in tool outputs. Methods: We examined predictions of four popular tools (netMHCpan, HLAthena, MHCflurry, and MHCnuggets) across a range of possible peptide sources (human, viral, and random

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Shar, Piar Ali, Weiyang Tao, Shuo Gao, et al. "Pred-binding: large-scale protein–ligand binding affinity prediction." Journal of Enzyme Inhibition and Medicinal Chemistry 31, no. 6 (2016): 1443–50. http://dx.doi.org/10.3109/14756366.2016.1144594.

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Husnul, Khotimah, Jatmiko Widy, Nur Azizah Dita та ін. "Prediction of drug candidate from Rosmarinus officinalis L to inhibit IL-6R, IL-1R1, and TNF-α: In silico study". World Journal of Advanced Research and Reviews 21, № 2 (2024): 252–60. https://doi.org/10.5281/zenodo.13995362.

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Tooth pain is a manifestation of pulpitis caused by dental caries. Toothache can be treated using eugenol. However, eugenol has several disadvantages, including its toxic effects on fibroblast pulp tissue in a dose-dependent manner. This research assesses the binding affinity of drug candidates, predicting physicochemical properties, pharmacokinetics, drug-likeness, LD50, and toxicity. Molecular docking results show that Rosmarinic acid, Carnosic acid, Carnosol, Ursolic acid can bind strongly to IL-6R and IL-1R1. Meanwhile, only the compounds Carnosic acid and Ursolic acid bind strongly to TNF

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Wang, Xun, Dayan Liu, Jinfu Zhu, Alfonso Rodriguez-Paton, and Tao Song. "CSConv2d: A 2-D Structural Convolution Neural Network with a Channel and Spatial Attention Mechanism for Protein-Ligand Binding Affinity Prediction." Biomolecules 11, no. 5 (2021): 643. http://dx.doi.org/10.3390/biom11050643.

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The binding affinity of small molecules to receptor proteins is essential to drug discovery and drug repositioning. Chemical methods are often time-consuming and costly, and models for calculating the binding affinity are imperative. In this study, we propose a novel deep learning method, namely CSConv2d, for protein-ligand interactions’ prediction. The proposed method is improved by a DEEPScreen model using 2-D structural representations of compounds as input. Furthermore, a channel and spatial attention mechanism (CS) is added in feature abstractions. Data experiments conducted on ChEMBLv23

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Langham, James J., Ann E. Cleves, Russell Spitzer, Daniel Kirshner, and Ajay N. Jain. "Physical Binding Pocket Induction for Affinity Prediction." Journal of Medicinal Chemistry 52, no. 19 (2009): 6107–25. http://dx.doi.org/10.1021/jm901096y.

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Öztürk, Hakime, Arzucan Özgür, and Elif Ozkirimli. "DeepDTA: deep drug–target binding affinity prediction." Bioinformatics 34, no. 17 (2018): i821—i829. http://dx.doi.org/10.1093/bioinformatics/bty593.

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Zhang, Diya, Qiaozhen Meng, and Fei Guo. "Incorporating Water Molecules into Highly Accurate Binding Affinity Prediction for Proteins and Ligands." International Journal of Molecular Sciences 25, no. 23 (2024): 12676. http://dx.doi.org/10.3390/ijms252312676.

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In the binding process between proteins and ligand molecules, water molecules play a pivotal role by forming hydrogen bonds that enable proteins and ligand molecules to bind more strongly. However, current methodologies for predicting binding affinity overlook the importance of water molecules. Therefore, we developed a model called GraphWater-Net, specifically designed for predicting protein–ligand binding affinity, by incorporating water molecules. GraphWater-Net employs topological structures to represent protein atoms, ligand atoms and water molecules, and their interactions. Leveraging th

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Pantsar, Tatu, and Antti Poso. "Binding Affinity via Docking: Fact and Fiction." Molecules 23, no. 8 (2018): 1899. http://dx.doi.org/10.3390/molecules23081899.

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In 1982, Kuntz et al. published an article with the title “A Geometric Approach to Macromolecule-Ligand Interactions”, where they described a method “to explore geometrically feasible alignment of ligands and receptors of known structure”. Since then, small molecule docking has been employed as a fast way to estimate the binding pose of a given compound within a specific target protein and also to predict binding affinity. Remarkably, the first docking method suggested by Kuntz and colleagues aimed to predict binding poses but very little was specified about binding affinity. This raises the q

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OUYANG, XUCHANG, STEPHANUS DANIEL HANDOKO, and CHEE KEONG KWOH. "CSCORE: A SIMPLE YET EFFECTIVE SCORING FUNCTION FOR PROTEIN–LIGAND BINDING AFFINITY PREDICTION USING MODIFIED CMAC LEARNING ARCHITECTURE." Journal of Bioinformatics and Computational Biology 09, supp01 (2011): 1–14. http://dx.doi.org/10.1142/s021972001100577x.

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Protein–ligand docking is a computational method to identify the binding mode of a ligand and a target protein, and predict the corresponding binding affinity using a scoring function. This method has great value in drug design. After decades of development, scoring functions nowadays typically can identify the true binding mode, but the prediction of binding affinity still remains a major problem. Here we present CScore, a data-driven scoring function using a modified Cerebellar Model Articulation Controller (CMAC) learning architecture, for accurate binding affinity prediction. The performan

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Kappel, Kalli, Inga Jarmoskaite, Pavanapuresan P. Vaidyanathan, William J. Greenleaf, Daniel Herschlag, and Rhiju Das. "Blind tests of RNA–protein binding affinity prediction." Proceedings of the National Academy of Sciences 116, no. 17 (2019): 8336–41. http://dx.doi.org/10.1073/pnas.1819047116.

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Interactions between RNA and proteins are pervasive in biology, driving fundamental processes such as protein translation and participating in the regulation of gene expression. Modeling the energies of RNA–protein interactions is therefore critical for understanding and repurposing living systems but has been hindered by complexities unique to RNA–protein binding. Here, we bring together several advances to complete a calculation framework for RNA–protein binding affinities, including a unified free energy function for bound complexes, automated Rosetta modeling of mutations, and use of secon

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16

Kim, Ryangguk, and Jeffrey Skolnick. "Assessment of programs for ligand binding affinity prediction." Journal of Computational Chemistry 29, no. 8 (2008): 1316–31. http://dx.doi.org/10.1002/jcc.20893.

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Henrich, Stefan, Isabella Feierberg, Ting Wang, Niklas Blomberg, and Rebecca C. Wade. "Comparative binding energy analysis for binding affinity and target selectivity prediction." Proteins: Structure, Function, and Bioinformatics 78, no. 1 (2009): 135–53. http://dx.doi.org/10.1002/prot.22579.

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18

Kalemati, Mahmood, Mojtaba Zamani Emani, and Somayyeh Koohi. "BiComp-DTA: Drug-target binding affinity prediction through complementary biological-related and compression-based featurization approach." PLOS Computational Biology 19, no. 3 (2023): e1011036. http://dx.doi.org/10.1371/journal.pcbi.1011036.

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Drug-target binding affinity prediction plays a key role in the early stage of drug discovery. Numerous experimental and data-driven approaches have been developed for predicting drug-target binding affinity. However, experimental methods highly rely on the limited structural-related information from drug-target pairs, domain knowledge, and time-consuming assays. On the other hand, learning-based methods have shown an acceptable prediction performance. However, most of them utilize several simple and complex types of proteins and drug compounds data, ranging from the protein sequences to the t

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Zeng, Haoyang, and David K. Gifford. "DeepLigand: accurate prediction of MHC class I ligands using peptide embedding." Bioinformatics 35, no. 14 (2019): i278—i283. http://dx.doi.org/10.1093/bioinformatics/btz330.

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Abstract Motivation The computational modeling of peptide display by class I major histocompatibility complexes (MHCs) is essential for peptide-based therapeutics design. Existing computational methods for peptide-display focus on modeling the peptide-MHC-binding affinity. However, such models are not able to characterize the sequence features for the other cellular processes in the peptide display pathway that determines MHC ligand selection. Results We introduce a semi-supervised model, DeepLigand that outperforms the state-of-the-art models in MHC Class I ligand prediction. DeepLigand combi

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Marshall, K. W., K. J. Wilson, J. Liang, A. Woods, D. Zaller, and J. B. Rothbard. "Prediction of peptide affinity to HLA DRB1*0401." Journal of Immunology 154, no. 11 (1995): 5927–33. http://dx.doi.org/10.4049/jimmunol.154.11.5927.

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Abstract A method to predict quantitatively peptide binding to HLA DRB1*0401 has been developed using a data set of the relative contributions of each of the naturally occurring amino acids in the context of a simplified peptide back-bone. The prediction assumed that the relative role of each of the peptide side chains could be treated independently and could be measured by assaying each of the 20 naturally occurring amino acids at the central 11 positions of a 13-residue peptide previously shown to contain the minimal requirements for high-affinity binding to HLA-DR proteins. The resultant da

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Li, Min, Zhangli Lu, Yifan Wu, and YaoHang Li. "BACPI: a bi-directional attention neural network for compound–protein interaction and binding affinity prediction." Bioinformatics 38, no. 7 (2022): 1995–2002. http://dx.doi.org/10.1093/bioinformatics/btac035.

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Abstract Motivation The identification of compound–protein interactions (CPIs) is an essential step in the process of drug discovery. The experimental determination of CPIs is known for a large amount of funds and time it consumes. Computational model has therefore become a promising and efficient alternative for predicting novel interactions between compounds and proteins on a large scale. Most supervised machine learning prediction models are approached as a binary classification problem, which aim to predict whether there is an interaction between the compound and the protein or not. Howeve

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Dandibhotla, Somanath, Madhav Samudrala, Arjun Kaneriya, and Sivanesan Dakshanamurthy. "GNNSeq: A Sequence-Based Graph Neural Network for Predicting Protein–Ligand Binding Affinity." Pharmaceuticals 18, no. 3 (2025): 329. https://doi.org/10.3390/ph18030329.

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Background/Objectives: Accurately predicting protein–ligand binding affinity is essential in drug discovery for identifying effective compounds. While existing sequence-based machine learning models for binding affinity prediction have shown potential, they lack accuracy and robustness in pattern recognition, which limits their generalizability across diverse and novel binding complexes. To overcome these limitations, we developed GNNSeq, a novel hybrid machine learning model that integrates a Graph Neural Network (GNN) with Random Forest (RF) and XGBoost. Methods: GNNSeq predicts ligand bindi

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Bae, Haelee, and Hojung Nam. "GraphATT-DTA: Attention-Based Novel Representation of Interaction to Predict Drug-Target Binding Affinity." Biomedicines 11, no. 1 (2022): 67. http://dx.doi.org/10.3390/biomedicines11010067.

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Drug-target binding affinity (DTA) prediction is an essential step in drug discovery. Drug-target protein binding occurs at specific regions between the protein and drug, rather than the entire protein and drug. However, existing deep-learning DTA prediction methods do not consider the interactions between drug substructures and protein sub-sequences. This work proposes GraphATT-DTA, a DTA prediction model that constructs the essential regions for determining interaction affinity between compounds and proteins, modeled with an attention mechanism for interpretability. We make the model conside

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Chen, Zihao, Long Hu, Bao-Ting Zhang, et al. "Artificial Intelligence in Aptamer–Target Binding Prediction." International Journal of Molecular Sciences 22, no. 7 (2021): 3605. http://dx.doi.org/10.3390/ijms22073605.

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Aptamers are short single-stranded DNA, RNA, or synthetic Xeno nucleic acids (XNA) molecules that can interact with corresponding targets with high affinity. Owing to their unique features, including low cost of production, easy chemical modification, high thermal stability, reproducibility, as well as low levels of immunogenicity and toxicity, aptamers can be used as an alternative to antibodies in diagnostics and therapeutics. Systematic evolution of ligands by exponential enrichment (SELEX), an experimental approach for aptamer screening, allows the selection and identification of in vitro

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Fan, Cong, Ping-pui Wong, and Huiying Zhao. "DStruBTarget: Integrating Binding Affinity with Structure Similarity for Ligand-Binding Protein Prediction." Journal of Chemical Information and Modeling 60, no. 1 (2019): 400–409. http://dx.doi.org/10.1021/acs.jcim.9b00717.

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Bodramoni Balu, Malreddy Vijay Kumar Reddy, Pramod Saini, and Mr. Kadirvelu G. "GNN-Based Drug–Target Binding Affinity Prediction Using Molecular Graphs and Protein Sequences." International Research Journal on Advanced Engineering Hub (IRJAEH) 3, no. 05 (2025): 2221–25. https://doi.org/10.47392/irjaeh.2025.0326.

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The rapid and cost-effective prediction of drug-target interactions (DTIs) is a critical challenge in computational drug discovery. This project presents a novel web-based system that predicts Drug Target Affinity (DTA) using Graph Neural Networks (GNNs) and amino acid sequence embeddings. The model represents drug molecules as molecular graphs derived from SMILES strings and proteins as encoded sequences. A custom GNN architecture processes graph-structured molecular data while a convolutional embedding layer extracts features from protein sequences. The integrated model predicts binding affi

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Usha, Singaravelu, and Samuel Selvaraj. "Prediction of kinase-inhibitor binding affinity using energetic parameters." Bioinformation 12, no. 3 (2016): 172–81. http://dx.doi.org/10.6026/97320630012172.

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Das, Sourav, Michael P. Krein, and Curt M. Breneman. "Binding Affinity Prediction with Property-Encoded Shape Distribution Signatures." Journal of Chemical Information and Modeling 50, no. 2 (2010): 298–308. http://dx.doi.org/10.1021/ci9004139.

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Yugandhar, K., and M. Michael Gromiha. "Protein–protein binding affinity prediction from amino acid sequence." Bioinformatics 30, no. 24 (2014): 3583–89. http://dx.doi.org/10.1093/bioinformatics/btu580.

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O'Donnell, Timothy J., Alex Rubinsteyn, Maria Bonsack, Angelika B. Riemer, Uri Laserson, and Jeff Hammerbacher. "MHCflurry: Open-Source Class I MHC Binding Affinity Prediction." Cell Systems 7, no. 1 (2018): 129–32. http://dx.doi.org/10.1016/j.cels.2018.05.014.

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Wang, Yuxiao, Qihong Jiao, Jingxuan Wang, Xiaojun Cai, Wei Zhao, and Xuefeng Cui. "Prediction of protein-ligand binding affinity with deep learning." Computational and Structural Biotechnology Journal 21 (2023): 5796–806. http://dx.doi.org/10.1016/j.csbj.2023.11.009.

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Lee, Seungyong, and Sanghyun Park. "Protein-Ligand Binding Affinity Prediction Using Protein Modality Alignment." Journal of KIISE 52, no. 5 (2025): 415–23. https://doi.org/10.5626/jok.2025.52.5.415.

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Kaneriya, Arjun, Madhav Samudrala, Harrish Ganesh, James Moran, Somanath Dandibhotla, and Sivanesan Dakshanamurthy. "StructureNet: Physics-Informed Hybridized Deep Learning Framework for Protein–Ligand Binding Affinity Prediction." Bioengineering 12, no. 5 (2025): 505. https://doi.org/10.3390/bioengineering12050505.

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Accurately predicting protein–ligand binding affinity is an important step in the drug discovery process. Deep learning (DL) methods have improved binding affinity prediction by using diverse categories of molecular data. However, many models rely heavily on interaction and sequence data, which impedes proper learning and limits performance in de novo applications. To address these limitations, we developed a novel graph neural network model, called StructureNet (structure-based graph neural network), to predict protein–ligand binding affinity. StructureNet improves existing DL methods by focu

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Suri, Sadhana, and Sivanesan Dakshanamurthy. "IntegralVac: A Machine Learning-Based Comprehensive Multivalent Epitope Vaccine Design Method." Vaccines 10, no. 10 (2022): 1678. http://dx.doi.org/10.3390/vaccines10101678.

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In the growing field of vaccine design for COVID and cancer research, it is essential to predict accurate peptide binding affinity and immunogenicity. We developed a comprehensive machine learning method, ‘IntegralVac,’ by integrating three existing deep learning tools: DeepVacPred, MHCSeqNet, and HemoPI. IntegralVac makes predictions for single and multivalent cancer and COVID-19 epitopes without manually selecting epitope prediction possibilities. We performed several rounds of optimization before integration, then re-trained IntegralVac for multiple datasets. We validated the IntegralVac wi

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Limbu, Sarita, and Sivanesan Dakshanamurthy. "A New Hybrid Neural Network Deep Learning Method for Protein–Ligand Binding Affinity Prediction and De Novo Drug Design." International Journal of Molecular Sciences 23, no. 22 (2022): 13912. http://dx.doi.org/10.3390/ijms232213912.

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Accurately predicting ligand binding affinity in a virtual screening campaign is still challenging. Here, we developed hybrid neural network (HNN) machine deep learning methods, HNN-denovo and HNN-affinity, by combining the 3D-CNN (convolutional neural network) and the FFNN (fast forward neural network) hybrid neural network framework. The HNN-denovo uses protein pocket structure and protein–ligand interactions as input features. The HNN-affinity uses protein sequences and ligand features as input features. The HNN method combines the CNN and FCNN machine architecture for the protein structure

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Akshara, Vinayakrishnan* Malavika K. Aneesha Thomas Aswagosh K. "The Study of Insilco Design and Biological Evaluation of Naphthalene Derivatives." International Journal of Pharmaceutical Sciences 3, no. 1 (2025): 1964–69. https://doi.org/10.5281/zenodo.14724043.

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Naphthalene is an aromatic compound that contain two fused benzene rings. Naphthalene derivatives has diverse biological activities and gained attention as potential therapeutic agents. In this study we applied Insilco drug design techniques to evaluate pharmacokinetic properties, biological activities and binding affinity of 2-(bromomethyl) naphthalene, 8-amino-2-naphthol and acenaphthalene. The molecular structures were created by using King Draw, followed by the prediction of key pharmacokinetic parameters (solubility, permeability, toxicity, etc.) using Swiss ADME. Biological activity pred

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Gim, Mogan, Junseok Choe, Seungheun Baek, et al. "ArkDTA: attention regularization guided by non-covalent interactions for explainable drug–target binding affinity prediction." Bioinformatics 39, Supplement_1 (2023): i448—i457. http://dx.doi.org/10.1093/bioinformatics/btad207.

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Abstract Motivation Protein–ligand binding affinity prediction is a central task in drug design and development. Cross-modal attention mechanism has recently become a core component of many deep learning models due to its potential to improve model explainability. Non-covalent interactions (NCIs), one of the most critical domain knowledge in binding affinity prediction task, should be incorporated into protein–ligand attention mechanism for more explainable deep drug–target interaction models. We propose ArkDTA, a novel deep neural architecture for explainable binding affinity prediction guide

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Sharabi, Oz, Jason Shirian, and Julia M. Shifman. "Predicting affinity- and specificity-enhancing mutations at protein–protein interfaces." Biochemical Society Transactions 41, no. 5 (2013): 1166–69. http://dx.doi.org/10.1042/bst20130121.

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Manipulations of PPIs (protein–protein interactions) are important for many biological applications such as synthetic biology and drug design. Combinatorial methods have been traditionally used for such manipulations, failing, however, to explain the effects achieved. We developed a computational method for prediction of changes in free energy of binding due to mutation that bring about deeper understanding of the molecular forces underlying binding interactions. Our method could be used for computational scanning of binding interfaces and subsequent analysis of the interfacial sequence optima

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Walpoth, Belinda Nazan, and Burak Erman. "Regulation of ryanodine receptor RyR2 by protein-protein interactions: prediction of a PKA binding site on the N-terminal domain of RyR2 and its relation to disease causing mutations." F1000Research 4 (January 28, 2015): 29. http://dx.doi.org/10.12688/f1000research.5858.1.

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Protein-protein interactions are the key processes responsible for signaling and function in complex networks. Determining the correct binding partners and predicting the ligand binding sites in the absence of experimental data require predictive models. Hybrid models that combine quantitative atomistic calculations with statistical thermodynamics formulations are valuable tools for bioinformatics predictions. We present a hybrid prediction and analysis model for determining putative binding partners and interpreting the resulting correlations in the yet functionally uncharacterized interactio

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Ghimire, Ashutosh, Hilal Tayara, Zhenyu Xuan, and Kil To Chong. "CSatDTA: Prediction of Drug–Target Binding Affinity Using Convolution Model with Self-Attention." International Journal of Molecular Sciences 23, no. 15 (2022): 8453. http://dx.doi.org/10.3390/ijms23158453.

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Drug discovery, which aids to identify potential novel treatments, entails a broad range of fields of science, including chemistry, pharmacology, and biology. In the early stages of drug development, predicting drug–target affinity is crucial. The proposed model, the prediction of drug–target affinity using a convolution model with self-attention (CSatDTA), applies convolution-based self-attention mechanisms to the molecular drug and target sequences to predict drug–target affinity (DTA) effectively, unlike previous convolution methods, which exhibit significant limitations related to this asp

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Yuan, Zhen, Xingyu Chen, Sisi Fan, et al. "Binding Free Energy Calculation Based on the Fragment Molecular Orbital Method and Its Application in Designing Novel SHP-2 Allosteric Inhibitors." International Journal of Molecular Sciences 25, no. 1 (2024): 671. http://dx.doi.org/10.3390/ijms25010671.

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The accurate prediction of binding free energy is a major challenge in structure-based drug design. Quantum mechanics (QM)-based approaches show promising potential in predicting ligand–protein binding affinity by accurately describing the behavior and structure of electrons. However, traditional QM calculations face computational limitations, hindering their practical application in drug design. Nevertheless, the fragment molecular orbital (FMO) method has gained widespread application in drug design due to its ability to reduce computational costs and achieve efficient ab initio QM calculati

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Liang, Yigao, Shaohua Jiang, Min Gao, Fengjiao Jia, Zaoyang Wu, and Zhijian Lyu. "GLSTM-DTA: Application of Prediction Improvement Model Based on GNN and LSTM." Journal of Physics: Conference Series 2219, no. 1 (2022): 012008. http://dx.doi.org/10.1088/1742-6596/2219/1/012008.

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Abstract Most prediction models of drug-target binding affinity (DTA) treated drugs and targets as sequences, and feature extraction networks could not sufficiently extract features. Inspired by DeepDTA and GraphDTA, we proposed an improved model named GLSTM-DTA for DTA prediction, which combined Graph Neural Network (GNN) and Long Short-Term Memory Network (LSTM). The feature extraction block consists of two parts: GNN block and LSTM block, which extract drug features and protein features respectively. The novelty of our work is using LSTM, instead of Convolutional neural network (CNN) to ext

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Annala, Matti, Kirsti Laurila, Harri Lähdesmäki, and Matti Nykter. "A Linear Model for Transcription Factor Binding Affinity Prediction in Protein Binding Microarrays." PLoS ONE 6, no. 5 (2011): e20059. http://dx.doi.org/10.1371/journal.pone.0020059.

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Morehead, Alex, Jian Liu, Pawan Neupane, Nabin Giri, and Jianlin Cheng. "Protein‐Ligand Structure and Affinity Prediction in CASP16 Using a Geometric Deep Learning Ensemble and Flow Matching." Proteins: Structure, Function, and Bioinformatics, April 8, 2025. https://doi.org/10.1002/prot.26827.

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ABSTRACTPredicting the structure of ligands bound to proteins is a foundational problem in modern biotechnology and drug discovery, yet little is known about how to combine the predictions of protein‐ligand structure (poses) produced by the latest deep learning methods to identify the best poses and how to accurately estimate the binding affinity between a protein target and a list of ligand candidates. Further, a blind benchmarking and assessment of protein‐ligand structure and binding affinity prediction is necessary to ensure it generalizes well to new settings. Towards this end, we introdu

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Tao, Fangting, Jinyuan Sun, Pengyue Gao, George Fu Gao, and Bian Wu. "Reliable prediction of protein-protein binding affinity changes upon mutations with Pythia-PPI." National Science Review, June 10, 2025. https://doi.org/10.1093/nsr/nwaf231.

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Abstract Protein-protein interactions are essential for numerous biological functions, and predicting binding affinity changes caused by mutations is crucial for understanding the impact of genetic variation and advancing protein engineering. Although machine learning-based methods show promise in improving prediction accuracy, limited experimental data remain a significant bottleneck. In this study, we employed multi-task learning and self-distillation to overcome the data limitation and improve the accuracy of protein-protein binding affinity prediction. By incorporating a mutation stability

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Rahman, Julia, M. A. Hakim Newton, Mohammed Eunus Ali, and Abdul Sattar. "Distance plus attention for binding affinity prediction." Journal of Cheminformatics 16, no. 1 (2024). http://dx.doi.org/10.1186/s13321-024-00844-x.

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AbstractProtein-ligand binding affinity plays a pivotal role in drug development, particularly in identifying potential ligands for target disease-related proteins. Accurate affinity predictions can significantly reduce both the time and cost involved in drug development. However, highly precise affinity prediction remains a research challenge. A key to improve affinity prediction is to capture interactions between proteins and ligands effectively. Existing deep-learning-based computational approaches use 3D grids, 4D tensors, molecular graphs, or proximity-based adjacency matrices, which are

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Seo, Sangmin, Jonghwan Choi, Sanghyun Park, and Jaegyoon Ahn. "Binding affinity prediction for protein–ligand complex using deep attention mechanism based on intermolecular interactions." BMC Bioinformatics 22, no. 1 (2021). http://dx.doi.org/10.1186/s12859-021-04466-0.

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Abstract Background Accurate prediction of protein–ligand binding affinity is important for lowering the overall cost of drug discovery in structure-based drug design. For accurate predictions, many classical scoring functions and machine learning-based methods have been developed. However, these techniques tend to have limitations, mainly resulting from a lack of sufficient energy terms to describe the complex interactions between proteins and ligands. Recent deep-learning techniques can potentially solve this problem. However, the search for more efficient and appropriate deep-learning archi

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Wu, Jialin, Zhe Liu, Xiaofeng Yang, and Zhanglin Lin. "Improved compound–protein interaction site and binding affinity prediction using self-supervised protein embeddings." BMC Bioinformatics 23, no. 1 (2022). http://dx.doi.org/10.1186/s12859-022-05107-w.

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Abstract Background Compound–protein interaction site and binding affinity predictions are crucial for drug discovery and drug design. In recent years, many deep learning-based methods have been proposed for predications related to compound–protein interaction. For protein inputs, how to make use of protein primary sequence and tertiary structure information has impact on prediction results. Results In this study, we propose a deep learning model based on a multi-objective neural network, which involves a multi-objective neural network for compound–protein interaction site and binding affinity

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Shim, Jooyong, Zhen-Yu Hong, Insuk Sohn, and Changha Hwang. "Prediction of drug–target binding affinity using similarity-based convolutional neural network." Scientific Reports 11, no. 1 (2021). http://dx.doi.org/10.1038/s41598-021-83679-y.

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AbstractIdentifying novel drug–target interactions (DTIs) plays an important role in drug discovery. Most of the computational methods developed for predicting DTIs use binary classification, whose goal is to determine whether or not a drug–target (DT) pair interacts. However, it is more meaningful but also more challenging to predict the binding affinity that describes the strength of the interaction between a DT pair. If the binding affinity is not sufficiently large, such drug may not be useful. Therefore, the methods for predicting DT binding affinities are very valuable. The increase in n

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Oršolić, Davor, and Tomislav Šmuc. "Dynamic applicability domain (dAD): compound-target binding affinity estimates with local conformal prediction." Bioinformatics, August 18, 2023. http://dx.doi.org/10.1093/bioinformatics/btad465.

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Abstract Motivation Increasing efforts are being made in the field of machine learning to advance the learning of robust and accurate models from experimentally measured data and enable more efficient drug discovery processes. The prediction of binding affinity is one of the most frequent tasks of compound bioactivity modelling. Learned models for binding affinity prediction are assessed by their average performance on unseen samples, but point predictions are typically not provided with a rigorous confidence assessment. Approaches such as the conformal predictor framework equip conventional m

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