Search · four archives

Results for binding affinity prediction

or ask it as a questionAsk the stacks →

ArchivesarXiv PubMed bioRxiv chemRxiv

WhenAny time Past month Past year Past 5 years

OrderMost relevant Most cited Newest

23 papers · ranked by Valyu relevance

01
AarXiv16 Sep 2019
OnionNet: a Multiple-Layer Intermolecular-Contact-Based Convolutional Neural Network for Protein–Ligand Binding Affinity Prediction
Liangzhen Zheng, Jingrong Fan, Yuguang Mu
Computational drug discovery provides an efficient tool helping large scale lead molecules screening. One of the major tasks of lead discovery is identifying molecules with promising binding affinities towards a target, a protein in general. The accuracies of current scoring functions which are used to predict the…
97relevance
02
PPubMed10 Jun 2026
Structural Knowledge Is What Matters in Protein–Ligand Binding Affinity Prediction
Natàlia Segura-Alabart, Francesc Serratosa, Gyorgy M. Keseru
Binding affinity prediction is about estimating the degree to which a drug binds to a protein. Predicting the binding affinity between a drug and a protein in a computational process helps researchers filter huge libraries of compounds before performing expensive biochemical lab experiments. Currently, there is…
94relevance
03
BbioRxiv1 Nov 2025
ProAffinity++
Keyu Xu, Zeyuan Di, Jianquan Zhao, Haicang Zhang + 1 more
Proteins are essential biological macromolecules that play a crucial role in living organisms. Protein-protein interactions, which govern various biological processes such as signal transduction, cell metabolism, and cell growth, are key aspect of protein function. The strength of these interactions, characterized by…
93relevance
04
PPubMed25 Nov 2020
ISLAND: in-silico proteins binding affinity prediction using sequence information
Wajid Arshad Abbasi, Adiba Yaseen, Fahad Ul Hassan, Saiqa Andleeb + 1 more
'Fayyaz Ul Amir Afsar Minhas'] Background Determining binding affinity in protein-protein interactions is important in the discovery and design of novel therapeutics and mutagenesis studies. Determination of binding affinity of proteins in the formation of protein complexes requires sophisticated, expensive and…
92relevance
05
BbioRxiv18 Mar 2021
Binding affinity prediction for protein-ligand complex using deep attention mechanism based on intermolecular interactions
Sangmin Seo, Jonghwan Choi, Sanghyun Park, Jaegyoon Ahn
Accurate prediction of protein-ligand binding affinity is important in that it can lower the overall cost of drug discovery in structure-based drug design. For more accurate prediction, many classical scoring functions and machine learning-based methods have been developed. However, these techniques tend to have…
92relevance
06
BbioRxiv13 Jun 2026
ProtAff: Protein Binding Affinity Prediction via LoRA-Finetuned ESM-2
Lee-Shin Chu, Jeff Vogt, Michael Chungyoun, Jeffrey J. Gray
Predicting the binding affinity of protein–protein interactions remains a central challenge in computational biology. Structure prediction models such as AlphaFold3 (AF3) and Boltz-2 can produce high-quality docking poses, and their confidence scores indicate structure quality, but these same scores fail to rank…
92relevance
07
BbioRxiv19 Apr 2024
Protein-ligand binding affinity prediction: Is 3D binding pose needed?
Ming-Hsiu Wu, Ziqian Xie, Degui Zhi
Accurate protein-ligand binding affinity prediction is crucial in drug discovery. Existing methods are predominately docking-free, without explicitly considering atom-level interaction between proteins and ligands in scenarios where crystallized protein-ligand binding conformations are unavailable. Now, with…
91relevance
08
AarXiv29 Sep 2024
Binding Affinity Prediction: From Conventional to Machine Learning-Based Approaches
Xuefeng Liu, Songhao Jiang, Xiaotian Duan, Archit Vasan + 7 more
Approaches Authors: ['Xuefeng Liu' 'Songhao Jiang' 'Xiaotian Duan' 'Archit Vasan' 'C. Liu' 'Chih-chan Tien' 'Heng Ma' 'Thomas Brettin' 'Fangfang Xia' 'Ian Foster' 'Rick Stevens'] Protein-ligand binding is the process by which a small molecule (drug or inhibitor) attaches to a target protein. The binding affinity, which…
90relevance
09
CchemRxiv23 May 2019
Learning from the Ligand: Using Ligand-Based Features to Improve Binding Affinity Prediction
Fergus Boyles, Charlotte M Deane, Garrett Morris
Machine learning scoring functions for protein-ligand binding affinity prediction have been found to consistently outperform classical scoring functions. Structure-based scoring functions for universal affinity prediction typically use features describing interactions derived from the protein-ligand complex, with…
90relevance
10
BbioRxiv17 Nov 2023
Predicting Affinity Through Homology (PATH): Interpretable Binding Affinity Prediction with Persistent Homology
Yuxi Long, Bruce R. Donald
Accurate binding affinity prediction is crucial to structure-based drug design. Recent work used computational topology to obtain an effective representation of protein-ligand interactions. While algorithms using algebraic topology have proven useful in predicting properties of biomolecules, previous algorithms…
90relevance
11
PPubMed16 Dec 2022
Machine learning methods for protein-protein binding affinity prediction in protein design Guo and Yamaguchi 10.3389/fbinf.2022.1065703
Zhongliang Guo, Rui Yamaguchi
Protein-protein interactions govern a wide range of biological activity. A proper estimation of the protein-protein binding affinity is vital to design proteins with high specificity and binding affinity toward a target protein, which has a variety of applications including antibody design in immunotherapy, enzyme…
90relevance
12
AarXiv1 Nov 2019
DeepAtom: A Framework for Protein-Ligand Binding Affinity Prediction
Yanjun Li, Mohammad Rezaei, Chenglong Li, Xiaolin Li
—The cornerstone of computational drug design is the calculation of binding affinity between two biological counterparts, especially a chemical compound, i.e., a ligand, and a protein. Predicting the strength of protein-ligand binding with reasonable accuracy is critical for drug discovery. In this paper, we propose a…
90relevance
13
AarXiv10 Jul 2024
SPIN: SE(3)-Invariant Physics Informed Network for Binding Affinity Prediction
Seungyeon Choi, Sangmin Seo, Sanghyun Park
Prediction Authors: ['Seungyeon Choi' 'Sangmin Seo' 'Sanghyun Park'] Abstract. Accurate prediction of protein-ligand binding affinity is crucial for rapid and efficient drug development. Recently, the importance of predicting binding affinity has led to increased attention on research that models the three-dimensional…
89relevance
14
PPubMed9 Dec 2015
Characterizing informative sequence descriptors and predicting binding affinities of heterodimeric protein complexes
Yerukala Sathipati Srinivasulu, Jyun-Rong Wang, Kai-Ti Hsu, Ming-Ju Tsai + 4 more
'Ming-Ju Tsai' 'Phasit Charoenkwan' 'Wen-Lin Huang' 'Hui-Ling Huang' 'Shinn-Ying Ho'] Background Protein-protein interactions (PPIs) are involved in various biological processes, and underlying mechanism of the interactions plays a crucial role in therapeutics and protein engineering. Most machine learning approaches…
89relevance
15
AarXiv25 Nov 2020
ISLAND: in-silico proteins binding affinity prediction using sequence information
Wajid Arshad Abbasi, Adiba Yaseen, Fahad Hassan, Saiqa Andleeb + 1 more
'Fayyaz Minhas'] Determination of binding affinity of proteins in the formation of protein complexes requires sophisticated, expensive and time-consuming experimentation which can be replaced with computational methods. Most computational prediction techniques require protein structures which limit their applicability…
89relevance
16
PPubMed15 Nov 2018
Learning protein binding affinity using privileged information
Wajid Arshad Abbasi, Amina Asif, Asa Ben-Hur, Fayyaz ul Amir Afsar Minhas
'Fayyaz ul Amir Afsar Minhas'] Background Determining protein-protein interactions and their binding affinity are important in understanding cellular biological processes, discovery and design of novel therapeutics, protein engineering, and mutagenesis studies. Due to the time and effort required in wet lab…
87relevance
17
CchemRxiv27 Jan 2021
Learning from Docked Ligands: Ligand-Based Features Rescue Structure-Based Scoring Functions When Trained On Docked Poses
Fergus Boyles, Charlotte M Deane, Garrett Morris
Machine learning scoring functions for protein-ligand binding affinity have been found to consistently outperform classical scoring functions when trained and tested on crystal structures of bound protein-ligand complexes. However, it is less clear how these methods perform when applied to docked poses of complexes. We…
85relevance
18
CchemRxiv22 May 2024
Assessment of molecular dynamics time series descriptors in protein-ligand affinity prediction.
Jakub Poziemski, Artur Yurkevych, Pawel Siedlecki
The advancement of computational methods in drug discovery, particularly through the use of machine learning (ML) and deep learning (DL), has significantly enhanced the precision of binding affinity predictions. Despite progress in computer-aided drug discovery (CADD) accurate prediction of binding affinity remains a…
84relevance
19
CchemRxiv24 Jun 2019
Comparison of Ligand Affinity Ranking Using AutoDock-GPU and MM-GBSA Scores in the D3R Grand Challenge 4
Léa El Khoury, Diogo Santos-Martins, Sukanya Sasmal, Jérome Eberhardt + 6 more
Molecular docking has been successfully used in computer-aided molecular design projects for the identification of ligand poses within protein binding sites. However, relying on docking scores to rank different ligands with respect to their experimental affinities might not be sufficient. It is believed that the…
82relevance
20
AarXiv2 Jun 2021
PANDA: Predicting the change in proteins binding affinity upon mutations by finding a signal in primary structures
Wajid Arshad Abbasi, Syed Ali Abbas, Saiqa Andleeb
Accurately determining a change in protein binding affinity upon mutations is important for the discovery and design of novel therapeutics and to assist mutagenesis studies. Determination of change in binding affinity upon mutations requires sophisticated, expensive, and time-consuming wet-lab experiments that can be…
79relevance
21
CchemRxiv26 Sep 2024
Computational Methods in Drug Discovery and Development
Authors not listed
The rapid advancements in computational methods have revolutionized drug discovery and development. These methods, ranging from molecular modelling to machine learning algorithms, have drastically increased in number and sophistication. However, a comprehensive understanding of these diverse approaches is essential for…
76relevance
22
CchemRxiv24 Sep 2021
AtomNet PoseRanker: Enriching Ligand Pose Quality for Dynamic Proteins in Virtual High Throughput Screens
Kate Stafford, Brandon M. Anderson, Jon Sorenson, Henry van den Bedem
Structure-based, virtual High Throughput Screening (vHTS) methods for predicting ligand activity in drug discovery are important when there are no or relatively few known compounds that interact with a therapeutic target of interest. State-of-the-art computational vHTS necessarily relies on effective methods for pose…
76relevance
23
CchemRxiv24 Aug 2023
In silico analysis of correlation between physicochemical properties of ligands and binding affinity to protein targets
Md. Aktar Hossain, Saima Sultana
In silico analysis is a powerful technique to identify better therapeutic interventions. Molecular docking is widely used to screen ligands through analysing binding affinities for target receptors. In this study we screened ligands for two proteins which are potential drug targets: deoxyuridine triphosphate…
74relevance

Results for binding affinity prediction

OnionNet: a Multiple-Layer Intermolecular-Contact-Based Convolutional Neural Network for Protein–Ligand Binding Affinity Prediction

Structural Knowledge Is What Matters in Protein–Ligand Binding Affinity Prediction

ProAffinity++

ISLAND: in-silico proteins binding affinity prediction using sequence information

Binding affinity prediction for protein-ligand complex using deep attention mechanism based on intermolecular interactions

ProtAff: Protein Binding Affinity Prediction via LoRA-Finetuned ESM-2

Protein-ligand binding affinity prediction: Is 3D binding pose needed?

Binding Affinity Prediction: From Conventional to Machine Learning-Based Approaches

Learning from the Ligand: Using Ligand-Based Features to Improve Binding Affinity Prediction

Predicting Affinity Through Homology (PATH): Interpretable Binding Affinity Prediction with Persistent Homology

Machine learning methods for protein-protein binding affinity prediction in protein design Guo and Yamaguchi 10.3389/fbinf.2022.1065703

DeepAtom: A Framework for Protein-Ligand Binding Affinity Prediction

SPIN: SE(3)-Invariant Physics Informed Network for Binding Affinity Prediction

Characterizing informative sequence descriptors and predicting binding affinities of heterodimeric protein complexes

ISLAND: in-silico proteins binding affinity prediction using sequence information

Learning protein binding affinity using privileged information

Learning from Docked Ligands: Ligand-Based Features Rescue Structure-Based Scoring Functions When Trained On Docked Poses

Assessment of molecular dynamics time series descriptors in protein-ligand affinity prediction.

Comparison of Ligand Affinity Ranking Using AutoDock-GPU and MM-GBSA Scores in the D3R Grand Challenge 4

PANDA: Predicting the change in proteins binding affinity upon mutations by finding a signal in primary structures

Computational Methods in Drug Discovery and Development

AtomNet PoseRanker: Enriching Ligand Pose Quality for Dynamic Proteins in Virtual High Throughput Screens

In silico analysis of correlation between physicochemical properties of ligands and binding affinity to protein targets

OnionNet: a Multiple-Layer Intermolecular-Contact-Based Convolutional Neural Network for Protein–Ligand Binding Affinity Prediction

Structural Knowledge Is What Matters in Protein–Ligand Binding Affinity Prediction

ProAffinity++

ISLAND: in-silico proteins binding affinity prediction using sequence information

Binding affinity prediction for protein-ligand complex using deep attention mechanism based on intermolecular interactions

ProtAff: Protein Binding Affinity Prediction via LoRA-Finetuned ESM-2

Protein-ligand binding affinity prediction: Is 3D binding pose needed?

Binding Affinity Prediction: From Conventional to Machine Learning-Based Approaches

Learning from the Ligand: Using Ligand-Based Features to Improve Binding Affinity Prediction

Predicting Affinity Through Homology (PATH): Interpretable Binding Affinity Prediction with Persistent Homology

Machine learning methods for protein-protein binding affinity prediction in protein design Guo and Yamaguchi 10.3389/fbinf.2022.1065703

DeepAtom: A Framework for Protein-Ligand Binding Affinity Prediction

SPIN: SE(3)-Invariant Physics Informed Network for Binding Affinity Prediction

Characterizing informative sequence descriptors and predicting binding affinities of heterodimeric protein complexes

ISLAND: in-silico proteins binding affinity prediction using sequence information

Learning protein binding affinity using privileged information

Learning from Docked Ligands: Ligand-Based Features Rescue Structure-Based Scoring Functions When Trained On Docked Poses

Assessment of molecular dynamics time series descriptors in protein-ligand affinity prediction.

Comparison of Ligand Affinity Ranking Using AutoDock-GPU and MM-GBSA Scores in the D3R Grand Challenge 4

PANDA: Predicting the change in proteins binding affinity upon mutations by finding a signal in primary structures

Computational Methods in Drug Discovery and Development

AtomNet PoseRanker: Enriching Ligand Pose Quality for Dynamic Proteins in Virtual High Throughput Screens

In silico analysis of correlation between physicochemical properties of ligands and binding affinity to protein targets