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 | 425 | 2019 |
Prediction of drug–ABC-transporter interaction—Recent advances and future challenges F Montanari, GF Ecker Advanced drug delivery reviews 86, 17-26, 2015 | 255 | 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 | 246 | 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 | 134 | 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 | 95 | 2019 |
Predicting drug-induced liver injury: The importance of data curation E Kotsampasakou, F Montanari, GF Ecker Toxicology 389, 139-145, 2017 | 68 | 2017 |
Img2Mol–accurate SMILES recognition from molecular graphical depictions DA Clevert, T Le, R Winter, F Montanari Chemical science 12 (42), 14174-14181, 2021 | 56 | 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 | 36 | 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 | 33 | 2021 |
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 | 29 | 2017 |
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 | 29 | 2016 |
BCRP inhibition: from data collection to ligand‐based modeling F Montanari, GF Ecker Molecular Informatics 33 (5), 322-331, 2014 | 28 | 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 | 23 | 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 | 23 | 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 | 23 | 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 | 23 | 2014 |
The ABC of phytohormone translocation E Hellsberg, F Montanari, GF Ecker Planta medica 81 (06), 474-487, 2015 | 22 | 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 | 20 | 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 | 17* | 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 | 16 | 2011 |