Literature Corner

Main Reference
Da Silva, F., Desaphy, J. and Rognan D.
IChem: A Versatile Toolkit for Detecting, Comparing, and Predicting Protein-Ligand Interactions
ChemMedChem, 2018, 13, 507-510.
DOI: 10.1002/cmdc.201700505

Basic Concepts

Protein-ligand interaction fingerprints and graphs

Desaphy, J., Ducrot, P., Raimbaud, E. and Rognan, D.
Encoding protein-ligand interaction patterns in fingerprints and graphs.
J. Chem. Inf. Model, 2013, 53, 623-637.
DOI: 10.1021/ci300566n
Marcou, G. and Rognan, D.
Optimizing fragment and scaffold docking by use of molecular interaction fingerprints.
J. Chem. Inf. Model., 2007, 47, 195-207.
DOI: 10.1021/ci600342e

Cavity detection, comparisons and druggability estimate

Tran-Nguyen, V.K., Da Silva, F., Bret, G. and Rognan, D.
All in One: Cavity Detection, Druggability Estimate, Cavity-Based Pharmacophore Perception and Virtual Screening.
J. Chem. Inf. Model., 2019, 59, 573-585.
DOI: 10.1021/acs.jcim.8b00684
Desaphy, J., Azdimousa, K., and Rognan, D.
Comparison and druggability prediction of protein-ligand binding pockets from pharmacophore-annotated shapes.
J. Chem. Inf. Model., 2012, 52, 2287-2299.
DOI: 10.1021/ci300184x

Protein-Protein Interfaces

Da Silva, F., Bret, G., Teixeira, L., Gonzalez, C.F. and Rognan, D.
Exhaustive repertoire of druggable cavities at protein-protein interfaces of known three-dimensional structure.
J. Med. Chem, 2019, 62, 9732-9742.
DOI: 10.1021/acs.jmedchem.9b01184
Da Silva, F., Desaphy, J., Bret, G. and Rognan, D.
IChemPIC: A Random Forest Classifier of Biological and Crystallographic Protein-Protein Interfaces.
J. Chem. Inf. Model, 2015, 55, 2005−2014.
DOI: 10.1021/acs.jcim.5b00190

Fragment-based drug design

Desaphy, J. and Rognan, D.
scPDBFrag: a database of protein-ligand interaction patterns for bioisosteric replacements.
J.Chem. Inf. Model., 2014, 54, 1908-1918.
DOI: 10.1021/ci500282c

Scoring functions

Volkov, M., Turk, A.J., Drizard, N., Martin, M., Hoffmann, B. Gaston-Mathé, Y. and Rognan, D.
On the frustration to predict binding affinities from protein-ligand structures with deep neural networks.
J. Med. Chem., 2022, 65, 7946–7958.
DOI: 10.1021/acs.jmedchem.2c00487
Tran-Nguyen, V.-K., Bret, G. and Rognan, D.
Accuracy of fast scoring functions to predict high-throughput screening data from docking poses: The simpler the better.
J. Chem. Inf. Model., 2021, 67, 2788-2797.
DOI: 10.1021/acs.jcim.1c00292
Tran-Nguyen, V.-K., Jacquemard, C. and Rognan D.
LIT-PCBA: An unbiased dataset for machine learning and virtual screening.
J. Chem. Inf. Model., 2020, 60, 4263-4273.
DOI: 10.1021/acs.jcim.0c00155
Jacquemard, C., Tran-Nguyen, V. K., Drwal, M. N., Rognan, D., & Kellenberger, E.
Local Interaction Density (LID), a Fast and Efficient Tool to Prioritize Docking Poses.
Molecules, 2019, 24, 2610.
DOI: 10.3390/molecules24142610
Gabel, J., Desaphy, J. and Rognan, D.
Beware of machine learning-based scoring functions - On the danger of developing black boxes.
J. Chem. Inf. Model, 2014, 54, 2807−2815.
DOI: 10.1021/ci500406k

GPCRs Classification

Koensgen, F., Da Silva, F., Rognan, D. and Kellenberger, E.
Unsupervised Classification of G-Protein Coupled Receptors and Their Conformational States Using IChem Intramolecular Interaction Patterns.
J. Chem. Inf. Model., 2019, 59, 3611-3618.
DOI: 10.1021/acs.jcim.9b00054

Virtual Screening

Kaur, B., Sindt, F., Zhang, L.F., Rognan, D. and Gabr, M.
Structure-Guided Discovery of CHI3L1 Inhibitors from Ultralarge Chemical Spaces for Glioblastoma Therapy.
ACS Omega, 2026, 11, 1317-1322.
DOI: 10.1021/acsomega.5c08775
Eguida, M., Bret, G., Sindt, F., Li, F.L., Chau, I., Ackloo, S., Arrowsmith, C., Bolotokova, A., Ghiabi, P., Gibson, E., Halabelian, L., Houliston, S., Harding, R.J., Hutchinson, A., Loppnau, P., Perveen, S., Seitova, A., Zeng, H., Schapira, M. and Rognan, D.
Subpocket Similarity-Based Hit Identification for Challenging Targets: Application to the WDR Domain of LRRK2.
J. Chem. Inf. Model., 2024, 64, 5344-5355.
DOI: 10.1021/acs.jcim.4c00601
Sindt, F., Seyller, A., Eguida, M., Rognan, D.
Protein Structure-Based Organic Chemistry-Driven Ligand Design from Ultralarge Chemical Spaces.
ACS Cent. Sci., 2024, 10, 615-627.
DOI: 10.1021/acscentsci.3c01521
Eguida, M., Schmitt-Valencia, C., Hibert, M., Villa, P. and Rognan, D.
Target-Focused Library Design by Pocket-Applied Computer Vision and Fragment Deep Generative Linking.
J. Med. Chem., 2022, 65, 13771-13783.
DOI: 10.1021/acs.jmedchem.2c00931
da Silva Figueiredo Celestino Gomes, P., Da Silva, F., Bret, G. and Rognan, D.
Ranking docking poses by graph matching of protein-ligand interactions: lessons learned from the D3R Grand Challenge 2.
J. Comput.-Aided Mol. Des., 2018, 32, 75-87.
DOI: 10.1007/s10822-017-0046-1
Slynko, I., Da Silva, F., Bret, G. and Rognan, D.
Docking pose selection by interaction pattern graph similarity: application to the D3R grand challenge 2015.
J. Comput.-Aided Mol. Des., 2016, 30, 669-683.
DOI: 10.1007/s10822-016-9930-3
Chalopin, M., Tesse, A., Martinez, M.C., Rognan, D., Arnal, J.-F. And Andriantsitohaina, R.
Estrogen receptor alpha as a key target of red wine polyphenols action on the endothelium.
PLoS One, 2010, 5, e8554.
DOI: 10.1371/journal.pone.0008554

Applications from Third Parties

Zhao, Z. and Bourne, P.E.
Deciphering covalent kinase inhibitor binding landscape through structural kinome profiling.
Eur. J. Med. Chem., 2026, 312, 118872.
DOI: 10.1016/j.ejmech.2026.118872
Fajardo-Diaz, E., Bignon, E., Dehez, F., Karami, Y. and Gonzalez-Aleman, R.
InterMap: Accelerated Detection of Interaction Fingerprints on Large-Scale Molecular Ensembles.
J. Chem. Theory Comput., 2026.
DOI: 10.1021/acs.jctc.6c00083
Lei, LX., Guo, Q.J., Liu, W., Wang, Z.J., Han, K.T., Shi, C.J., Li, Z.X., Lu, S.C., Wang, M.Q., Zhang, Z.W., Dai, RY., Wang, ZH. and Liu, XY.
A novel deep learning framework for predicting protein-ligand interaction fingerprints from sequence data: integrating graph inductive bias transformer with Kolmogorov-Arnold networks.
Comput. Toxicol., 2025, 36, 100386.
DOI: 10.1016/j.comtox.2025.100386
Orlandi, M., Geng, YQ., Macchiagodena, M., Pagliai, M. and Procacci, P.
Solvation Free Energies of Drug-like Molecules via Fast Growth in an Explicit Solvent: Assessment of the AM1-BCC, RESP/HF/6-31G*, RESP-QM/MM, and ABCG2 Fixed-Charge Approaches.
J. Chem. Theory Comput., 2025, 21, 7977-7990.
DOI: 10.1021/acs.jctc.5c00749
Aniceto, N., Martinho, N., Rufino, I. and Guedes, RC.
LigExtract: Large-scale Automated Identification of Ligands from Protein Structures in the Protein Data Bank.
Genomics, Proteomics & Bioinformatics, 2025, 23, qzaf018.
DOI: 10.1093/gpbjnl/qzaf018
Zillmer, H. and Walther, D.
Towards a comprehensive view of the pocketome universe-biological implications and algorithmic challenges.
PLoS Comput. Biol., 2025, 21, e1013298.
DOI: 10.1371/journal.pcbi.1013298
Errington, D., Schneider, C., Bouysset, C. and Dreyer, F.A.
Assessing interaction recovery of predicted protein-ligand poses.
J. Cheminform., 2025, 17, 76.
DOI: 10.1186/s13321-025-01011-6
Mareuil, F., Torchet, R., Ruano, .LC., Mallet, V., Nilges, M., Bouvier, G. and Sperandio, O.
InDeepNet: a web platform for predicting functional binding sites in proteins using InDeep.
Nucleic Acids Res., 2025, 53, W324-W329.
DOI: 10.1093/nar/gkaf403
Fellinger, C., Seidel, T., Merget, B., Schleifer, K. J., & Langer, T.
GRADE and X-GRADE: Unveiling Novel Protein–Ligand Interaction Fingerprints Based on GRAIL Scores.
J. Chem. Inf. Model., 2025, 65, 2456-2475.
DOI: 10.1021/acs.jcim.4c01902
Strauss, A., Gonzalez-Hernandez, A.J., Lee, J., Abreu, N., Selvakumar, P., Salas-Estrada, L., Kristt, M., Arefin, A., Huynh, K., Marx, DC., Gilliland, K., Melancon, BJ., Filizola, M., Meyerson, J. and Levitz, J.
Structural basis of positive allosteric modulation of metabotropic glutamate receptor activation and internalization.
Nat. Commun., 2024, 15, 50548-x.
DOI: 10.1038/s41467-024-50548-x
Zhou, R.F., Fan, J., Li, S.S., Zeng, W.J., Chen, Y.L., Zheng, X.S., Chen, H.Y. and Liao, J.
LVPocket: integrated 3D global-local information to protein binding pockets prediction with transfer learning of protein structure classification.
J. Cheminform., 2024, 16, 79.
DOI: 10.1186/s13321-024-00871-8
Imbernon, J.R., Weibel, J.M., Ennifar, E., Prevost, G. and Kellenberger, E.
Structural analysis of neomycin B and kanamycin A binding Aminoglycosides Modifying Enzymes (AME) and bacterial ribosomal RNA.
Mol. Inform., 2024, 43, e202300339.
DOI: 10.1002/minf.202300339
Fu, T.T., Zhang, H.X. and Zheng, Q.C.
Assessing the role of residue Phe108 of cytochrome P450 3A4 in allosteric effects of midazolam metabolism.
Phys. Chem. Chem. Phys., 2024, 26, 8807-8814.
DOI: 10.1039/d3cp05270b
Lin, X.L., Yang, W.L., Chen, Y.Y., Liao, X.W., Wu, Z.M. and Zhang, X.L.
ResPocket: A Multi-Scale Feature Fusion Method for Improving Protein Binding Site Detection.
IEEE Int. Conf. Bioinf. Biomed., 2024, 1588-1591.
DOI: 10.1109/BIBM62325.2024.10822324
Li, Z., Huang, R., Xia, M., Patterson, T. A., & Hong, H.
Fingerprinting Interactions between Proteins and Ligands for Facilitating Machine Learning in Drug Discovery.
Biomolecules, 2024, 14, 72.
DOI: 10.3390/biom14010072
Mastropietro, A., Pasculli, G. and Bajorath, J.
Learning characteristics of graph neural networks predicting protein-ligand affinities.
Nat. Mach. Intel., 2023, 5, 1427-1436.
DOI: 10.1038/s42256-023-00756-9
Chiesa, L., Sick, E. and Kellenberger, E.
Predicting the duration of action of β2-adrenergic receptor agonists: Ligand and structure-based approaches.
Mol. Inform., 2023, 42, e202300141.
DOI: 10.1002/minf.202300141
Libouban, P.Y., Aci-Sèche, S., Gómez-Tamayo, J.C., Tresadern, G. and Bonnet, P.
The Impact of Data on Structure-Based Binding Affinity Predictions Using Deep Neural Networks.
Int. J. Mol. Sci., 2023, 24, 16120.
DOI: 10.3390/ijms242216120
Tran-Nguyen, V.K., Junaid, M., Simeon, S. and Ballester, P.J.
A practical guide to machine-learning scoring for structure-based virtual screening.
Nat. Protoc., 2023, 18, 3460-3511.
DOI: 10.1038/s41596-023-00885-w
Zhao, Z. and Bourne, P.E.
How Ligands Interact with the Kinase Hinge.
ACS Med. Chem. Lett., 2023, 14, 1503-1508.
DOI: 10.1021/acsmedchemlett.3c00212
Salas-Estrada, L., Provasi, D., Qiu, X., Kaniskan, H.Ü., Huang, X.P., DiBerto, JF., Ribeiro, J.M.L., Jin, J., Roth, B.L. and Filizola, M.
De Novo Design of κ-Opioid Receptor Antagonists Using a Generative Deep-Learning Framework.
J. Chem. Inf. Model., 2023, 63, 5056-5065.
DOI: 10.1021/acs.jcim.3c00651
Berenger, F. and Tsuda, K.
3D-Sensitive Encoding of Pharmacophore Features.
J. Chem. Inf. Model., 2023, 63, 2360-+2369.
DOI: 10.1021/acs.jcim.2c01623
Zhao, Z., Bohidar, N. and Bourne, P.E.
Analysis of KRAS-Ligand Interaction Modes and Flexibilities Reveals the Binding Characteristics.
J. Chem. Inf. Model., 2023, 63, 1362-1370.
DOI: 10.1021/acs.jcim.3c00097
Briand, M.A., Dreano, L., Legehar, A., Grazhdankin, E., Ghemtio, L. and Xhaard, H.
Exploring cooperative molecular contacts using a PostgreSQL database system.
Mol. Inform., 2023, 42, e202200235.
DOI: 10.1002/minf.202200235
Imbernon, J.R., Chiesa, L. and Kellenberger, E.
Mining the Protein Data Bank to inspire fragment library design.
Front. Chem., 2023, 11.
DOI: 10.3389/fchem.2023.1089714
Zhang, H., Luo, Q.Q., Hu, M.L., Wang, N., Qi, H.Z., Zhang, H.R. and Ding, L.
Discovery of potent microtubule-destabilizing agents targeting for colchicine site by virtual screening, biological evaluation, and molecular dynamics simulation.
Eur. J. Pharm. Sci., 2023, 180, 106340.
DOI: 10.1016/j.ejps.2022.106340
Wágner, G., Mocking, T.A.M., Ma, X.Y., Slynko, I., Pereira, D.D., Breeuwer, R., Rood, N.J.N., van der Horst, C., Vischer, H.F., de Graaf, C., de Esch, I.J.P., Wijtmans, M. and Leurs, R.
SAR exploration of the non-imidazole histamine H3 receptor ligand ZEL-H16 reveals potent inverse agonism.
Arch Pharm (Weinheim), 2023, 356, e2200451.
DOI: 10.1002/ardp.202200451
Shulga, D.A., Tserkovnikova, N.A., Tarasov, D.N. and Tovbin, D.G.
Investigation of the tight binding mechanism of a new anticoagulant DD217 to factor Xa by means of molecular docking and molecular dynamics.
J. Biomol. Struct. Dyn., 2023, 41, 4723-4734.
DOI: 10.1080/07391102.2022.2072387
Aggarwal, R., Gupta, A., Chelur, V., Jawahar, C. and Priyakumar, U.D.
DeepPocket: Ligand Binding Site Detection and Segmentation using 3D Convolutional Neural Networks.
J. Chem. Inf. Model., 2022, 62, 5069-5079.
DOI: 10.1021/acs.jcim.1c00799
Ahn, S., Lee, SE. and Kim, M.H.
Random-forest model for drug-target interaction prediction via Kullbeck-Leibler divergence.
J. Cheminform., 2022, 14, 67.
DOI: 10.1186/s13321-022-00644-1
Zhao, Z. and Bourne, P.E.
Harnessing systematic protein-ligand interaction fingerprints for drug discovery.
Drug. Discov. Today, 2022, 27, 103313.
DOI: 10.1016/j.drudis.2022.07.004
Shi, M.S., Chen, T., Wei, S.P., Zhao, C.Y., Zhang, X.Y., Li, X.H., Tang, X.Y., Liu, Y., Yang, Z. and Chen, L.J.
Molecular Docking, Molecular Dynamics Simulations, and Free Energy Calculation Insights into the Binding Mechanism between VS-4718 and Focal Adhesion Kinase.
ACS Omega, 2022, 36, 32442–32456.
DOI: 10.1021/acsomega.2c03951
Chelur, V.R. and Priyakumar, U.D.
BiRDS - Binding Residue Detection from Protein Sequences Using Deep ResNets.
J. Chem. Inf. Model., 2022, 62, 1809-1818.
DOI: 10.1021/acs.jcim.1c00972
Mao, J., Luo, Q.Q., Zhang, H.R., Zheng, X.H., Shen, C., Qi, H.Z., Hu, M.L. and Zhang, H.
Discovery of microtubule stabilizers with novel scaffold structures based on virtual screening, biological evaluation, and molecular dynamics simulation.
Chem. Biol. Interact., 2022, 352, 109784.
DOI: 10.1016/j.cbi.2021.109784
Mallet, V., Ruano, L.C., Franel, A.M., Nilges, M., Druart, K., Bouvier, G. and Sperandio, O.
InDeep: 3D fully convolutional neural networks to assist in silico drug design on protein-protein interactions.
Bioinformatics, 2022, 38, 1261-1268.
DOI: 10.1093/bioinformatics/btab849
Wang, D.D., Chan, M.T. and Yan, H.
Structure-based protein-ligand interaction fingerprints for binding affinity prediction.
Comput. Struct. Biotechnol. J., 2021, 19, 6291-6300.
DOI: 10.1016/j.csbj.2021.11.018
Xiong, G.L., Shen, C., Yang, Z.Y., Jiang, D.J., Liu, S., Lu, A.P., Chen, X., Hou, T.J., Cao, D.S.
Featurization strategies for protein-ligand interactions and their applications in scoring function development.
WIREs Comput. Mol. Sci., 2022, 12.
DOI: 10.1002/wcms.1567
Qin, T., Zhu, Z.H., Wang, X.S., Xia, J. and Wu, S.
Computational representations of protein-ligand interfaces for structure-based virtual screening.
Expert. Opin. Drug. Discov., 2021, 16, 1175-1192.
DOI: 10.1080/17460441.2021.1929921
Shi, M.S., Zhao, M., Wang, L., Liu, KJ., Li, P.H., Liu, J., Cai, X.Y., Chen, L.J. and Xu, D.G.
Exploring the stability of inhibitor binding to SIK2 using molecular dynamics simulation and binding free energy calculation.
Phys. Chem. Chem. Phys., 2021, 23, 13216-13227.
DOI: 10.1039/d1cp00717c
Tu, G., Fu, T.T., Yang, F.Y., Yang, J.Y., Zhang, Z., Yao, X.J., Xue, W.W. and Zhu, F.
Understanding the Polypharmacological Profiles of Triple Reuptake Inhibitors by Molecular Simulation.
ACS Chem. Neurosci., 2021, 12, 2013-2026.
DOI: 10.1021/acschemneuro.1c00127
Kimber, T.B., Chen, Y.H. and Volkamer, A.
Deep Learning in Virtual Screening: Recent Applications and Developments.
Int. J. Mol. Sci., 2021, 22, 4435.
DOI: 10.3390/ijms22094435