@article{borrel_melander_fourches_2021, title={Cheminformatics Analysis of Fluoroquinolones and Their Inhibition Potency Against Four Pathogens}, volume={40}, ISSN={["1868-1751"]}, DOI={10.1002/minf.202000215}, abstractNote={Abstract}, number={5}, journal={MOLECULAR INFORMATICS}, author={Borrel, Alexandre and Melander, Christian and Fourches, Denis}, year={2021}, month={May} }
 @article{zin_borrel_fourches_2020, title={Benchmarking 2D/3D/MD-QSAR Models for Imatinib Derivatives: How Far Can We Predict?}, volume={60}, ISSN={["1549-960X"]}, DOI={10.1021/acs.jcim.0c00200}, abstractNote={Imatinib, a 2-phenylaminopyridine-based BCR-ABL tyrosine kinase inhibitor, is a highly effective drug for treating Chronic Myeloid Leukemia (CML). However, cases of drug resistance are constantly emerging due to various mutations in the ABL kinase domain; thus, it is crucial to identify novel bioactive analogues. Reliable QSAR models and molecular docking protocols have been shown to facilitate the discovery of new compounds from chemical libraries prior to experimental testing. However, as the vast majority of QSAR models strictly relies on 2D descriptors, the rise of 3D descriptors directly computed from molecular dynamics simulations offers new opportunities to potentially augment the reliability of QSAR models. Herein, we employed molecular docking and molecular dynamics on a large series of Imatinib derivatives and developed an ensemble of QSAR models relying on deep neural nets (DNN) and hybrid sets of 2D/3D/MD descriptors in order to predict the binding affinity and inhibition potencies of those compounds. Through rigorous validation tests, we showed that our DNN regression models achieved excellent external prediction performances for the pKi data set (n = 555, R2 ≥ 0.71. and MAE ≤ 0.85), and the pIC50 data set (n = 306, R2 ≥ 0.54. and MAE ≤ 0.71) with strict validation protocols based on external test sets and 10-fold native and nested cross validations. Interestingly, the best DNN and random forest models performed similarly across all descriptor sets. In fact, for this particular series of compounds, our external test results suggest that incorporating additional 3D protein-ligand binding site fingerprint, descriptors, or even MD time-series descriptors did not significantly improve the overall R2 but lowered the MAE of DNN QSAR models. Those augmented models could still help in identifying and understanding the key dynamic protein-ligand interactions to be optimized for further molecular design.}, number={7}, journal={JOURNAL OF CHEMICAL INFORMATION AND MODELING}, author={Zin, Phyo Phyo Kyaw and Borrel, Alexandre and Fourches, Denis}, year={2020}, month={Jul}, pages={3342–3360} }
 @article{borrel_kleinstreuer_fourches_2018, title={Exploring drug space with ChemMaps.com}, volume={34}, ISSN={["1460-2059"]}, DOI={10.1093/bioinformatics/bty412}, abstractNote={Abstract}, number={21}, journal={BIOINFORMATICS}, author={Borrel, Alexandre and Kleinstreuer, Nicole C. and Fourches, Denis}, year={2018}, month={Nov}, pages={3773–3775} }
 @article{borrel_fourches_2017, title={RealityConvert: a tool for preparing 3D models of biochemical structures for augmented and virtual reality}, volume={33}, ISSN={["1460-2059"]}, DOI={10.1093/bioinformatics/btx485}, abstractNote={Abstract}, number={23}, journal={BIOINFORMATICS}, author={Borrel, Alexandre and Fourches, Denis}, year={2017}, month={Dec}, pages={3816–3818} }
 @article{zhang_borrel_ghemtio_regad_gennas_camproux_yli-kauhaluoma_xhaard_2017, title={Structural Isosteres of Phosphate Groups in the Protein Data Bank}, volume={57}, ISSN={["1549-960X"]}, DOI={10.1021/acs.jcim.6b00519}, abstractNote={We developed a computational workflow to mine the Protein Data Bank for isosteric replacements that exist in different binding site environments but have not necessarily been identified and exploited in compound design. Taking phosphate groups as examples, the workflow was used to construct 157 data sets, each composed of a reference protein complexed with AMP, ADP, ATP, or pyrophosphate as well other ligands. Phosphate binding sites appear to have a high hydration content and large size, resulting in U-shaped bioactive conformations recurrently found across unrelated protein families. A total of 16 413 replacements were extracted, filtered for a significant structural overlap on phosphate groups, and sorted according to their SMILES codes. In addition to the classical isosteres of phosphate, such as carboxylate, sulfone, or sulfonamide, unexpected replacements that do not conserve charge or polarity, such as aryl, aliphatic, or positively charged groups, were found.}, number={3}, journal={JOURNAL OF CHEMICAL INFORMATION AND MODELING}, author={Zhang, Yuezhou and Borrel, Alexandre and Ghemtio, Leo and Regad, Leslie and Gennas, Gustav Boije and Camproux, Anne-Claude and Yli-Kauhaluoma, Jan and Xhaard, Henri}, year={2017}, month={Mar}, pages={499–516} }