Proteochemometrics – recent developments in bioactivity and selectivity modeling

DeepDTA: deep drug–target binding affinity prediction

Öztürk, H., Özgür, A., & Özkırımlı, E. (2018) · Bioinformatics · doi:10.1093/bioinformatics/bty593 · Cited by 1,728 · see citing works

Unified rational protein engineering with sequence-based deep representation learning

Alley, E. C., Khimulya, G., Biswas, S., AlQuraishi, M., & Church, G. M. (2019) · Nature Methods · doi:10.1038/s41592-019-0598-1 · Cited by 1,131 · see citing works

K DEEP : Protein–Ligand Absolute Binding Affinity Prediction via 3D-Convolutional Neural Networks

Jiménez-Luna, J., Škalič, M., Martínez-Rosell, G., & Fabritiis, G. D. (2018) · Journal of Chemical Information and Modeling · doi:10.1021/acs.jcim.7b00650 · Cited by 923 · see citing works

Mol2vec: Unsupervised Machine Learning Approach with Chemical Intuition

Jaeger-Honz, S., Fulle, S., & Turk, S. (2017) · Journal of Chemical Information and Modeling · doi:10.1021/acs.jcim.7b00616 · Cited by 759 · see citing works

Deep Learning Applications for Predicting Pharmacological Properties of Drugs and Drug Repurposing Using Transcriptomic Data

Aliper, A., Plis, S., Artemov, A., Ulloa, A., Mamoshina, P., & Zhavoronkov, A. (2016) · Molecular Pharmaceutics · doi:10.1021/acs.molpharmaceut.6b00248 · Cited by 610 · see citing works

Collaborative matrix factorization with multiple similarities for predicting drug-target interactions

Zheng, X., Ding, H., Mamitsuka, H., & Zhu, S. (2013) · ACM SIGKDD Conference on Knowledge Discovery and Data Mining (KDD) · doi:10.1145/2487575.2487670 · Cited by 335 · see citing works

Beyond the hype: deep neural networks outperform established methods using a ChEMBL bioactivity benchmark set

Lenselink, E. B., ten Dijke, N., Bongers, B., Papadatos, G., van Vlijmen, H. W. T., Kowalczyk, W., IJzerman, A. P., & van Westen, G. J. P. (2017) · Journal of Cheminformatics · doi:10.1186/s13321-017-0232-0 · Cited by 334 · see citing works

Learned protein embeddings for machine learning

Yang, K., Wu, Z., Bedbrook, C. N., & Arnold, F. H. (2018) · Bioinformatics · doi:10.1093/bioinformatics/bty178 · Cited by 316 · see citing works

DEEPScreen: high performance drug–target interaction prediction with convolutional neural networks using 2-D structural compound representations

Rifaioğlu, A. S., Nalbat, E., Atalay, V., Martin, M. J., Çetin-Atalay, R., & Doğan, T. (2020) · Chemical Science · doi:10.1039/c9sc03414e · Cited by 241 · see citing works

Repurposing High-Throughput Image Assays Enables Biological Activity Prediction for Drug Discovery

Simm, J., Klambauer, G., Arany, Á., Steijaert, M. N., Wegner, J. K., Gustin, E. M., Chupakhin, V., Chong, Y. T., Vialard, J. E., Buijnsters, P. J. J. A., et al. (2018) · Cell chemical biology · doi:10.1016/j.chembiol.2018.01.015 · Cited by 228 · see citing works

Proteochemometric modeling as a tool to design selective compounds and for extrapolating to novel targets

van Westen, G. J. P., Wegner, J. K., IJzerman, A. P., van Vlijmen, H. W. T., & Bender, A. (2011) · MedChemComm · doi:10.1039/c0md00165a · Cited by 176 · see citing works

Reliable estimation of prediction errors for QSAR models under model uncertainty using double cross-validation

Baumann, D., & Baumann, K. (2014) · Journal of Cheminformatics · doi:10.1186/s13321-014-0047-1 · Cited by 168 · see citing works

Development of proteo-chemometrics: a novel technology for the analysis of drug-receptor interactions

Lapinsh, M., Prusis, P., Gutcaits, A., Lundstedt, T., & Wikberg, J. E. S. (2001) · Biochimica et Biophysica Acta (BBA) - General Subjects · doi:10.1016/s0304-4165(00)00187-2 · Cited by 134 · see citing works

Benchmarking of protein descriptor sets in proteochemometric modeling (part 1): comparative study of 13 amino acid descriptor sets

van Westen, G. J. P., Swier, R. F., Wegner, J. K., IJzerman, A. P., van Vlijmen, H. W., & Bender, A. (2013) · Journal of Cheminformatics · doi:10.1186/1758-2946-5-41 · Cited by 124 · see citing works

Improved large-scale prediction of growth inhibition patterns using the NCI60 cancer cell line panel

Cortés‐Ciriano, I., van Westen, G. J. P., Bouvier, G., Nilges, M., Overington, J. P., Bender, A., & Malliavin, T. E. (2015) · Bioinformatics · doi:10.1093/bioinformatics/btv529 · Cited by 121 · see citing works

Prediction of the potency of mammalian cyclooxygenase inhibitors with ensemble proteochemometric modeling

Cortés‐Ciriano, I., Murrell, D. S., van Westen, G. J. P., Bender, A., & Malliavin, T. E. (2015) · Journal of Cheminformatics · doi:10.1186/s13321-014-0049-z · Cited by 118 · see citing works

Polypharmacology modelling using proteochemometrics (PCM): recent methodological developments, applications to target families, and future prospects

Cortés‐Ciriano, I., Ain, Q. U., Subramanian, V., Lenselink, E. B., Méndez‐Lucio, O., IJzerman, A. P., Wohlfahrt, G., Prūsis, P., Malliavin, T. E., van Westen, G. J. P., et al. (2014) · MedChemComm · doi:10.1039/c4md00216d · Cited by 115 · see citing works

Active Learning for Computational Chemogenomics

Reker, D., Schneider, P., Schneider, G., & Brown, J. B. (2017) · Future Medicinal Chemistry · doi:10.4155/fmc-2016-0197 · Cited by 101 · see citing works

Benchmarking of protein descriptor sets in proteochemometric modeling (part 2): modeling performance of 13 amino acid descriptor sets

van Westen, G. J. P., Swier, R. F., Cortés‐Ciriano, I., Wegner, J. K., Overington, J. P., IJzerman, A. P., van Vlijmen, H. W., & Bender, A. (2013) · Journal of Cheminformatics · doi:10.1186/1758-2946-5-42 · Cited by 92 · see citing works

Improved approach for proteochemometrics modeling: application to organic compound—amine G protein-coupled receptor interactions

Lapins, M., Prūsis, P., Uhlén, S., & Wikberg, J. E. S. (2005) · Bioinformatics · doi:10.1093/bioinformatics/bti703 · Cited by 81 · see citing works

Novel Consensus Architecture To Improve Performance of Large-Scale Multitask Deep Learning QSAR Models

Zakharov, A. V., Zhao, T., Nguyễn, Ð.-T., Peryea, T., Sheils, T. K., Yasgar, A., Huang, R., Southall, N. T., & Simeonov, A. M. (2019) · Journal of Chemical Information and Modeling · doi:10.1021/acs.jcim.9b00526 · Cited by 79 · see citing works

Kinome-wide interaction modelling using alignment-based and alignment-independent approaches for kinase description and linear and non-linear data analysis techniques

Lapins, M., & Wikberg, J. E. S. (2010) · BMC Bioinformatics · doi:10.1186/1471-2105-11-339 · Cited by 63 · see citing works

Pred-binding: large-scale protein–ligand binding affinity prediction

Shar, P. A., Tao, W., Gao, S., Huang, C., Li, B., Zhang, W., Shahen, M., Zheng, C., Bai, Y., & Wang, Y. (2016) · Journal of Enzyme Inhibition and Medicinal Chemistry · doi:10.3109/14756366.2016.1144594 · Cited by 60 · see citing works

Unprecedently Large-Scale Kinase Inhibitor Set Enabling the Accurate Prediction of Compound–Kinase Activities: A Way toward Selective Promiscuity by Design?

Christmann‐Franck, S., van Westen, G. J. P., Papadatos, G., Escudie, F. B., Roberts, A., Overington, J. P., & Domine, D. (2016) · Journal of Chemical Information and Modeling · doi:10.1021/acs.jcim.6b00122 · Cited by 55 · see citing works

Evaluation of deep and shallow learning methods in chemogenomics for the prediction of drugs specificity

Playe, B., & Stoven, V. (2020) · Journal of Cheminformatics · doi:10.1186/s13321-020-0413-0 · Cited by 50 · see citing works

An improved approach for predicting drug–target interaction: proteochemometrics to molecular docking

Shaikh, N., Sharma, M. C., & Garg, P. (2016) · Molecular BioSystems · doi:10.1039/c5mb00650c · Cited by 44 · see citing works

Merits of random forests emerge in evaluation of chemometric classifiers by external validation

Scott, I. M., Lin, W., Liakata, M., Wood, J. E., Vermeer, C. P., Allaway, D., Ward, J. L., Draper, J., Beale, M. H., Corol, D. I., et al. (2013) · Analytica Chimica Acta · doi:10.1016/j.aca.2013.09.027 · Cited by 41 · see citing works

Computational drug repositioning using collaborative filtering via multi-source fusion

Zhang, J., Li, C., Lin, Y., Shao, Y., & Li, S. (2017) · Expert Systems with Applications · doi:10.1016/j.eswa.2017.05.004 · Cited by 39 · see citing works

Proteochemometric modelling coupled to in silico target prediction: an integrated approach for the simultaneous prediction of polypharmacology and binding affinity/potency of small molecules

Paricharak, S., Cortés-Ciriano, I., IJzerman, A. P., Malliavin, T. E., & Bender, A. (2015) · Journal of Cheminformatics · doi:10.1186/s13321-015-0063-9 · Cited by 38 · see citing works

Kinome‐Wide Profiling Prediction of Small Molecules

Sorgenfrei, F. A., Fulle, S., & Merget, B. (2017) · ChemMedChem · doi:10.1002/cmdc.201700180 · Cited by 35 · see citing works

Evaluation of Cross-Validation Strategies in Sequence-Based Binding Prediction Using Deep Learning

Rio, A. L.-D., Nonell‐Canals, A., Vidal, D., & Perera-Lluna, A. (2019) · Journal of Chemical Information and Modeling · doi:10.1021/acs.jcim.8b00663 · Cited by 35 · see citing works

Multi-channel PINN: investigating scalable and transferable neural networks for drug discovery

Lee, M., Kim, H., Joe, H., & Kim, H. (2019) · Journal of Cheminformatics · doi:10.1186/s13321-019-0368-1 · Cited by 30 · see citing works

Prediction of PARP Inhibition with Proteochemometric Modelling and Conformal Prediction

Cortés‐Ciriano, I., Bender, A., & Malliavin, T. E. (2015) · Molecular Informatics · doi:10.1002/minf.201400165 · Cited by 29 · see citing works

Identification of novel small molecule inhibitors for solute carrier SGLT1 using proteochemometric modeling

Burggraaff, L., Oranje, P., Gouka, R., van der Pijl, P. C., Geldof, M., van Vlijmen, H. V., IJzerman, A., & van Westen, G. V. (2019) · Journal of Cheminformatics · doi:10.1186/s13321-019-0337-8 · Cited by 23 · see citing works

Study on human GPCR–inhibitor interactions by proteochemometric modeling

Gao, J. A., Huang, Q., Wu, D., Zhang, Q., Zhang, Y., Chen, T., Liu, Q., Zhu, R., Cao, Z., & He, Y. (2012) · Gene · doi:10.1016/j.gene.2012.11.061 · Cited by 22 · see citing works

Prospectively Validated Proteochemometric Models for the Prediction of Small-Molecule Binding to Bromodomain Proteins

Giblin, K. A., Hughes, S. J., Boyd, H., Hansson, P., & Bender, A. (2018) · Journal of Chemical Information and Modeling · doi:10.1021/acs.jcim.8b00400 · Cited by 21 · see citing works

Identification of Allosteric Modulators of Metabotropic Glutamate 7 Receptor Using Proteochemometric Modeling

Tresadern, G., Trabanco, A. A., Pérez‐Benito, L., Overington, J. P., van Vlijmen, H., & van Westen, G. J. P. (2017) · Journal of Chemical Information and Modeling · doi:10.1021/acs.jcim.7b00338 · Cited by 20 · see citing works

Target specific proteochemometric model development for BACE1 – protein flexibility and structural water are critical in virtual screening

Manoharan, P., Chennoju, K., & Ghoshal, N. (2015) · Molecular BioSystems · doi:10.1039/c5mb00088b · Cited by 19 · see citing works

Origin of aromatase inhibitory activity via proteochemometric modeling

Simeon, S., Spjuth, O., Lapins, M., Nabu, S., Anuwongcharoen, N., Prachayasittikul, V., Wikberg, J. E. S., & Nantasenamat, C. (2016) · PeerJ · doi:10.7717/peerj.1979 · Cited by 19 · see citing works

Quantitative Characterization of the Interaction Space of the Mammalian Carbonic Anhydrase Isoforms I, II , VII , IX , XII , and XIV and their Inhibitors, Using the Proteochemometric Approach

Rasti, B., Karimi‐Jafari, M. H., & Ghasemi, J. B. (2016) · Chemical Biology & Drug Design · doi:10.1111/cbdd.12759 · Cited by 18 · see citing works

Proteochemometrics-Based Prediction of Peptide Binding to HLA-DP Proteins

Yordanov, V., Dimitrov, I., & Doytchinova, I. A. (2017) · Journal of Chemical Information and Modeling · doi:10.1021/acs.jcim.7b00026 · Cited by 17 · see citing works

Applying Chemometrics Approaches to Model and Predict the Binding Affinities Between the Human Amphiphysin SH3 Domain and Its Peptide Ligands (Suplementary Material)

Liu, L., He, D., Shao-ming, Y., & Xu, Y. (2010) · Protein and Peptide Letters · doi:10.2174/092986610790226085 · Cited by 14 · see citing works

Proteochemometric modeling of the origin of thymidylate synthase inhibition

Rasti, B., & Shahangian, S. S. (2017) · Chemical Biology & Drug Design · doi:10.1111/cbdd.13163 · Cited by 11 · see citing works

3D proteochemometrics: using three-dimensional information of proteins and ligands to address aspects of the selectivity of serine proteases

Subramanian, V., Ain, Q. U., Henno, H., Pietilä, L., Fuchs, J. E., Prūsis, P., Bender, A., & Wohlfahrt, G. (2017) · MedChemComm · doi:10.1039/c6md00701e · Cited by 10 · see citing works

Exploring the origin of phosphodiesterase inhibition via proteochemometric modeling

Rasti, B., Schaduangrat, N., Shahangian, S. S., & Nantasenamat, C. (2017) · RSC Advances · doi:10.1039/c7ra02332d · Cited by 10 · see citing works

Structural insights into the origin of phosphoinositide 3-kinase inhibition

Hariri, S., Rasti, B., Mirpour, M., Vaghar-Lahijani, G., Attar, F., & Shiri, F. (2020) · Structural Chemistry · doi:10.1007/s11224-020-01510-2 · Cited by 10 · see citing works

Deep Protein-Ligand Binding Prediction Using Unsupervised Learned RepresentationsPreprint

Kim, P. T., Winter, R., & Clevert, D.-A. (2020) · ChemRxiv · doi:10.26434/chemrxiv.11523117.v1 · Cited by 9 · see citing works

Finding the molecular scaffold of nuclear receptor inhibitors through high-throughput screening based on proteochemometric modelling

Qiu, T., Wu, D., Qiu, J., & Cao, Z. (2018) · Journal of Cheminformatics · doi:10.1186/s13321-018-0275-x · Cited by 8 · see citing works

Probing the origin of dihydrofolate reductase inhibition via proteochemometric modeling

Hariri, S., Ghasemi, J. B., Shirini, F., & Rasti, B. (2018) · Journal of Chemometrics · doi:10.1002/cem.3090 · Cited by 7 · see citing works

A novel proteochemometrics model for predicting the inhibition of nine carbonic anhydrase isoforms based on supervised Laplacian score and k-nearest neighbour regression

Nazarshodeh, E., Sheikhpour, R., Gharaghani, S., & Sarram, M. A. (2018) · SAR and QSAR in environmental research · doi:10.1080/1062936x.2018.1447995 · Cited by 6 · see citing works

Peptide Binding Prediction to Five Most Frequent HLA‐DQ Proteins – a Proteochemometric Approach

Dimitrov, I., & Doytchinova, I. A. (2015) · Molecular Informatics · doi:10.1002/minf.201400150 · Cited by 5 · see citing works

Quantitative protein descriptors for secondary structure characterization and protein classification

Lindström, A., Pettersson, F. H., & Linusson, A. (2008) · Chemometrics and Intelligent Laboratory Systems · doi:10.1016/j.chemolab.2008.08.006 · Cited by 4 · see citing works

MGraphDTA: deep multiscale graph neural network for explainable drug–target binding affinity prediction

Yang, Z., Zhong, W., Zhao, L., & Chen, C. Y. (2022) · Chemical Science · doi:10.1039/d1sc05180f · Cited by 354 · see citing works

Connecting chemistry and biology through molecular descriptors

Fernández‐Torras, A., Comajuncosa-Creus, A., Duran‐Frigola, M., & Aloy, P. (2021) · Current Opinion in Chemical Biology · doi:10.1016/j.cbpa.2021.09.001 · Cited by 70 · see citing works

Sequence-based prediction of protein binding regions and drug–target interactions

Lee, I., & Nam, H. (2022) · Journal of Cheminformatics · doi:10.1186/s13321-022-00584-w · Cited by 67 · see citing works

AI for targeted polypharmacology: The next frontier in drug discovery

Cichońska, A., Ravikumar, B., & Rahman, R. (2024) · Current Opinion in Structural Biology · doi:10.1016/j.sbi.2023.102771 · Cited by 61 · see citing works

Chemical representation learning for toxicity prediction

Born, J., Markert, G., Janakarajan, N., Kimber, T. B., Volkamer, A., Martínez, M. R., & Manica, M. (2023) · Digital Discovery · doi:10.1039/d2dd00099g · Cited by 53 · see citing works

Advancing Targeted Protein Degradation via Multiomics Profiling and Artificial Intelligence

Duran-Frigola, M., Cigler, M., & Winter, G. E. (2023) · Journal of the American Chemical Society · doi:10.1021/jacs.2c11098 · Cited by 50 · see citing works

AI-Driven Polypharmacology in Small-Molecule Drug Discovery

Abdelsayed, M. (2025) · International Journal of Molecular Sciences · doi:10.3390/ijms26146996 · Cited by 49 · see citing works

Applications of Artificial Intelligence in Drug Design: Opportunities and Challenges

Thomas, M. C., Boardman, A., García-Ortegón, M., Yang, H., de Graaf, C., & Bender, A. (2021) · Methods in molecular biology · doi:10.1007/978-1-0716-1787-8_1 · Cited by 41 · see citing works

QSPRpred: a Flexible Open-Source Quantitative Structure-Property Relationship Modelling Tool

van den Maagdenberg, H. W., Šícho, M., Araripe, D. A., Luukkonen, S., Schoenmaker, L., Jespers, M., Béquignon, O. J. M., González, M. G., van den Broek, R. L., Bernatavicius, A., et al. (2024) · Journal of Cheminformatics · doi:10.1186/s13321-024-00908-y · Cited by 33 · see citing works

TAG-DTA: Binding-region-guided strategy to predict drug-target affinity using transformers

Monteiro, N. R. C., Oliveira, J. L., & Arrais, J. P. (2023) · Expert Systems with Applications · doi:10.1016/j.eswa.2023.122334 · Cited by 28 · see citing works

Comprehensive detection and characterization of human druggable pockets through binding site descriptors

(2024) · doi:10.1038/s41467-024-52146-3 · Cited by 32 · see citing works

DoubleSG-DTA: Deep Learning for Drug Discovery: Case Study on the Non-Small Cell Lung Cancer with EGFRT790M Mutation

Qian, Y., Ni, W., Xianyu, X., Tao, L., & Wang, Q. (2023) · Pharmaceutics · doi:10.3390/pharmaceutics15020675 · Cited by 25 · see citing works

Active Site Sequence Representations of Human Kinases Outperform Full Sequence Representations for Affinity Prediction and Inhibitor Generation: 3D Effects in a 1D Model

Born, J., Huynh, T., Stroobants, A., Cornell, W. D., & Manica, M. (2021) · Journal of Chemical Information and Modeling · doi:10.1021/acs.jcim.1c00889 · Cited by 23 · see citing works

An automatic generation of pre-processing strategy combined with machine learning multivariate analysis for NIR spectral data

Arianti, N. D., Saputra, E., & Sitorus, A. (2023) · Journal of Agriculture and Food Research · doi:10.1016/j.jafr.2023.100625 · Cited by 21 · see citing works

Toward Assay-Aware Bioactivity Model(er)s: Getting a Grip on Biological Context

Schoenmaker, L., Sastrokarijo, E. G., Heitman, L. H., Beltman, J. B., Jespers, W., & van Westen, G. J. P. (2025) · Journal of Chemical Information and Modeling · doi:10.1021/acs.jcim.5c00603 · Cited by 21 · see citing works

Advances in AI-based strategies and tools to facilitate natural product and drug development

Basnet, B. B., Zhou, Z., Wei, B., & Wang, H. (2025) · Critical Reviews in Biotechnology · doi:10.1080/07388551.2025.2478094 · Cited by 17 · see citing works

Polypharmacology prediction: the long road toward comprehensively anticipating small-molecule selectivity to de-risk drug discovery

Manen-Freixa, L., & Antolín, A. A. (2024) · Expert Opinion on Drug Discovery · doi:10.1080/17460441.2024.2376643 · Cited by 15 · see citing works

Balancing Data on Deep Learning-Based Proteochemometric Activity Classification

Rio, A. L.-D., Picart‐Armada, S., & Perera-Lluna, A. (2021) · Journal of Chemical Information and Modeling · doi:10.1021/acs.jcim.1c00086 · Cited by 14 · see citing works

Large-Scale Modeling of Sparse Protein Kinase Activity Data

Luukkonen, S., Meijer, E., Tricarico, G. A., Hofmans, J., Stouten, P. F. W., van Westen, G. J. P., & Lenselink, E. B. (2023) · Journal of Chemical Information and Modeling · doi:10.1021/acs.jcim.3c00132 · Cited by 14 · see citing works

AI's role in pharmaceuticals: Assisting drug design from protein interactions to drug development

Bechelli, S., & Delhommelle, J. (2023) · Artificial Intelligence Chemistry · doi:10.1016/j.aichem.2023.100038 · Cited by 14 · see citing works

A study of the interaction space of two lactate dehydrogenase isoforms (LDHA and LDHB) and some of their inhibitors using proteochemometrics modeling

Damavandi, S., Shiri, F., Emamjomeh, A., Pirhadi, S., & Beyzaei, H. (2023) · BMC Chemistry · doi:10.1186/s13065-023-00991-6 · Cited by 11 · see citing works

The method predicting interaction between protein targets and small-molecular ligands with the wide applicability domain

Karasev, D. А., Sobolev, B. N., Lagunin, A. A., Filimonov, D. A., & Poroikov, V. (2022) · Computational Biology and Chemistry · doi:10.1016/j.compbiolchem.2022.107674 · Cited by 10 · see citing works

BEAR: A Novel Virtual Screening Method Based on Large-Scale Bioactivity Data

Kwon, Y., Park, S., Lee, J., Kang, J., Lee, H. J., & Kim, W. (2023) · Journal of Chemical Information and Modeling · doi:10.1021/acs.jcim.2c01300 · Cited by 10 · see citing works

Combining Bayesian and Evidential Uncertainty Quantification for Improved Bioactivity Modeling

Khalil, B., Schweighofer, K., Dyubankova, N., van Westen, G. J. P., & van Vlijmen, H. (2025) · Journal of Chemical Information and Modeling · doi:10.1021/acs.jcim.5c01597 · Cited by 11 · see citing works

Machine learning proteochemometric models for Cereblon glue activity predictions

Prael, F. J., Cox, J. W., Sturm, N., Kutchukian, P. S., Forrester, W. C., Michaud, G. A., Blank, J., Shen, L., & Rodríguez-Pérez, R. (2024) · Artificial Intelligence in the Life Sciences · doi:10.1016/j.ailsci.2024.100100 · Cited by 9 · see citing works

Graph neural processes for molecules: an evaluation on docking scores and strategies to improve generalization

García-Ortegón, M., Seal, S., Rasmussen, C. E., Bender, A., & Bacallado, S. (2024) · Journal of Cheminformatics · doi:10.1186/s13321-024-00904-2 · Cited by 8 · see citing works

Generation of dual-target compounds using a transformer chemical language model

Srinivasan, S., & Bajorath, J. (2024) · Cell Reports Physical Science · doi:10.1016/j.xcrp.2024.102255 · Cited by 8 · see citing works

Advancements in Rational Multi‐Targeted Drug Discovery

Ravikumar, B., Cichońska, A., Sahni, N., Aittokallio, T. A., & Rahman, R. (2024) · Polypharmacology · doi:10.1002/9781394182862.ch9 · Cited by 8 · see citing works

Development of a proteochemometric-based support vector machine model for predicting bioactive molecules of tubulin receptors

Agyapong, O., Miller, W. A., Wilson, M. D., & Kwofie, S. K. (2021) · Molecular Diversity · doi:10.1007/s11030-021-10329-w · Cited by 7 · see citing works

3DDPDs: describing protein dynamics for proteochemometric bioactivity prediction. A case for (mutant) G protein-coupled receptors

González, M. G., van den Broek, R. L., Braun, T. G. M., Chatzopoulou, M., Jespers, W., IJzerman, A. P., Heitman, L. H., & van Westen, G. J. P. (2023) · Journal of Cheminformatics · doi:10.1186/s13321-023-00745-5 · Cited by 6 · see citing works

ProteoMutaMetrics: machine learning approaches for solute carrier family 6 mutation pathogenicity prediction

Huang, J., Osthushenrich, T., MacNamara, A., Mälarstig, A., Brocchetti, S., Bradberry, S. J., Scarabottolo, L., Ferrada, E., Sosnin, S., Digles, D., et al. (2024) · RSC Advances · doi:10.1039/d4ra00748d · Cited by 6 · see citing works

Integration of Machine Learning With PBPK and QSAR Modeling Approaches to Facilitate Drug Discovery and Development

Chen, X., & Lin, Z. (2026) · CPT Pharmacometrics & Systems Pharmacology · doi:10.1002/psp4.70228 · Cited by 5 · see citing works

A status report on human odorant receptors and their allocated agonists

Lalis, M., Hladiš, M., Khalil, S. A., Deroo, C., Marin, C., Bensafi, M., Baldovini, N., Briand, L. V., Fiorucci, S., & Topin, J. (2024) · Chemical Senses · doi:10.1093/chemse/bjae037 · Cited by 5 · see citing works

An industrial evaluation of proteochemometric modelling: Predicting drug-target affinities for kinases

Stroobants, A., Mervin, L., Engkvist, O., & Robb, G. R. (2023) · Artificial Intelligence in the Life Sciences · doi:10.1016/j.ailsci.2023.100079 · Cited by 4 · see citing works

Novel drug design and bioinformatics: an introduction

Khan, M. K. A., & Akhtar, S. (2021) · Physical Sciences Reviews · doi:10.1515/psr-2018-0158 · Cited by 3 · see citing works

Extracting prime protein targets as possible drug candidates: machine learning evaluation

Chattopadhyay, S., Phuong, N., Flower, D. R., & Chattopadhyay, A. K. (2023) · Medical & Biological Engineering & Computing · doi:10.1007/s11517-023-02893-0 · Cited by 3 · see citing works

Excuse me, there is a mutant in my bioactivity soup!A comprehensive analysis of the genetic variability landscape of bioactivity databases and its effect on activity modellingPreprint

González, M. G., Béquignon, O. J. M., Manners, E. J., Gaulton, A., Mutowo, P., Dawson, E., Zdrazil, B., Leach, A. R., IJzerman, A. P., Heitman, L. H., et al. (2024) · ChemRxiv · doi:10.26434/chemrxiv-2024-kxlgm · Cited by 3 · see citing works

Proteochemometric (PCM) Modelling: A Machine Learning Technique for Drug Designing

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Additional file 1 of Sequence-based prediction of protein binding regions and drug–target interactions

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Additional file 2 of 3DDPDs: describing protein dynamics for proteochemometric bioactivity prediction. A case for (mutant) G protein-coupled receptors

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Unbiased descriptor and parameter selection confirms the potential of proteochemometric modelling

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Déjà Vu - Réjà Vu : on knowledge-based approaches linking ligand and target information to bioactivity

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Proteochemometric (PCM) Modelling: A Machine Learning Technique for Drug Designing

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