Publications
91 results found
Loecher A, Bruyns-Haylett M, Ballester PJ, et al., 2023, A machine learning approach to predict cellular uptake of pBAE polyplexes., Biomater Sci, Vol: 11, Pages: 5797-5808
The delivery of genetic material (DNA and RNA) to cells can cure a wide range of diseases but is limited by the delivery efficiency of the carrier system. Poly β-amino esters (pBAEs) are promising polymer-based vectors that form polyplexes with negatively charged oligonucleotides, enabling cell membrane uptake and gene delivery. pBAE backbone polymer chemistry, as well as terminal oligopeptide modifications, define cellular uptake and transfection efficiency in a given cell line, along with nanoparticle size and polydispersity. Moreover, uptake and transfection efficiency of a given polyplex formulation also vary from cell type to cell type. Therefore, finding the optimal formulation leading to high uptake in a new cell line is dictated by trial and error, and requires time and resources. Machine learning (ML) is an ideal in silico screening tool to learn the non-linearities of complex data sets, like the one presented herein, with the aim of predicting cellular internalisation of pBAE polyplexes. A library of pBAE nanoparticles was fabricated and the uptake studied in 4 different cell lines, on which various ML models were successfully trained. The best performing models were found to be gradient-boosted trees and neural networks. The gradient-boosted trees model was then analysed using SHapley Additive exPlanations, to interpret the model and gain an understanding into the important features and their impact on the predicted outcome.
Tran-Nguyen V-K, Ballester P, 2023, Beware of simple methods for structure-based virtual screening: the critical importance of broader comparisons, Journal of Chemical Information and Modeling, Vol: 63, Pages: 1401-1405, ISSN: 1549-9596
We discuss how data unbiasing and simple methods such as protein-ligand Interaction FingerPrint (IFP) can overestimate virtual screening performance. We also show that IFP is strongly outperformed by target-specific machine-learning scoring functions, which were not considered in a recent report concluding that simple methods were better than machine-learning scoring functions at virtual screening.
Ogunleye AZ, Piyawajanusorn C, Goncalves A, et al., 2022, Interpretable Machine Learning Models to Predict the Resistance of Breast Cancer Patients to Doxorubicin from Their microRNA Profiles, ADVANCED SCIENCE, Vol: 9
Ballester PJ, Stevens R, Haibe-Kains B, et al., 2022, Artificial intelligence for drug response prediction in disease models, BRIEFINGS IN BIOINFORMATICS, Vol: 23, ISSN: 1467-5463
Hernández-Hernández S, Vishwakarma S, Ballester PJ, 2022, Conformal prediction of small-molecule drug resistance in cancer cell lines, Pages: 92-108
Drug design is a critical step in the drug discovery process, where promising drug molecules are engineered to be later evaluated preclinically and perhaps clinically. Phenotypic drug design has again gained traction. Cancer cell lines, a frequently adopted in vitro model for phenotype drug design, can be used to evaluate the drug resistance level (lack of inhibitory activity, for example) of a large number of molecules, and discard those that are the least likely to become drug candidates. By reusing these datasets, supervised learning models have been built to predict drug resistance on cancer cell lines. Usually, these methods have assigned reliability to the whole model rather than reliability to individual predictions (molecules). In problems such as drug design, accurately achieving the latter would revolutionize decision making. Conformal prediction is a model-agnostic method to assign reliability to each model prediction. In this study, we investigated the impact of conformal prediction on the prediction of inhibitory activity of molecules on a given cancer cell line. This analysis was carried out in each of the 60 cell lines from the NCI-60 panel to understand the variability of the results across cancer types. We also discussed the implications of predicting the molecules considered most potent. In addition, we investigated how the further subdivision of the training set to build conformal prediction models may affect the results obtained. Overall, we observed that those molecules deemed most reliable by conformal prediction are substantially better predicted than those that are not. This suggest that such computational tools are promising to guide phenotypic drug design.
Tran-Nguyen V-K, Simeon S, Junaid M, et al., 2022, Structure-based virtual screening for PDL1 dimerizers: Evaluating generic scoring functions, Current Research in Structural Biology, Vol: 4, Pages: 206-210
Ghislat G, Rahman T, Ballester PJ, 2021, Recent progress on the prospective application of machine learning to structure-based virtual screening, Current Opinion in Chemical Biology, Vol: 65, Pages: 28-34
Simeon S, Ghislat G, Ballester P, 2021, Characterizing the Relationship Between the Chemical Structures of Drugs and their Activities on Primary Cultures of Pediatric Solid Tumors, Current Medicinal Chemistry, Vol: 28, Pages: 7830-7839
Frasser CF, Benito CD, Skibinsky-Gitlin ES, et al., 2021, Using Stochastic Computing for Virtual Screening Acceleration, Electronics, Vol: 10, Pages: 2981-2981
Nguyen LC, Naulaerts S, Bruna A, et al., 2021, Predicting Cancer Drug Response In Vivo by Learning an Optimal Feature Selection of Tumour Molecular Profiles, Biomedicines, Vol: 9, Pages: 1319-1319
Piyawajanusorn C, Nguyen LC, Ghislat G, et al., 2021, A gentle introduction to understanding preclinical data for cancer pharmaco-omic modeling, Briefings in Bioinformatics
Ballester PJ, Carmona J, 2021, Artificial intelligence for the next generation of precision oncology, npj Precision Oncology, Vol: 5
Ghislat G, Cheema AS, Baudoin E, et al., 2021, NF-$\upkappa$B–dependent IRF1 activation programs cDC1 dendritic cells to drive antitumor immunity, Science Immunology, Vol: 6, Pages: eabg3570-eabg3570
Ghislat G, Cheema AS, Baudoin E, et al., 2020, An NF-$\upkappa$B/IRF1 axis programs cDC1s to drive anti-tumor immunity
Ghislat G, Rahman T, Ballester PJ, 2020, Identification and Validation of Carbonic Anhydrase II as the First Target of the Anti-Inflammatory Drug Actarit, Biomolecules, Vol: 10, Pages: 1570-1570
Ariey-Bonnet J, Carrasco K, Grand ML, et al., 2020, In silico molecular target prediction unveils mebendazole as a potent MAPK14 inhibitor, Molecular Oncology
Ballester PJ, 2020, Selecting machine-learning scoring functions for structure-based virtual screening, Drug Discovery Today: Technologies
Ahmad S, Ballester PJ, Fernandez M, 2020, Editorial: Intelligent Systems for Genome Functional Annotations, Frontiers in Genetics, Vol: 11
Patil S, Hofer J, Ballester PJ, et al., 2020, Drug Repurposing for Covid-19: Discovery of Potential Small-Molecule Inhibitors of Spike Protein-ACE2 Receptor Interaction Through Virtual Screening and Consensus Scoring
Naulaerts S, Menden MP, Ballester PJ, 2020, Concise Polygenic Models for Cancer-Specific Identification of Drug-Sensitive Tumors from Their Multi-Omics Profiles, Biomolecules, Vol: 10, Pages: 963-963
Fresnais L, Ballester PJ, 2020, The impact of compound library size on the performance of scoring functions for structure-based virtual screening, Briefings in Bioinformatics, Vol: 22
Li H, Sze K-H, Lu G, et al., 2020, Machine-learning scoring functions for structure-based virtual screening, WIREs Computational Molecular Science, Vol: 11
Fresnais L, Ballester PJ, 2020, The impact of compound library size on the performance of scoring functions for structure-based virtual screening
Li H, Sze K-H, Lu G, et al., 2020, Machine-learning scoring functions for structure-based drug lead optimization, WIREs Computational Molecular Science, Vol: 10
Ballester P, 2020, Stochastic-based Neural Network hardware acceleration for an efficient ligand-based virtual screening
Bomane A, Gonçalves A, Ballester PJ, 2019, Paclitaxel Response Can Be Predicted With Interpretable Multi-Variate Classifiers Exploiting DNA-Methylation and miRNA Data, Frontiers in Genetics, Vol: 10
Sidorov P, Naulaerts S, Ariey-Bonnet J, et al., 2019, Predicting Synergism of Cancer Drug Combinations Using NCI-ALMANAC Data, Frontiers in Chemistry, Vol: 7
Menden MP, Wang D, Mason MJ, et al., 2019, Community assessment to advance computational prediction of cancer drug combinations in a pharmacogenomic screen, NATURE COMMUNICATIONS, Vol: 10, ISSN: 2041-1723
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Ballester PJ, 2019, Machine Learning for Molecular Modelling in Drug Design, Biomolecules, Vol: 9, Pages: 216-216
Peón A, Li H, Ghislat G, et al., 2019, MolTarPred: A web tool for comprehensive target prediction with reliability estimation, Chemical Biology &$\mathsemicolon$ Drug Design, Vol: 94, Pages: 1390-1401
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