Hot-spot analysis for drug discovery targeting protein-protein interactions

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New insights into protein–protein interaction modulators in drug discovery and therapeutic advance

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Aromatic clusters in protein–protein and protein–drug complexes

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Small molecules targeting protein–protein interactions for cancer therapy

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DDMut-PPI: predicting effects of mutations on protein–protein interactions using graph-based deep learning

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Fragment-based drug discovery supports drugging ‘undruggable’ protein–protein interactions

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Targeting protein-protein interaction interfaces in COVID-19 drug discovery

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Computational methods-guided design of modulators targeting protein-protein interactions (PPIs)

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Machine learning methods for protein-protein binding affinity prediction in protein design

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ProtInteract: A deep learning framework for predicting protein–protein interactions

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Blocker-SELEX: a structure-guided strategy for developing inhibitory aptamers disrupting undruggable transcription factor interactions

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Targeting protein-protein interactions in drug discovery: Modulators approved or in clinical trials for cancer treatment

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Methods for Discovering and Targeting Druggable Protein-Protein Interfaces and Their Application to Repurposing

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An Overview of Computational Methods, Tools, Servers, and Databases for Drug Repurposing

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Docking-based identification of small-molecule binding sites at protein-protein interfaces

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Targeting protein–protein interactions in the DNA damage response pathways for cancer chemotherapy

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Selective fluorescent labeling of cellular proteins and its biological applications

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Protein–Protein Binding Free Energy Predictions with the MM/PBSA Approach Complemented with the Gaussian-Based Method for Entropy Estimation

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Main protease inhibitors and drug surface hotspots for the treatment of COVID-19: A drug repurposing and molecular docking approach

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A Bionic‐Homodimerization Strategy for Optimizing Modulators of Protein–Protein Interactions: From Statistical Mechanics Theory to Potential Clinical Translation

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Molecular Dynamics Simulation-Driven Focused Virtual Screening and Experimental Validation of Inhibitors for MTDH-SND1 Protein–Protein Interaction

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Deciphering the Lexicon of Protein Targets: A Review on Multifaceted Drug Discovery in the Era of Artificial Intelligence

Nandi, S., Bhaduri, S., Das, D., Ghosh, P., Mandal, M., & Mitra, P. (2024) · Molecular Pharmaceutics · doi:10.1021/acs.molpharmaceut.3c01161 · Cited by 28

Prediction and targeting of GPCR oligomer interfaces

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Proteomics for cancer drug design

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Decoding Protein-protein Interactions: An Overview

Slater, O., Miller, B., & Kontoyianni, M. (2020) · Current Topics in Medicinal Chemistry · doi:10.2174/1568026620666200226105312 · Cited by 21

Deep learning-based method for predicting and classifying the binding affinity of protein-protein complexes

Nikam, R., Yugandhar, K., & Gromiha, M. M. (2023) · Biochimica et Biophysica Acta (BBA) - Proteins and Proteomics · doi:10.1016/j.bbapap.2023.140948 · Cited by 21

Targeting protein-protein interactions with low molecular weight and short peptide modulators: insights on disease pathways and starting points for drug discovery

Trisciuzzi, D., Villoutreix, B. O., Siragusa, L., Baroni, M., Cruciani, G., & Nicolotti, O. (2023) · Expert Opinion on Drug Discovery · doi:10.1080/17460441.2023.2218641 · Cited by 19

Obtaining Protein Association Energy Landscape for Integral Membrane Proteins

Rajagopal, N., & Nangia, S. (2019) · Journal of Chemical Theory and Computation · doi:10.1021/acs.jctc.9b00626 · Cited by 19

Conformational Selection Mechanism Provides Structural Insights into the Optimization of APC-Asef Inhibitors

He, X., Huang, N., Qiu, Y., Zhang, J., Liu, Y., Yin, X., & Lu, S. (2021) · Molecules · doi:10.3390/molecules26040962 · Cited by 18

Survivin - caspase protein-protein interaction: Experimental evidence and computational investigations to decipher the hotspot residues for drug targeting

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Crystallographic mining of ASK1 regulators to unravel the intricate PPI interfaces for the discovery of small molecule

Agrahari, A. K., Dikshit, M., & Asthana, S. (2022) · Computational and Structural Biotechnology Journal · doi:10.1016/j.csbj.2022.07.008 · Cited by 18

Application of Nanopore Sensors for Biomolecular Interactions and Drug Discovery

Jeong, K., Kim, J., Dhanasekar, N. N., Lee, M., & Chi, S. (2022) · Chemistry - An Asian Journal · doi:10.1002/asia.202200679 · Cited by 16

In silico identification of small molecule modulators for disruption of Hsp90–Cdc37 protein–protein interaction interface for cancer therapeutic application

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Applications of in Silico Methods for Design and Development of Drugs Targeting Protein-Protein Interactions

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Cyclobutane-bearing restricted anchoring residues enabled geometry-specific hydrocarbon peptide stapling

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Apoptosis Inhibitor 5: A Multifaceted Regulator of Cell Fate

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Using machine‐learning‐driven approaches to boost hot‐spot's knowledge

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Large-Scale Crystallographic Fragment Screening Expedites Compound Optimization and Identifies Putative Protein–Protein Interaction Sites

Barthel, T., Wollenhaupt, J., Lima, G. M. A., Wahl, M. C., & Weiss, M. S. (2022) · Journal of Medicinal Chemistry · doi:10.1021/acs.jmedchem.2c01165 · Cited by 14

ProBAN : Neural network algorithm for predicting binding affinity in protein–protein complexes

Bogdanova, E. A., & Novoseletsky, V. N. (2024) · Proteins Structure Function and Bioinformatics · doi:10.1002/prot.26700 · Cited by 13

Mapping of the FGF14:Nav1.6 complex interface reveals FLPK as a functionally active peptide modulating excitability

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Extraction of molecular features for the drug discovery targeting protein‐protein interaction of Helicobacter pylori CagA and tumor suppressor protein ASSP2

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Structural insights into Beclin 1 interactions with it's regulators for autophagy modulation

Sarmadhikari, D., & Asthana, S. (2025) · Computational and Structural Biotechnology Journal · doi:10.1016/j.csbj.2025.06.044 · Cited by 13

Unleashing the power of DNA-encoded libraries for challenging targets in drug discovery

Du, N., Li, Y., Lou, Q., Zhang, G., Li, Y., & Li, Y. (2026) · Pharmaceutical Science Advances · doi:10.1016/j.pscia.2026.100115 · Cited by 10

Recent developments in the utility of saturated azaheterocycles in peptidomimetics

Singh, M. K., & Lakshman, M. K. (2021) · Organic & Biomolecular Chemistry · doi:10.1039/d1ob01329g · Cited by 9

State-of-the-art strategies to prioritize Mycobacterium tuberculosis drug targets for drug discovery using a subtractive genomics approach

Akinnuwesi, A., Egieyeh, S. A., & Cloete, R. E. A. (2023) · Frontiers in Drug Discovery · doi:10.3389/fddsv.2023.1254656 · Cited by 9

Cold spots are universal in protein–protein interactions

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Electrophile Scanning Reveals Reactivity Hotspots for the Design of Covalent Peptide Binders

Grob, N. M., Remarcik, C., Rössler, S. L., Wong, J. Y. K., Wang, J. C. K., Tao, J., Smith, C. L., Loas, A., Buchwald, S. L., Eaton, D., et al. (2023) · ACS Chemical Biology · doi:10.1021/acschembio.3c00538 · Cited by 9

Reinstating apoptosis using putative Bcl-xL natural product inhibitors: Molecular docking and ADMETox profiling investigations

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Structure-based Virtual screening, Biological Evaluation and Biophysical Study of Novel Mcl-1 Inhibitors

Du, J., Liu, L., Liu, B., Yang, J., Hou, X., Yu, J., & Fang, H. (2020) · Future Medicinal Chemistry · doi:10.4155/fmc-2020-0114 · Cited by 8

Village Regrouping in the Eastern Plains of China: A Perspective on Home-Field Distance

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Monte Carlo simulations using PELE to identify a protein–protein inhibitor binding site and pose

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Structure-informed machine learning for drug discovery: a task-centric perspective

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Effect of Direct and Water-Mediated Interactions on the Identification of Hotspots in Biomolecular Complexes with Multiple Subsystems

Sládek, V., Artiushenko, P. V., & Fedorov, D. G. (2024) · Journal of Chemical Information and Modeling · doi:10.1021/acs.jcim.4c00973 · Cited by 8

Development of R7BP inhibitors through cross-linking coupled mass spectrometry and integrated modeling

Adikaram, P. R., Zhang, J., Kittock, C. M., Pandey, M., Hassan, S. A., Lue, N., Wang, G., Guček, M., & Simonds, W. F. (2019) · Communications Biology · doi:10.1038/s42003-019-0585-1 · Cited by 7

Protein-protein interactions: a structural view of inhibition strategies and the IL-23/IL-17 axis

Allen, S. J., & Lumb, K. J. (2020) · Advances in protein chemistry and structural biology · doi:10.1016/bs.apcsb.2019.12.006 · Cited by 7

Accurate Protein Dynamic Conformational Ensembles: Combining AlphaFold, MD, and Amide 15N(1H) NMR Relaxation

Lesovoy, D., Roshchin, K., Sala, B. M., Sandalova, T., Achour, A., Agback, T., Agback, P., & Orekhov, V. (2025) · International Journal of Molecular Sciences · doi:10.3390/ijms26188917 · Cited by 6

Computational Design of Peptide-Based Binders to Therapeutic Targets

Mulligan, V. K., & Hosseinzadeh, P. (2022) · ACS symposium series · doi:10.1021/bk-2022-1417.ch003 · Cited by 7

Computational methods for predicting hotspots at protein–RNA interfaces

Mei, L., Hao, G., & Yang, G. (2021) · Wiley Interdisciplinary Reviews - RNA · doi:10.1002/wrna.1675 · Cited by 6

Computational Analysis of Protein–Protein Interactions in Motile T-Cells

Kumar, S., Fazil, M. H. U. T., Ahmad, K., Tripathy, M., Rajapakse, J. C., & Verma, N. K. (2019) · Methods in molecular biology · doi:10.1007/978-1-4939-9036-8_18 · Cited by 6

Accurate Protein Dynamic Conformational Ensembles: Combining AlphaFold, MD and Amide 15 N( 1 H) NMR Relaxation

Lesovoy, D., Roshchin, K., Sala, B. M., Sandalova, T., Achour, A., Agback, T., Orekhov, V., & Agback, P. (2025) · doi:10.1101/2025.02.07.637034 · Cited by 6

Laser capture microdissection

Liotta, L. A., Conrads, T. P., Howard, M. A., Araujo, R. P., & Petricoin, E. (2025) · Nature Reviews Methods Primers · doi:10.1038/s43586-025-00453-4 · Cited by 5

Improved prediction of protein-protein interactions by a modified strategy using three conventional docking software in combination

Choi, S., Son, S. H., Kim, M. Y., Na, I., Uversky, V. N., & Kim, C. G. (2023) · International Journal of Biological Macromolecules · doi:10.1016/j.ijbiomac.2023.126526 · Cited by 5

Exploring computational tools for improved structural design and stability of helical AApeptides

Li, T., Song, S., & He, Y. J. (2023) · Supramolecular Materials · doi:10.1016/j.supmat.2023.100038 · Cited by 5

PPI-hotspotID for detecting protein–protein interaction hot spots from the free protein structure

Chen, Y. C., Sargsyan, K., Wright, J. D., Chen, Y., Huang, Y., & Lim, C. (2024) · eLife · doi:10.7554/elife.96643 · Cited by 10

Computational and Experimental Characterization of NF023, A Candidate Anticancer Compound Inhibiting cIAP2/TRAF2 Assembly

Cossu, F., Sorrentino, L., Fagnani, E., Zaffaroni, M., Milani, M., Toni Giorgino, A., & Mastrangelo, E. (2020) · Journal of Chemical Information and Modeling · doi:10.1021/acs.jcim.0c00518 · Cited by 5

Computationally-obtained structural insights into the molecular interactions between Pidilizumab and binding partners DLL1 and PD-1

Albuquerque, A., da Silva, H. C., Sartori, G., & da Silva, J. H. M. (2021) · Journal of Biomolecular Structure and Dynamics · doi:10.1080/07391102.2021.1885492 · Cited by 9

Rewards of divergence in sequences, 3-D structures and dynamics of yeast and human spliceosome SF3b complexes

Yazhini, A., Sandhya, S., & Srinivasan, N. (2021) · Current Research in Structural Biology · doi:10.1016/j.crstbi.2021.05.003 · Cited by 5

Phage transcription activator RinA regulates Staphylococcus aureus virulence by governing sarA expression

Jiang, M., Li, Y., Sun, B., Xu, S., Pan, T. S., & Li, Y. (2022) · Genes & Genomics · doi:10.1007/s13258-022-01352-8 · Cited by 4

Hot-Spot-Guided Generative Deep Learning for Drug-Like PPI Inhibitor Design

Sun, H., Li, J., Zhang, Y., Lin, S., Chen, J., Tan, H., Wang, R., Mao, X., Zhao, J., Li, R., et al. (2025) · Interdisciplinary Sciences Computational Life Sciences · doi:10.1007/s12539-025-00756-w · Cited by 6

Targeting protein–protein interactions with reversible covalent modalities: Non‐cysteine chemistries

Basu, R., & Fletcher, S. (2026) · British Journal of Pharmacology · doi:10.1111/bph.70377 · Cited by 3

Categorization of hotspots into three types – weak, moderate and strong to distinguish protein–protein versus protein–peptide interactions

Kumar, A. K., & Rathore, R. S. (2023) · Journal of Biomolecular Structure and Dynamics · doi:10.1080/07391102.2023.2252077 · Cited by 3

Computed Protein–Protein Enthalpy Signatures as a Tool for Identifying Conformation Sampling Problems

Çınaroğlu, S. S., & Biggin, P. C. (2023) · Journal of Chemical Information and Modeling · doi:10.1021/acs.jcim.3c01041 · Cited by 3

Mycobacterium susceptibility to ivermectin by inhibition of eccD3, an ESX-3 secretion system component.

Granados-Tristán, A. L., Carrillo‐Tripp, M., Hernández-Luna, C. E., Herrera-Rodulfo, A. F., González-Escalante, L. A., Arriaga-Guerrero, A. L., Silva-Ramírez, B., Escobedo-Guajardo, B. L., Mercado-Hernández, R., de León, M. B., et al. (2025) · PLoS Computational Biology · doi:10.1371/journal.pcbi.1012936 · Cited by 3

Revisiting Protein–Protein Docking: A Systematic Evaluation Framework

Jiang, L., Zhang, K., Zhu, K., Wang, Y., Kang, Y., & Hou, T. (2025) · Journal of Chemical Information and Modeling · doi:10.1021/acs.jcim.5c01399 · Cited by 3

Prediction of protein interactions with function in protein (de-)phosphorylation

Vagiona, A.-C., Notopoulou, S., Zdráhal, Z., Gonçalves-Kulik, M., Petrakis, S., & Andrade-Navarro, M. A. (2025) · PLoS ONE · doi:10.1371/journal.pone.0319084 · Cited by 4

Drug Design Methods to Regulate Protein–Protein Interactions

Poluri, K. M., Gulati, K., Tripathi, D. K., & Nagar, N. (2023) · Springer eBooks · doi:10.1007/978-981-99-2423-3_6 · Cited by 2

Deep learning identification of novel autophagic protein-protein interactions and experimental validation of Beclin 2-Ubiquilin 1 axis in triple-negative breast cancer

Wang, H., Xie, X., Li, X., Huang, W. J., Wu, L., JIN, W., & Liu, B. (2024) · Oncology Research Featuring Preclinical and Clinical Cancer Therapeutics · doi:10.32604/or.2024.055921 · Cited by 2

MENSAdb: a thorough structural analysis of membrane protein dimers

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Target-specific design of drug-like PPI inhibitors via hot-spot-guided generative deep learningPreprint

Sun, H., Li, J., Zhang, Y., Lin, S., Chen, J., Tan, H., Wang, R., Mao, X., Zhao, J., Li, R., et al. (2024) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/2024.10.29.620869 · Cited by 2

Druggability assessments of small peptides as protein–protein interaction inhibitors targeting EED-EZH2 binding within the Polycomb Repressive Complex 2 (PRC2), an epigenetic regulator

Kundil, V. T., Suresh, S. C., Chellappan, S., Kizhakkepurayil, A., Basheer, S. M., & Gangadharan, A. K. (2025) · Molecular Diversity · doi:10.1007/s11030-025-11321-4 · Cited by 1

A hybrid DNN model using novel integrated interface features for predicting protein-protein complexes binding affinity

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Modelling eNvironment for Isoforms (MoNvIso): A general platform to predict structural determinants of protein isoforms in genetic diseases

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