Floriane Montanari
Floriane Montanari
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Learning continuous and data-driven molecular descriptors by translating equivalent chemical representations
R Winter, F Montanari, F Noé, DA Clevert
Chemical science 10 (6), 1692-1701, 2019
Prediction of drug–ABC-transporter interaction—Recent advances and future challenges
F Montanari, GF Ecker
Advanced drug delivery reviews 86, 17-26, 2015
Efficient multi-objective molecular optimization in a continuous latent space
R Winter, F Montanari, A Steffen, H Briem, F Noé, DA Clevert
Chemical science 10 (34), 8016-8024, 2019
Bayer’s in silico ADMET platform: a journey of machine learning over the past two decades
AH Göller, L Kuhnke, F Montanari, A Bonin, S Schneckener, A Ter Laak, ...
Drug Discovery Today 25 (9), 1702-1709, 2020
Modeling physico-chemical ADMET endpoints with multitask graph convolutional networks
F Montanari, L Kuhnke, A Ter Laak, DA Clevert
Molecules 25 (1), 44, 2019
Predicting drug-induced liver injury: The importance of data curation
E Kotsampasakou, F Montanari, GF Ecker
Toxicology 389, 139-145, 2017
Img2Mol–accurate SMILES recognition from molecular graphical depictions
DA Clevert, T Le, R Winter, F Montanari
Chemical science 12 (42), 14174-14181, 2021
Selectivity profiling of BCRP versus P-gp inhibition: from automated collection of polypharmacology data to multi-label learning
F Montanari, B Zdrazil, D Digles, GF Ecker
Journal of cheminformatics 8, 1-13, 2016
Improving molecular graph neural network explainability with orthonormalization and induced sparsity
R Henderson, DA Clevert, F Montanari
International Conference on Machine Learning, 4203-4213, 2021
Flagging drugs that inhibit the bile salt export pump
F Montanari, M Pinto, N Khunweeraphong, K Wlcek, MI Sohail, T Noeske, ...
Molecular Pharmaceutics 13 (1), 163-171, 2016
Virtual screening of DrugBank reveals two drugs as new BCRP inhibitors
F Montanari, A Cseke, K Wlcek, GF Ecker
SLAS DISCOVERY: Advancing Life Sciences R&D 22 (1), 86-93, 2017
BCRP inhibition: from data collection to ligand‐based modeling
F Montanari, GF Ecker
Molecular Informatics 33 (5), 322-331, 2014
ChemInformatics Model Explorer (CIME): exploratory analysis of chemical model explanations
C Humer, H Heberle, F Montanari, T Wolf, F Huber, R Henderson, ...
Journal of Cheminformatics 14 (1), 21, 2022
Vienna LiverTox workspace—a set of machine learning models for prediction of interactions profiles of small molecules with transporters relevant for regulatory agencies
F Montanari, B Knasmüller, S Kohlbacher, C Hillisch, C Baierová, ...
Frontiers in Chemistry 7, 899, 2020
Integrative modeling strategies for predicting drug toxicities at the eTOX project
F Sanz, P Carrió, O López, L Capoferri, DP Kooi, NPE Vermeulen, ...
Molecular Informatics 34 (6‐7), 477-484, 2015
Exploiting open data: a new era in pharmacoinformatics
D Goldmann, F Montanari, L Richter, B Zdrazil, GF Ecker
Future medicinal chemistry 6 (5), 503-514, 2014
The ABC of phytohormone translocation
E Hellsberg, F Montanari, GF Ecker
Planta medica 81 (06), 474-487, 2015
Subtle structural differences trigger inhibitory activity of propafenone analogues at the two polyspecific ABC transporters: P‐glycoprotein (P‐gp) and breast cancer resistance …
T Schwarz, F Montanari, A Cseke, K Wlcek, L Visvader, S Palme, P Chiba, ...
ChemMedChem 11 (12), 1380-1394, 2016
A Fast and Interpretable Deep Learning Approach for Accurate Electrostatics-Driven pKa Predictions in Proteins
PBPS Reis, M Bertolini, F Montanari, W Rocchia, M Machuqueiro, ...
Journal of chemical theory and computation 18 (8), 5068-5078, 2022
Differences in the number of intrinsically disordered regions between yeast duplicated proteins, and their relationship with functional divergence
F Montanari, DC Shields, N Khaldi
PloS one 6 (9), e24989, 2011
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